Free to use IOC feed for various tools/malware. It started out for just C2 tools but has morphed into tracking infostealers and botnets as well. It uses shodan.io/">Shodan searches to collect the IPs. The most recent collection is always stored in data; the IPs are broken down by tool and there is an all.txt.
The feed should update daily. Actively working on making the backend more reliable
Honorable Mentions
Many of the Shodan queries have been sourced from other CTI researchers:
I encourage opening an issue/PR if you know of any additional Shodan searches for identifying adversary infrastructure. I will not set any hard guidelines around what can be submitted, just know, fidelity is paramount (high true/false positive ratio is the focus).
Specially crafted Javascript inside an HTML page can trigger a type confusion
bug in Microsoft Edge that allows accessing a C++ object as if it was a BSTR
string. This can result in information disclosure, such as allowing an attacker
to determine the value of pointers to other objects and/or functions. This
information can be used to bypass ASLR mitigations. It may also be possible to
modify arbitrary memory and achieve remote code execution, but this was not
investigated.
A specially crafted style sheet inside an HTML page can trigger a NULL pointer
dereference in Microsoft Internet Explorer 10 and 11. The pointer in question
is assumed to point to a function, and the code attempts to use it to execute
this function, which normally leads to an access violation when attempting to
execute unmapped memory at address 0. In some cases, Control Flow Guard
(CFG) will attempt to check if the address is a valid indirect call target.
Because of the way CFG is implemented, this can lead to a read access
violation in unmapped memory at a seemingly arbitrary address.
A specially crafted Javascript inside an HTML page can trigger a use-after-free
bug in Tree::ANode::IsInTree or a breakpoint in
Abandonment::InduceAbandonment in Microsoft Edge. The use-after-free bug is
mitigated by MemGC: if MemGC is enabled (which it is by default) the memory
is never freed. This effectively prevents exploitation of the issue. The
Abandonment appears to be triggered by a stack exhaustion bug; the Javascript
creates a loop where an event handler triggers a new event, which in turn
triggers the event handler, etc.. This consumes a stack space until there is no
more stack available. Edge does appear to be able to handle such a situation
gracefully under certain conditions, but not all. It is easy to avoid those
conditions to force triggering the Abandonment.
The interesting thing is that this indicates that the assumption that "hitting
Abandonment means a bug is not a security issue" may not be correct in all
cases.
A specially crafted Javascript inside an HTML page can trigger a use-after-free
bug in the CTreePosGap::PartitionPointers function of edgehtml.dll in
Microsoft Edge. This use-after-free bug is mitigated by MemGC by default:
with MemGC enabled the memory is never actually freed. This mitigation is
considered sufficient to make this a non-security issue as explained by
Microsoft SWIAT in their blog post Triaging the exploitability of IE/Edge
crashes.
Since this is not considered a security issue, I have the opportunity to share
details about the issue with you before the issue has been fixed. And since
Microsoft are unlikely to provide a fix for this issue on short notice, you
should be able to reproduce this issue for some time after publication of this
post. I will try to explain how I analyzed this issue using BugId and
EdgeDbg, so that you can reproduce what I did and see for yourself.
While working independently has many advantages, it does have one major
drawback: no one to bounce ideas off or help you solve problems. So, in order to
address this, I am now looking for opportunities to work closer with other
researchers again.
With MS16-063 Microsoft has patched CVE-2016-0199: a memory corruption bug
in the garbage collector of the JavaScript engine used in Internet Explorer 11.
By exploiting this vulnerability, a website can causes this garbage collector
to handle some data in memory as if it was an object, when in fact it contains
data for another type of value, such as a string or number. The garbage
collector code will use this data as a virtual function table (vftable) in order
to make a virtual function call. An attacker has enough control over this data
to allow execution of arbitrary code.
Software components such as memory managers often use magic values to mark
memory as having a certain state. These magic values have often (but not always)
been chosen to coincide with addresses that fall outside of the user-land
address space on 32-bit versions of the Operating System. This ensures that if
a vulnerability in the software allows an attacker to get the code to use such
a value as a pointer, this results in an access violation. However, on 64-bit
architectures the entire 32-bit address space can be used for user-land
allocations, allowing an attacker to allocate memory at all the addresses
commonly used as magic values and exploit such a vulnerability.
In my previous blog post I wrote about "magic values" that were originally
chosen to help mitigate exploitation of memory corruption flaws and how this
mitigation could potentially be bypassed on 64-bit Operating Systems,
specifically Windows. In this blog post, I will explain how to create a heap
spray (of sorts) that can be used to allocate memory in the relevant address
space range and fill it with arbitrary data for use in exploiting such a
vulnerability.
A specially crafted web-page can cause Microsoft Internet Explorer 9 to
reallocate a memory buffer in order to grow it in size. The original buffer
will be copied to newly allocated memory and then freed. The code continues to
use the freed copy of the buffer.
(The fix and CVE number for this bug are not known)
A specially crafted web-page can cause Microsoft Internet Explorer 11 to free
a memory block that contains information about an image. The code continues
to use the data in freed memory block immediately after freeing it. It does not
appear that there is enough time between the free and reuse to exploit this
issue.
(The fix and CVE number for this bug are not known)
A specially crafted web-page can cause Microsoft Internet Explorer 10 to read
data out-of-bounds. This issue was fixed before I was able to analyze it in
detail, hence I did not determine exactly what the root cause was.
(The fix and CVE number for this bug are not known)
A specially crafted web-page can cause Microsoft Internet Explorer 9 to access
data before the start of a memory block. An attack that is able to control
what is stored before this memory block may be able to disclose information
from memory or execute arbitrary code.
A specially crafted script can cause the VBScript engine to access data before
initializing it. An attacker that is able to run such a script in any
application that embeds the VBScript engine may be able to control execution
flow and execute arbitrary code. This includes all versions of Microsoft
Internet Explorer.
(The fix and CVE number for this bug are not known)
A specially crafted script can cause the VBScript engine to read data beyond a
memory block for use as a regular expression. An attacker that is able to run
such a script in any application that embeds the VBScript engine may be able to
disclose information stored after this memory block. This includes all versions
of Microsoft Internet Explorer.
A specially crafted web-page can cause Microsoft Internet Explorer 9-11 to
assume a CSS value stored as a string can only be "true" or "false". To
determine which of these two values it is, the code checks if the fifth
character is an 'e' or a '\0'. An attacker that is able to set it to a
smaller string can cause the code to read data out-of-bounds and is able to
determine if a WCHAR value stored behind that string is '\0' or not.
Adam Rawnsley has spent the past decade reporting in-depth on Iran’s UAV industry and paying particular attention to the IRGC drone company Mado and its CEO Yousef Aboutalebi. One day in 2021, a self-professed “hacktivist” popped into Adam’s direct messages, told him his “group” had noticed Adam had done the most work on Mado, and dumped videos and documents allegedly hacked from the company’s network and CEO.
The material—painstakingly verified with the help of colleagues—fleshes out a portrait of the company Adam had been sketching out for years. Thanks to the additional sourcing and some help from colleagues at the Middlebury Institute of International Studies (MIIS) and work by others, we can now confirm that Mado engines are powering the Iranian drones raining down on Ukraine and are likely used in some of the cruise missiles Iran and its proxies have launched against Saudi Arabia and the United Arab Emirates.
Using the hacked documents and videos along with court records, web registration information, business records, and other open sources, Adam traces the rise of a key Iranian drone company from late 2000s aviation forum posts to contracts with some of the highest ranking generals in the Islamic Revolutionary Guard Corps. Mado’s trail starts in Iran but moves through China, Germany, Saudi Arabia, an Iranian motorcycle company, and finally Russia and Ukraine.
About the Presenter
Adam Rawnsley is a reporter at Rolling Stone. He spent his career in journalism covering national and cybersecurity, primarily through the lens of open source reporting. He has written for Bellingcat, Foreign Policy, Wired, and The Daily Beast and guest lectured on open source and security issues at CyberWarCon (2022), John Hopkins University, Georgetown University, and Middlebury College.
About LABScon 2023
This presentation was featured live at LABScon 2023, an immersive 3-day conference bringing together the world’s top cybersecurity minds, hosted by SentinelOne’s research arm, SentinelLabs.
By Joey Chen, Chetan Raghuprasad and Alex Karkins.
Cisco Talos discovered a new ongoing campaign since at least February 2024, operated by a threat actor distributing three famous infostealer malware, including Cryptbot, LummaC2 and Rhadamanthys.
Talos also discovered a new PowerShell command-line argument embedded in the LNK file to bypass anti-virus products and download the final payload into the victims’ host.
This campaign uses the Content Delivery Network (CDN) cache domain as a download server, hosting the malicious HTA file and payload.
Talos assesses with moderate confidence that the threat actor CoralRaider operates the campaign. We observed several overlaps in tactics, techniques, and procedures (TTPs) of CoralRaider’s Rotbot campaign, including the initial attack vector of the Windows Shortcut file, intermediate PowerShell decryptor and payload download scripts, the FoDHelper technique used to bypass User Access Controls (UAC) of the victim machine.
Victimology and actor infrastructure
The campaign affects victims across multiple countries, including the U.S., Nigeria, Pakistan, Ecuador, Germany, Egypt, the U.K., Poland, the Philippines, Norway, Japan, Syria and Turkey, based on our telemetry data and OSINT information. Our telemetry also disclosed that some affected users were from Japan’s computer service call center organizations and civil defense service organizations in Syria. The affected users were downloading files masquerading as movie files through the browser, indicating the possibility of a widespread attack on users across various business verticals and geographies.
We observe that this threat actor is using a Content Delivery Network (CDN) cache to store the malicious files on their network edge host in this campaign, avoiding request delay. The actor is using the CDN cache as a download server to deceive network defenders.
CDN edge URLs
Information Stealer
hxxps[://]techsheck[.]b-cdn[.]net/Zen90
Cryptbot
hxxps[://]zexodown-2[.]b-cdn[.]net/Peta12
Cryptbot
hxxps[://]denv-2[.]b-cdn[.]net/FebL5
Cryptbot, Rhadamanthys
hxxps[://]download-main5[.]b-cdn[.]net/BSR_v7IDcc
Rhadamanthys
hxxps[://]dashdisk-2[.]b-cdn[.]net/XFeb18
Cryptbot
hxxps[://]metrodown-3[.]b-cdn[.]net/MebL1
Cryptbot
hxxps[://]metrodown-2[.]b-cdn[.]net/MebL1
Cryptbot, LummaC2
hxxps[://]metrodown-2[.]b-cdn[.]net/SAq2
LummaC2
Talos discovered that the actor is using multiple C2 domains in the campaign. The DNS requests for the domains during our analysis period are shown in the graph, indicating the campaign is ongoing.
Tactics, techniques and procedures overlap with other campaigns
Talos assesses with moderate confidence that threat actor CoralRaider is likely operating this campaign based on several overlaps in the TTPs used and the targeted victims’ geography of this campaign with that of the CoralRaider’s Rotbot campaign. We spotted that the PowerShell scripts used in the attack chain of this campaign to decrypt the PowerShell scripts of further stages and the downloader PowerShell script are similar to those employed in the Rotbot’s campaign.
PowerShell decryptor script of Rotbot campaign (left) and new unknown campaign (right).
String decrypt and download routine of Rotbot campaign (Left) and new unknown campaign (right).
The Powershell script did not appear in any public repository or article, indicating the threat actor likely developed these PowerShell scripts. Pivoting on the PowerShell argument embedded in the LNK file showed us that such arguments are not popular and likely specific to the actor and the campaign.
Multi-stage infection chain to deliver the payload
The infection chain starts when a victim opens the malicious shortcut file from a ZIP file downloaded using the drive-by download technique, according to our telemetry. The threat actor is likely delivering malicious links to victims through phishing emails.
The Windows shortcut file has an embedded PowerShell command running a malicious HTA file on attacker-controlled CDN domains. HTA file executes an embedded Javascript, which decodes and runs a PowerShell decrypter script. PowerShell decrypter script decrypts the embedded PowerShell Loader script and runs it in the victim’s memory. The PowerShell Loader executes multiple functions to evade the detections and bypass UAC, and finally, it downloads and runs one of the payloads, Cryptbot, LummaC2 or Rhadamanthys information stealer.
Windows Shortcut file to execute the malicious HTA file
Windows shortcut file runs a PowerShell command to download and run an HTML application file on the victim’s machine. The threat actor has used “gp,” a PowerShell command alias for Get-ItemProperty, to read the registry contents of the application classes registry key and gets the executable name “mshta.exe.” Using mshta.exe, the PowerShell instance executes the remotely hosted malicious HTA file on the victim’s machine.
Obfuscated HTA runs embedded PowerShell decrypter
The malicious HTML application file is heavily obfuscated and has a Javascript that decodes and executes a function using the String fromCharCode method. The decoded function then executes an embedded PowerShell decryptor script.
The decryptor PowerShell script has a block of AES-encrypted string. Using the AES decryptor function, it generates an AES key of 256 bytes from a base64 encoded string “RVRVd2h4RUJHUWNiTEZpbkN5SXhzUWRHeFN4V053THQ=” and the IV “AAAAAAAAAAAAAAAA.” With the key and IV, it decrypts and executes the next stage of the PowerShell Loader script.
PowerShell loader downloads and runs the payload
The PowerShell loader script is modular and has multiple functions to perform a sequence of activities on the victim’s machine. Initially, it executes a function that drops a batch script in the victim machine’s temporary folder and writes its contents, which includes the PowerShell command to add the folder “ProgramData” of the victim machine to the Windows Defender exclusion list.
The dropped bath script is executed through a living-off-the-land binary (LoLBin) “FoDHelper.exe” and a Programmatic Identifiers (ProgIDs) registry key to bypass the User Access Controls (UAC) in the victim’s machine. Fodhelper is a Windows feature, an on-demand helper binary that runs by default with high integrity. Usually, when the FodHelper is run, it checks for the presence of the registry keys listed below. If the registry keys have commands assigned, the FodHelper will execute them in an elevated context without prompting the user.
Windows Defender, by default, detects if there are attempts to write to the registry keysHKCU:\Software\Classes\ms-settings\shell\open\command and to evade this detection, the threat actor uses the programmatic identifier (ProgID). In Windows machines, a programmatic identifier (ProgID ) is a registry entry that can be associated with a Class ID (CLSID ), which is a globally unique serial number that identifies a COM (Component Object Model) class object. The Windows Shell uses a default ProgID registry key called CurVer, which is used to set the default version of a COM application.
In this campaign, the threat actor abuses the “CurVer” registry key feature by creating a custom ProgID “ServiceHostXGRT” registry key in the software classes registry and assigns the Windows shell to execute a command to run the batch script.
The script configures the ProgID ServiceHostXGRT in the CurVer registry subkey of HKCU\Software\Classes\ms-settings\CurVer, which will get translated to HKCU:\Software\Classes\ms-settings\shell\open\command. After modifying the registry settings, the PowerShell script runs FoDHelper.exe, executing the command assigned to the registry key HKCU:\Software\Classes\ms-settings\shell\open\command and executing the dropped batch script. Finally, it deletes the configured registry keys to evade detection.
The batch script adds the folder “C:\ProgramData” to the Windows Defender exclusion list. The PowerShell loader script downloads the payload and saves it in the “C:\ProgramData” folder as “X1xDd.exe.”
After downloading the payload to the victim’s machine, the PowerShell loader executes another function that overwrites the previously dropped batch file with the new instructions to run the downloaded payload information stealer through the Windows start command. It again uses the same FoDHelper technique to run the batch script’s second version, which we explained earlier in this section.
Actor’s choice of three payloads in the same campaign
Talos discovered that the threat actor delivered three famous information stealer malware as payloads in this campaign, including CryptBot, LummaC2 and Rhadamanthys. These information stealers target victims’ information, such as system and browser data, credentials, cryptocurrency wallets and financial information.
CryptBot
CryptBot is a typical infostealer targeting Windows systems discovered in the wild in 2019 by GDATA. It is designed to steal sensitive information from infected computers, such as credentials from browsers, cryptocurrency wallets, browser cookies and credit cards, and creates screenshots of the infected system.
Talos has discovered a new CryptBot variant distributed in the wild since January 2024. The goal of the new CryptBot is the same, with some new innovative functionalities. The new CryptBot is packed with different techniques to obstruct malware analysis. A few new CryptBot variants are packed with VMProtect V2.0.3-2.13; others also have VMProtect, but with unknown versions. The new CryptBot attempts to steal sensitive information from infected machines and modifies the configuration changes of the stolen applications. The list of targeted browsers, applications and cryptocurrency wallets by the new variant of CryptBot is shown below.
We observed the new CryptBot variant also includes password manager application databases and authenticator application information in its stealing list to steal the cryptocurrency wallets that have two-factor authentication enabled.
CryptBot is aware that the target applications in the victim’s environment will have different versions, and their database files will have different file extensions. It scans the victim’s machine for database files’ extensions of the targeted applications for harvesting credentials.
LummaC2
Talos discovered that the actor is delivering a new variant of LummaC2 malware as an alternative payload in this campaign. LummaC2 is a notorious information stealer that attempts to harvest information from victims’ machines. Based on the report posted by outpost24 and other external security reports, LummaC2 has already been confirmed to be sold on the underground market for years.
The threat actor has modified LummaC2’s information stealer capability and obfuscated the malware with a custom algorithm. The obfuscation algorithm is saved in another section inside the malware shown below.
The new version of LummaC2 also presents the same signature of the alert message displayed to the user during its execution.
The C2 domains are encrypted with a symmetric algorithm, and we found that the actor has nine C2 servers that the malware will attempt to connect to one by one. Analyzing various samples of the new LummaC2 variant, we spotted that each will use a different key to encrypt the C2.
Talos has compiled the list of nine C2 domains the new LummaC2 variant attempts to connect in this campaign.
LummaC2’s first step in its exfiltration phase is its connection to the C2 server. The malware will exit the process if it does not receive the “OK” message as a response from any of the nine C2 servers. The second step will be exfiltrating information from infected machines. The basic stealing functionality is the same as the previous version, with the addition of victims’ discord credentials to exfiltrate.
Rhadamanthys
The last payload we found in this campaign is Rhadamanthys malware, a famous infostealer appearing in the underground forum advertisement in September 2022. The Rhadamanthys malware has been evolving till now, and its authors have released a new version, V0.6.0, on Feb. 15, 2024. However, the Rhadamanthys variant we found in this campaign is still v0.5.0.
The threat actor uses a Python executable file as a loader to execute the Rhadamanthys malware into memory. After decompiling the Python executable file, Python scripts load the Rhadamanthys malware in two stages. The first stage is a simple Python script that replaces the binary code from 0 to 9 and decodes the second stage.
In the second stage, the Python script uses the Windows API to allocate a memory block and inject Rhadamanthys malware into the process. We spotted that the threat actor is developing the Python script with the intention of including the functionality of executing a shellcode.
Analyzing the final executable file showed us that the malware unpacks the loader module with the custom format having the magic header “XS” and performs the process injection. The custom loader module in XS format is similar to that of a Rhadamanthys sample analyzed by the researcher at Check Point. The malware selects one of the listed processes as the target process for process injection from a hardcoded list in the binary:
"%Systemroot%\\system32\\dialer.exe"
"%Systemroot%\\system32\\openwith.exe"
Coverage
Cisco Secure Endpoint (formerly AMP for Endpoints) is ideally suited to prevent the execution of the malware detailed in this post. Try Secure Endpoint for free here.
Cisco Secure Web Appliance web scanning prevents access to malicious websites and detects malware used in these attacks.
Cisco Secure Email (formerly Cisco Email Security) can block malicious emails sent by threat actors as part of their campaign. You can try Secure Email for free here.
Cisco Secure Malware Analytics (Threat Grid) identifies malicious binaries and builds protection into all Cisco Secure products.
Umbrella, Cisco's secure internet gateway (SIG), blocks users from connecting to malicious domains, IPs and URLs, whether users are on or off the corporate network. Sign up for a free trial of Umbrella here.
Cisco Secure Web Appliance (formerly Web Security Appliance) automatically blocks potentially dangerous sites and tests suspicious sites before users access them.
Additional protections with context to your specific environment and threat data are available from the Firewall Management Center.
Cisco Duo provides multi-factor authentication for users to ensure only those authorized are accessing your network.
Open-source Snort Subscriber Rule Set customers can stay up to date by downloading the latest rule pack available for purchase on Snort.org. Snort SID for this threat is 63218 - 63225 and 300867 - 300870.
ClamAV detections are also available for this threat:
Lnk.Downloader.CoralRaider-10027128-0
Txt.Tool.CoralRaider-10027140-0
Html.Downloader.CoralRaider-10027220-0
Win.Infostealer.Lumma-10027222-0
Win.Infostealer.Rhadamanthys-10027293-0
Win.Infostealer.Rhadamanthys-10027294-0
Win.Infostealer.Cryptbot-10027295-0
Win.Infostealer.Cryptbot-10027296-0
Win.Infostealer.Cryptbot-10027297-0
Win.Infostealer.Cryptbot-10027298-0
Win.Infostealer.Cryptbot-10027299-0
Win.Infostealer.Cryptbot-10027300-0
Win.Infostealer.Cryptbot-10027301-0
Win.Infostealer.Cryptbot-10027302-0
Win.Infostealer.Cryptbot-10027303-0
Win.Infostealer.Cryptbot-10027305-0
Indicators of Compromise
Indicators of Compromise associated with this threat can be found here.
Avast discovered and analyzed a malware campaign hijacking an eScan antivirus update mechanism to distribute backdoors and coinminers
Avast disclosed the vulnerability to both eScan antivirus and India CERT. On 2023-07-31, eScan confirmed that the issue was fixed and successfully resolved
The campaign was orchestrated by a threat actor with possible ties to Kimsuky
Two different types of backdoors have been discovered, targeting large corporate networks
The final payload distributed by GuptiMiner was also XMRig
Introduction
We’ve been tracking a curious one here. Firstly, GuptiMiner is a highly sophisticated threat that uses an interesting infection chain along with a couple of techniques that include performing DNS requests to the attacker’s DNS servers, performing sideloading, extracting payloads from innocent-looking images, signing its payloads with a custom trusted root anchor certification authority, among others.
The main objective of GuptiMiner is to distribute backdoors within big corporate networks. We’ve encountered two different variants of these backdoors: The first is an enhanced build of PuTTY Link, providing SMB scanning of the local network and enabling lateral movement over the network to potentially vulnerable Windows 7 and Windows Server 2008 systems on the network. The second backdoor is multi-modular, accepting commands from the attacker to install more modules as well as focusing on scanning for stored private keys and cryptowallets on the local system.
Interestingly, GuptiMiner also distributes XMRig on the infected devices, which is a bit unexpected for such a thought-through operation.
The actors behind GuptiMiner have been capitalizing on an insecurity within an update mechanism of Indian antivirus vendor eScan to distribute the malware by performing a man-in-the-middle attack. We disclosed this security vulnerability to both eScan and the India CERT and received confirmation on 2023-07-31 from eScan that the issue was fixed and successfully resolved.
GuptiMiner is a long-standing malware, with traces of it dating back to 2018 though it is likely that it is even older. We have also found that GuptiMiner has possible ties to Kimsuky, a notorious North Korean APT group, by observing similarities between Kimsuky keylogger and parts of the GuptiMiner operation. In this analysis, we will cover the GuptiMiner’s features and its evolution over time. We will also denote in which samples the particular features are contained or introduced to support the overall comprehension in the vast range of IoCs.
It is also important to note that since the users rarely install more than one AV on their machine, we may have limited visibility into GuptiMiner’s activity and its overall scope. Because of this, we might be looking only at the tip of the iceberg and the true scope of the entire operation may still be subject to discovery.
Infection Chain
To illustrate the complexity of the whole infection, we’ve provided a flow chart containing all parts of the chain. Note that some of the used filenames and/or workflows can slightly vary depending on the specific version of GuptiMiner, but the flowchart below illustrates the overall process.
The whole process starts with eScan requesting an update from the update server where an unknown MitM intercepts the download and swaps the update package with a malicious one. Then, eScan unpacks and loads the package and a DLL is sideloaded by eScan clean binaries. This DLL enables the rest of the chain, following with multiple shellcodes and intermediary PE loaders.
Resulted GuptiMiner consists of using XMRig on the infected machine as well as introducing backdoors which are activated when deployed in large corporate networks.
GuptiMiner’s infection chain
Evolution and Timelines
GuptiMiner has been active since at least 2018. Over the years, the developers behind it have improved the malware significantly, bringing new features to the table. We will describe the specific features in detail in respective subsections.
With that said, we also wanted to illustrate the significant IoCs in a timeline representation, how they changed over time – focusing on mutexes, PDBs, and used domains. These timelines were created based on scanning for the IoCs over a large sample dataset, taking the first and last compilation timestamps of the samples, then forming the intervals. Note that the scanned dataset is larger than listed IoCs in the IoC section. For more detailed list of IoCs, please visit our GitHub.
Domains in Time
In general, GuptiMiner uses the following types of domains during its operations:
Malicious DNS – GuptiMiner hosts their own DNS servers for serving true destination domain addresses of C&C servers via DNS TXT responses
Requested domains – Domains for which the malware queries the DNS servers for
PNG download – Servers for downloading payloads in the form of PNG files. These PNG files are valid images (a logo of T-Mobile) that contain appended shellcodes at their end
Config mining pool – GuptiMiner contains two different configurations of mining pools. One is hardcoded directly in the XMRig config which is denoted in this group
Modified mining pool – GuptiMiner has the ability to modify the pre-defined mining pools which is denoted in this group
Final C&C – Domains that are used in the last backdoor stage of GuptiMiner, providing additional malware capabilities in the backdoored systems
Other – Domains serving different purposes, e.g., used in scripts
Note that as the malware connects to the malicious DNS servers directly, the DNS protocol is completely separated from the DNS network. Thus, no legitimate DNS server will ever see the traffic from this malware. The DNS protocol is used here as a functional equivalent of telnet. Because of this, this technique is not a DNS spoofing since spoofing traditionally happens on the DNS network.
Furthermore, the fact that the servers for which GuptiMiner asks for in the Requested domain category actually exist is purely a coincidence, or rather a network obfuscation to confuse network monitoring tools and analysts.
Timeline illustrating GuptiMiner’s usage of domains in time
From this timeline, it is apparent that authors behind GuptiMiner realize the correct setup of their DNS servers is crucial for the whole chain to work properly. Because of this, we can observe the biggest rotation and shorter timeframes are present in the Malicious DNS group.
Furthermore, since domains in the Requested domain group are irrelevant (at least from the technical viewpoint), we can notice that the authors are reusing the same domain names for longer periods of time.
Mutexes in Time
Mutexes help ensure correct execution flow of a software and malware authors often use these named objects for the same purpose. Since 2018, GuptiMiner has changed its mutexes multiple times. Most significantly, we can notice a change since 2021 where the authors changed the mutexes to reflect the compilation/distribution dates of their new versions.
Timeline illustrating GuptiMiner’s usage of mutexes in time
An attentive reader can likely observe two takeaways: The first is the apparent outliers in usage of MIVOD_6, SLDV15, SLDV13, and Global\Wed Jun 2 09:43:03 2021. According to our data, these mutexes were truly reused multiple times in different builds, creating larger timeframes than expected.
Another point is the re-introduction of PROCESS_ mutex near the end of last year. At this time, the authors reintroduced the mutex with the string in UTF-16 encoding, which we noted separately.
PDBs in Time
With regard to debugging symbols, the authors of GuptiMiner left multiple PDB paths in their binaries. Most of the time, they contain strings like MainWork, Projects, etc.
Timeline illustrating PDBs contained in GuptiMiner in time
Stage 0 – Installation Process
Intercepting the Updates
Everyone should update their software, right? Usually, the individual either downloads the new version manually from the official vendor’s site, or – preferably – the software itself performs the update automatically without much thought or action from the user. But what happens when someone is able to hijack this automatic process?
Our investigation started as we began to observe some of our users were receiving unusual responses from otherwise legitimate requests, for example on:
http://update3[.]mwti[.]net/pub/update/updll3.dlz
This is truly a legitimate URL to download the updll3.dlz file which is, under normal circumstances, a legitimate archive containing the update of the eScan antivirus. However, we started seeing suspicious behavior on some of our clients, originating exactly from URLs like this.
What we uncovered was that the actors behind GuptiMiner were performing man-in-the-middle (MitM) to download an infected installer on the victim’s PC, instead of the update. Unfortunately, we currently don’t have information on how the MitM was performed. We assume that some kind of pre-infection had to be present on the victim’s device or their network, causing the MitM.
Throughout the analysis, we will try to describe not just the flow of the infection chain, malware techniques, and functionalities of the stages, but we will also focus on different versions, describing how the malware authors developed and changed GuptiMiner over time.
The first GuptiMiner sample that we were able to find was compiled on Tuesday, 2018-04-19 09:47:41 and it was uploaded to VirusTotal the day after from India, followed by an upload from Germany: c3122448ae3b21ac2431d8fd523451ff25de7f6e399ff013d6fa6953a7998fa3
This file was named C:\Program Files\eScan\VERSION.DLL which points out the target audience is truly eScan users and it comes from an update package downloaded by the AV.
Even though this version lacked several features present in the newer samples, the installation process is still the same, as follows:
The eScan updater triggers the update
The downloaded package file is replaced with a malicious one on the wire because of a missing HTTPS encryption (MitM is performed)
A malicious package updll62.dlz is downloaded and unpacked by eScan updater
The contents of the package contain a malicious DLL (usually called version.dll) that is sideloaded by eScan. Because of the sideloading, the DLL runs with the same privileges as the source process – eScan – and it is loaded next time eScan runs, usually after a system restart
If a mutex is not present in the system (depends on the version, e.g. Mutex_ONLY_ME_V1), the malware searches for services.exe process and injects its next stage into the first one it can find
Cleanup is performed, removing the update package
The malicious DLL contains additional functions which are not present in the clean one. Thankfully the names are very verbose, so no analysis was required for most of them. The list of the functions can be seen below.
Additional exported functions
Some functions, however, are unique. For example, the function X64Call provides Heaven’s gate, i.e., it is a helper function for running x64 code inside a 32-bit process on a 64-bit system. The malware needs this to be able to run the injected shellcode depending on the OS version and thus the bitness of the services.exe process.
Heaven’s gate to run the shellcode in x64 environment when required
To keep the original eScan functionality intact, the malicious version.dll also needs to handle the original legacy version.dll functionality. This is done by forwarding all the exported functions from the original DLL. When a call of the legacy DLL function is identified, GuptiMiner resolves the original function and calls it afterwards.
Resolving function that ensures all the original version.dll exports are available
Injected Shellcode in services.exe
After the shellcode is injected into services.exe, it serves as a loader of the next stage. This is done by reading an embedded PE file in a plaintext form.
Embedded PE file loaded by the shellcode
This PE file is loaded by standard means, but additionally, the shellcode also destroys the PE’s DOS header and runs it by calling its entry point, as well as it removes the embedded PE from the original location memory altogether.
Command Line Manipulation
Across the entire GuptiMiner infection chain, every shellcode which is loading and injecting PE files also manipulates the command line of the current process. This is done by manipulating the result of GetCommandLineA/W which changes the resulted command line displayed for example in Task Manager.
Command line manipulation function
After inspecting this functionality, we believe it either doesn’t work as the authors intended or we don’t understand its usage. Long story short, the command line is changed in such a way that everything before the first --parameter is skipped, and this parameter is then appended to the process name.
To illustrate this, we could take a command: notepad.exe param1 --XX param2 which will be transformed into: notepad.exeXX param2
However, we have not seen a usage like power --shell.exe param1 param2 that would result into: powershell.exe param1 param2 nor have we seen any concealment of parameters (like usernames and passwords for XMRig), a type of behavior we would anticipate when encountering something like this. In either case, this functionality is obfuscating the command line appearance, which is worth mentioning. An interested reader can play around with the functionality at the awesome godbolt.org here.
Another version with a mutex ONLY_ME_V3 introduced a code virtualization. This can be observed by an additional section in the PE file called .v_lizer. This section was also renamed a few times in later builds.
A new section with the virtualized code is called .v_lizer
Thankfully the obfuscation is rather weak, provided the shellcode as well as the embedded PE file are still in the plaintext form.
Furthermore, the authors started to distinguish between the version.dll stage and the PE file loaded by the shellcode by additional mutex. Previously, both stages used the shared mutex ONLY_ME_Vx, now the sideloading uses MTX_V101 as a mutex.
The installation process has undergone multiple improvements over time, and, since it is rather different compared to older variants, we decided to describe it separately as an intermediary Stage 0.9. With these improvements, the authors introduced a usage of scheduled tasks, WMI events, two differently loaded next stages (Stage 1 – PNG loader), turning off Windows Defender, and installing crafted certificates to Windows.
There are also multiple files dropped at this stage, enabling further sideloading by the malware. These files are clean and serve exclusively for sideloading purposes. The malicious DLLs that are being sideloaded, are two PNG loaders (Stage 1):
At this stage, WMI events are used for loading the first of the PNG loaders. This loader is extracted to a path: C:\PROGRAMDATA\AMD\CNext\atiadlxx.dll
Along with it, additional clean files are dropped, and they are used for sideloading, in either of these locations (can be both): C:\ProgramData\AMD\CNext\slsnotif.exe C:\ProgramData\AMD\CNext\msvcr120.dll or C:\Program Files (x86)\AMD\CNext\CCCSlim\slsnotify.exe C:\Program Files (x86)\AMD\CNext\CCCSlim\msvcr120.dll
The clean file slsnotify.exe is then registered via WMI event in such a way that it is executed when these conditions are met:
WMI conditions to trigger sideloading
In other words, the sideloading is performed on a workday in either January, July, or November. The numbers represented by %d are randomly selected values. The two possibilities for the hour are exactly two hours apart and fall within the range of 11–16 or 13–18 (inclusive). This conditioning further underlines the longevity of GuptiMiner operations.
Similarly to the WMI events, GuptiMiner also drops a clean binary for sideloading at this location: C:\ProgramData\Brother\Brmfl14c\BrRemPnP.exe
The malicious PNG loader is then placed in one (or both) of these locations: C:\Program Files (x86)\Brother\Brmfl14c\BrLogAPI.dll C:\Program Files\Brother\Brmfl14c\BrLogAPI.dll
The scheduled task is created by invoking a Task Scheduler. The scheduled task has these characteristics:
It is created and named as C:\Windows\System32\Tasks\Microsoft\Windows\Brother\Brmfl14c
Let’s now look at how all these files, clean and malicious, are being deployed. One of GuptiMiner’s tricks is that it drops the final payload, containing PNG loader stage, only during the system shutdown process. Thus, this happens at the time other applications are shutting down and potentially not protecting the user anymore.
The main flow of the Stage 0.9 variant – drops final payload during system shutdown
From the code above, we can observe that only when the SM_SHUTTINGDOWN metric is non-zero, meaning the current session is shutting down, as well as all the supporting clean files were dropped successfully, the final payload DLL is dropped as well.
An engaged reader could also notice in the code above that the first function that is being called disables Windows Defender. This is done by standard means of modifying registry keys. Only if the Defender is disabled can the malware proceed with the malicious actions.
Adding Certificates to Windows
Most of the time, GuptiMiner uses self-signed binaries for their malicious activities. However, this time around, the attackers went a step further. In this case, both of the dropped PNG loader DLLs are signed with a custom trusted root anchor certification authority. This means that the signature is inherently untrusted since the attackers’ certification authority cannot be trusted by common verification processes in Windows.
However, during the malware installation, GuptiMiner also adds a root certificate to Windows’ certificate store making this certification authority trusted. Thus, when such a signed file is executed, it is understood as correctly signed. This is done by using CertCreateCertificateContext, CertOpenStore, and CertAddCertificateContextToStore API functions.
Function which adds GuptiMiner’s root certificate to Windows
The certificate is present in a plaintext form directly in the GuptiMiner binary file.
A certificate in the plaintext form which is added as root to Windows by the malware
During our research, we found three different certificate issuers used during the GuptiMiner operations:
GTE Class 3 Certificate Authority
VeriSign Class 3 Code Signing 2010
DigiCert Assured ID Code Signing CA
Note that these names are artificial and any resemblance to legitimate certification authorities shall be considered coincidental.
At later development stages, authors behind GuptiMiner started to integrate even better persistence of their payloads by storing the payloads in registry keys. Furthermore, the payloads were also encrypted by XOR using a fixed key. This ensures that the payloads look meaningless to the naked eye.
We’ve discovered these registry key locations to be utilized for storing the payloads so far:
When the entry point of the PE file is executed by the shellcode from Stage 0, the malware first creates a scheduled task to attempt to perform cleanup of the initial infection by removing updll62.dlz archive and version.dll library from the system.
Furthermore, the PE serves as a dropper for additional stages by contacting an attacker’s malicious DNS server. This is done by sending a DNS request to the attacker’s DNS server, obtaining the TXT record with the response. The TXT response holds an encrypted URL domain of a real C&C server that should be requested for an additional payload. This payload is a valid PNG image file (a T-Mobile logo) which also holds a shellcode appended to its end. The shellcode is afterwards executed by the malware in a separate thread, providing further malware functionality as a next stage.
Note that since the DNS server itself is malicious, the requested domain name doesn’t really matter – or, in a more abstract way of thinking about this functionality, it can be rather viewed as a “password” which is passed to the server, deciding whether the DNS server should or shouldn’t provide the desired TXT answer carrying the instructions.
As we already mentioned in the Domains timeline section, there are multiple of such “Requested domains” used. In the version referenced here, we can see these two being used:
ext.peepzo[.]com
crl.peepzo[.]com
and the malicious DNS server address is in this case:
ns1.peepzo[.]com
Here we can see a captured DNS TXT response using Wireshark. Note that Transaction ID = 0x034b was left unchanged during all the years of GuptiMiner operations. We find this interesting because we would expect this could get easily flagged by firewalls or EDRs in the affected network.
DNS TXT response captured by Wireshark
The requests when the malware is performing the queries is done in random intervals. The initial request for the DNS TXT record is performed in the first 20 minutes after the PNG loader is executed. The consecutive requests, which are done for the malware’s update routine, wait up to 69 hours between attempts.
This update mechanism is reflected by creating separate mutexes with the shellcode version number which is denoted by the first two bytes of the decrypted DNS TXT response (see below for the decryption process). This ensures that no shellcode with the same version is run twice on the system.
Mutex is numbered by the shellcode’s version information
DNS TXT Record Decryption
After the DNS TXT record is received, GuptiMiner decodes the content using base64 and decrypts it with a combination of MD5 used as a key derivation function and the RC2 cipher for the decryption. Note that in the later versions of this malware, the authors improved the decryption process by also using checksums and additional decryption keys.
For the key derivation function and the decryption process, the authors decided to use standard Windows CryptoAPI functions.
Typical use of standard Windows CryptoAPI functions
Interestingly, a keen eye can observe an oversight in this initialization process shown above, particularly in the CryptHashData function. The prototype of the CryptHashData API function is:
The second argument of this function is a pointer to an array of bytes of a length of dwDataLen. However, this malware provides the string L"POVO@1" in a Unicode (UTF-16) format, represented by the array of bytes *pbData.
Thus, the first six bytes from this array are only db 'P', 0, 'O', 0, 'V', 0 which effectively cuts the key in half and padding it with zeroes. Even though the malware authors changed the decryption key throughout the years, they never fixed this oversight, and it is still present in the latest version of GuptiMiner.
DNS TXT Record Parsing
At this point, we would like to demonstrate the decrypted TXT record and how to parse it. In this example, while accessing the attacker’s malicious DNS server ns.srnmicro[.]net and the requested domain spf.microsoft[.]com, the server returned this DNS TXT response:
After fully decoding and decrypting this string, we get:
This result contains multiple fields and can be interpreted as:
Name
Value
Version 1
1
Version 2
5
Key size
\r (= 0xD)
Key
Microsoft.com
C&C URL
http://www.deanmiller[.]net/m/
Checksum
\xde
The first two bytes, Version 1 and Version 2, form the PNG shellcode version. It is not clear why there are two such versions since Version 2 is actually never used in the program. Only Version 1 is considered whether to perform the update – i.e., whether to download and load the PNG shellcode or not. In either case, we could look at these numbers as a major version and a minor version, and only the major releases serve as a trigger for the update process.
The third byte is a key size that denotes how many bytes should be read afterwards, forming the key. Furthermore, no additional delimiter is needed between the key and the URL since the key size is known and the URL follows. Finally, the two-byte checksum can be verified by calculating a sum of all the bytes (modulo 0xFF).
After the DNS TXT record is decoded and decrypted, the malware downloads the next stage, from the provided URL, in the form of a PNG file. This is done by using standard WinINet Windows API, where the User-Agent is set to contain the bitness of the currently running process.
The malware communicates the bitness of the running process to the C&C
The C&C server uses the User-Agent information for two things:
Provides the next stage (a shellcode) in the correct bitness
Filters any HTTP request that doesn’t contain this information as a protection mechanism
Parsing the PNG File
After the downloaded file is a valid PNG file which also contains a shellcode appended at the end. The image is a T-Mobile logo and has exactly 805 bytes. These bytes are skipped by the malware and the rest of the file, starting at an offset 0x325, is decrypted by RC2 using the key provided in the TXT response (derived using MD5). The reason of using an image as this “prefix” is to further obfuscate the network communication where the payload looks like a legitimate image, likely overlooking the appended malware code.
PNG file containing the shellcode starting at 0x325
After the shellcode is loaded from the position 0x325, it proceeds with loading additional PE loader from memory to unpack next stages using Gzip.
In late 2023, the authors decided to ditch the years-long approach of using DNS TXT records for distributing payloads and they switched to IP address masking instead.
This new approach consists of a few steps:
Obtain an IP address of a hardcoded server name registered to the attacker by standard means of using gethostbyname API function
For that server, two IP addresses are returned – the first is an IP address which is a masked address, and the second one denotes an available payload version and starts with 23.195. as the first two octets
If the version is newer than the current one, the masked IP address is de-masked and results in a real C&C IP address
The real C&C IP address is used along with a hardcoded constant string (used in a URL path) to download the PNG file containing the shellcode
The de-masking process is done by XORing each octet of the IP address by 0xA, 0xB, 0xC, 0xD, respectively. The result is then taken, and a hardcoded constant string is added to the URL path.
As an example, one such server we observed was www.elimpacific[.]net. It was, at the time, returning:
The address 23.195.101[.]1 denotes a version and if it is greater than the current version, it performs the update by downloading the PNG file with the shellcode. This update is downloaded by requesting a PNG file from the real C&C server whose address is calculated by de-masking the 179.38.204[.]38 address:
The request is then made, along with the calculated IP address 185.45.192[.]43 and a hardcoded constant elimp. Using a constant like this serves as an additional password, in a sense: 185.45.192[.]43/elimp/
GuptiMiner is requesting the payload from a real IP address
When the PNG file is downloaded, the rest of the process is the same as usual.
We’ve discovered two servers for this functionality so far:
Along with other updates described above, we also observed an evolution in using anti-VM and anti-debugging tricks. These are done by checking well known disk drivers, registry keys, and running processes.
GuptiMiner checks for these disk drivers by enumerating HKEY_LOCAL_MACHINE\SYSTEM\ControlSet001\services\Disk\Enum:
vmware
qemu
vbox
virtualhd
Specifically, the malware also checks the registry key HKEY_LOCAL_MACHINE\SOFTWARE\Cylance for the presence of Cylance AV.
As other anti-VM measures, the malware also checks whether the system has more than 4GB available RAM and at least 4 CPU cores.
Last but not least, the malware also checks the presence of these processes by their prefixes:
Similarly to Storing Payloads in Registry, in later stages of GuptiMiner, the authors also started to save the downloaded PNG images (containing the shellcodes) into registry as well. Contrary to storing the payloads, the images are not additionally XORed since the shellcodes in them are already encrypted using RC2 (see DNS TXT Record Decryption section for details).
We’ve discovered these registry key locations to be utilized for storing the encrypted images containing the shellcodes so far:
This stage is the shortest, the Gzip loader, which is extracted and executed by the shellcode from the PNG file, is a simple PE that decompresses another shellcode using Gzip and executes it in a separate thread.
This thread additionally loads Stage 3, which we call Puppeteer, that orchestrates the core functionality of the malware – the cryptocurrency mining as well as, when applicable, deploying backdoors on the infected systems.
Throughout the GuptiMiner operations, Gzip loader has not been changed with later versions.
Let’s now look at the biggest Stage 3, the Puppeteer. It pulls its strings everywhere across the infected system, manipulating the GuptiMiner components to do its bidding, hence the name we’ve chosen. It orchestrates further actions and deploys two core components of the malware – an XMRig coinminer and two types of backdoors that target devices present in large corporate networks. Of course, Puppeteer also introduces additional tricks to the arsenal of the whole GuptiMiner operation.
This stage also uses one of the many Global\SLDV mutexes which we described in the Mutex timeline. For example, this particular sample uses SLDV01 as its mutex.
Puppeteer Setup
Puppeteer performs several steps for a proper setup. Firstly, it adds a new power scheme in Windows so the PC does not go to sleep. If the CPU has only one core (anti-VM) or the mutex already exists, the malware ceases to function by going to infinite sleep.
In the next phase, the malware kills all the processes with a name msiexec.exe, cmstp.exe, or credwiz.exe. After that, it creates a separate thread that injects XMRig into a credwiz.exe process freshly created by the malware. The malware also disables Windows Defender by setting its service start status to disabled.
For the persistence, Puppeteer chose an interesting approach. Firstly, it creates a scheduled task with the following configuration:
A legitimate rundll32.exe file is copied and renamed into C:\ProgramData\Microsoft\Crypto\Escan\dss.exe and this file is executed from the scheduled task
The malicious DLL is placed to C:\ProgramData\Microsoft\Crypto\Escan\updll3.dll3 and this file is loaded by dss.exe (exported function ValidateFile)
The task is executed with every boot (TASK_TRIGGER_BOOT) and TASK_RUNLEVEL_HIGHEST priority
The task is named and located at C:\Windows\system32\tasks\Microsoft\windows\autochk\ESUpgrade
With that, the malware copies the content of updll3.dll3 into memory and deletes the original file from disk. Puppeteer then waits for a system shutdown (similarly to Stage 0.9) by waiting for SM_SHUTTINGDOWN metric to be set to non-zero value, indicating the shutdown. This is checked every 100 milliseconds. Only when the shutdown of the system is initiated, the malware reintroduces the updll3.dll3 file back onto disk.
Putting the malicious DLL back just before the system restart is really sneaky but also has potentially negative consequences. If the victim’s device encounters a crash, power outage, or any other kind of unexpected shutdown, the file won’t be restored from memory and Puppeteer will stop working from this point. Perhaps this is the reason why authors actually removed this trick in later versions, trading the sophistication for malware’s stability.
A code ensuring the correct after-reboot execution
The repetitive loading of updll3.dll3, as seen in the code above, is in fact Puppeteer’s update process. The DLL will ultimately perform steps of requesting a new PNG shellcode from the C&C servers and if it is a new version, the chain will be updated.
XMRig Deployment
During the setup, Puppeteer created a separate thread for injecting an XMRig coinminer into credwiz.exe process. Before the injection takes place, however, a few preparation steps are performed.
The XMRig configuration is present directly in the XMRig binary (standard JSON config) stored in the Puppeteer binary. This configuration can be, however, modified to different values on the fly. In the example below, we can see a dynamic allocation of mining threads depending on the robustness of the infected system’s hardware.
Patching the XMRig configuration on the fly, dynamically assigning mining threads
The injection is standard: the malware creates a new suspended process of credwiz.exe and, if successful, the coinmining is injected and executed by WriteProcessMemory and CreateRemoteThread combo.
Puppeteer continuously monitors the system for running process, by default every 5 seconds. If it encounters any of the monitoring tools below, the malware kills any existing mining by taking down the whole credwiz.exe process as well as it applies a progressive sleep, postponing another re-injection attempt by additional 5 hours.
taskmgr.exe
autoruns.exe
wireshark.exe
wireshark-gtk.exe
tcpview.exe
Furthermore, the malware needs to locate the current updll3.dll3 on the system so its latest version can be stored in memory, removed from disk, and dropped just before another system restart. Two approaches are used to achieve this:
Reading eScan folder location from HKEY_LOCAL_MACHINE\SOFTWARE\AVC3
If one of the checked processes is called download.exe, which is a legitimate eScan binary, it obtains the file location to discover the folder. The output can look like this:
The check for download.exe serves as an alternative for locating eScan installation folder and the code seems heavily inspired by the example code of Obtaining a File Name From a File handle on MSDN.
Finally, Puppeteer also continuously monitors the CPU usage on the system and tweaks the core allocation in such a way it is not that much resource heavy and stays under the radar.
The backdoor is set up by the previous stage, Puppeteer, by first discovering whether the machine is operating on a Windows Server or not. This is done by checking a DNS Server registry key (DNS Server service is typically running on a Windows Server edition): SOFTWARE\Microsoft\Windows NT\CurrentVersion\DNS Server
After that, the malware runs a command to check and get a number of computers joined in a domain: net group “domain computers” /domain
The data printed by the net group command typically uses 25 characters per domain joined computer plus a newline (CR+LF) per every three computers, which can be illustrated by the example below:
Example output of net group command
In this version of the backdoor setup, Puppeteer checks whether the number of returned bytes is more than 100. If so, Puppeteer assumes it runs in a network shared with at least five computers and downloads additional payloads from a hardcoded C&C (https://m.airequipment[.]net/gpse/) and executes it using PowerShell command.
Note that the threshold for the number of returned bytes was different and significantly higher in later versions of GuptiMiner, as can be seen in a dedicated section discussing Modular Backdoor, resulting in compromising only those networks which had more than 7000 computers joined in the same domain!
If the checks above pass, Puppeteer uses a PowerShell command for downloading and executing the payload and, interestingly, it is run both in the current process as well as injected in explorer.exe.
Furthermore, regardless of whether the infected computer is present in a network of a certain size or not, it tries to download additional payload from dl.sneakerhost[.]com/u as well. This payload is yet another PNG file with the appended shellcode. We know this because the code uses the exact same parsing from the specific offset 0x325 of the PNG file as described in Stage 1. However, during our analysis, this domain was already taken down and we couldn’t verify what kind of payload was being distributed here.
The Puppeteer’s backdoor setup process was improved and tweaked multiple times during its long development. In the upcoming subsections, we will focus on more important changes, mostly those which influence other parts of the malware or present a whole new functionality.
Later Puppeteer Versions
In later versions, the attackers switched to the datetime mutex paradigm (as illustrated in Mutexes in Time section) and also introduced additional process monitoring of more Sysinternals tools like Process explorer, Process monitor, as well as other tools like OllyDbg, WinDbg, and TeamViewer.
Additionally, the GuptiMiner authors also started to modify pool addresses in XMRig configurations with a new approach. They started using subdomains by “r” and “m” depending on the available physical memory on the infected system. If there is at least 3 GB of RAM available, the malware uses: m.domain.tld with auto mode and enabled huge pages.
If the available RAM is lesser than 3 GB, it uses: r.domain.tld with light mode and disabled huge pages.
In order to not keep things simple, the authors later also started to use “p” as a subdomain in some versions, without any specific reason for the naming convention (perhaps just to say it is a “pool”).
The usage of all such domains in time can be seen in the Domains timeline.
Variety in Used DLLs
Puppeteer used many different names and locations of DLLs over the years for sideloading or directly loading using scheduled tasks. For example, these might be:
We’ve also seen “cleaner” Puppeteers, meaning they didn’t contain the setup process for backdoors, but they were able to delete the malicious DLLs from the system when a running monitoring tool was detected.
In this particular version, the deployment of the backdoor was performed once every 3 months, indicating a per-quarter deployment.
The deployment happens at March, June, September, and December
Stage 4 – Backdoor
Since no one who puts such an effort into a malware campaign deploys just coinminers on the infected devices, let’s dig deeper into additional sets of GuptiMiner’s functionalities – deploying two types of backdoors on the infected devices.
One of the backdoors deployed by GuptiMiner is based on a custom build of PuTTY Link (plink). This build contains an enhancement for local SMB network scanning, and it ultimately enables lateral movement over the network to potentially exploit Windows 7 and Windows Server 2008 machines by tunneling SMB traffic through the victim’s infected device.
Local SMB Scanning
First, the plink binary is injected into netsh.exe process by Puppeteer with the Deploy per-quarter approach. After a successful injection, the malware discovers local IP ranges by reading the IP tables from the victim’s device, adding those into local and global IP range lists.
With that, the malware continues with the local SMB scanning over the obtained IP ranges: xx.yy.zz.1-254. When a device supporting SMB is discovered, it is saved in a dedicated list. The same goes with IPs that don’t support SMB, effectively deny listing them from future actions. This deny list is saved in specific registry subkeys named Sem and Init, in this location: HKEY_LOCAL_MACHINE \SYSTEM\CurrentControlSet\Control\CMF\Class where Init contains the found IP addresses and Sem contains their total count.
There are conditions taking place when such a scan is performed. For example, the scan can happen only when it is a day in the week (!), per-quarter deployment, and only at times between 12 PM and 18 PM. Here, we denoted by (!) a unique coding artefact in the condition, since checking the day of the week is not necessary (always true).
Questionable conditioning for SMB scanning
Finally, the malware also creates a new registry key HKEY_LOCAL_MACHINE\SYSTEM\RNG\FFFF three hours after a successful scan. This serves as a flag that the scanning should be finished, and no more scanning is needed.
An even more interesting datetime-related bug can be seen in a conditioning of RNG\FFFF registry removal. The removal is done to indicate that the malware can perform another SMB scan after a certain period of time.
As we can see in the figure below, the malware obtains the write time of the registry key and the current system time by SystemTimeToVariantTime API function and subtracts those. The subtraction result is a floating-point number where the integral part means number of days.
Furthermore, the malware uses a constant 60*60*60*24=5184000 seconds (60 days) in the condition for the registry key removal. However, the condition is comparing VariantTime (days) with seconds. Thus, the backdoor can activate every 51.84 days instead of the (intended?) 60 days. A true blessing in disguise.
Removal of RNG\FFFF key, deploying the backdoor after 51.84 days
Lateral Movement Over SMB Traffic
After the local SMB scan is finished, the malware checks from the received SMB packet results whether any of the IP addresses that responded are running Windows 7 or Windows Server 2008. If any such a system is found on the local network, the malware adds these IP addresses to a list of potential targets.
Furthermore, GuptiMiner executes the main() legacy function from plink with artificial parameters. This will create a tunnel on the port 445 between the attacker’s server gesucht[.]net and the victim’s device.
Parameters used for plink main() function
This tunnel is used for sending SMB traffic through the victim’s device to the IP addresses from the target list, enabling lateral movement over the local network.
Note that this version of Puppeteer, deploying this backdoor, is from 2021. We also mentioned that only Windows 7 and Windows Server 2008 are targeted, which are rather old. We think this might be because the attackers try to deploy an exploit for possible vulnerabilities on these old systems.
To orchestrate the SMB communication, the backdoor hand-crafts SMB packets on the fly by modifying TID and UID fields to reflect previous SMB communication. As shown in the decompiled code below, the SMB packet 4, which is crafted and sent by the malware, contains both TID and UID from the responses of the local network device.
The backdoor hand-crafts SMB packets on the fly
Here we provide an example how the SMB packets look like in Wireshark when sent by the malware. After the connection is established, the malware tries to login as anonymous and makes requests for \IPC$ and a named pipe.
SMB traffic captured by Wireshark
Interested reader can find the captured PCAP on our GitHub.
Another backdoor that we’ve found during our research being distributed by Puppeteer is a modular backdoor which targets huge corporate networks. It consists of two phases – the malware scans the devices for the existence of locally stored private keys and cryptocurrency wallets, and the second part is an injected modular backdoor, in the form of a shellcode.
Checks on Private Keys, Wallets, and Corporate Network
This part of the backdoor focuses on scanning for private keys and wallet files on the system. This is done by searching for .pvk and .wallet files in these locations:
C:\Users\*
D:\*
E:\*
F:\*
G:\*
If there is such a file found in the system, its path is logged in a newly created file C:\Users\Public\Ca.txt. Interestingly, this file is not processed on its own by the code we have available. We suppose the data will be stolen later when further modules are downloaded by the backdoor.
The fact that the scan was performed is marked by creating a registry key: HKEY_LOCAL_MACHINE\SYSTEM\Software\Microsoft\DECLAG
If some private keys or wallets were found on the system or the malware is running in a huge corporate environment, the malware proceeds with injecting the backdoor, in a form of a shellcode, into the mmc.exe process.
The size of the corporate environment is guessed by the same approach as Puppeteer’s backdoor setup with the difference in the scale. Here, the malware compares the returned list of computers in the domain with 200,000 characters. To recapitulate, the data printed by the net group command uses 25 characters per domain joined computer plus a newline (CR+LF) per every three computers.
Example output of net group command
This effectively means that the network in which the malware operates must have at least 7781 computers joined in the domain, which is quite a large number.
This shellcode is a completely different piece of code than what we’ve seen so far across GuptiMiner campaign. It is designed to be multi-modular with the capability of adding more modules into the execution flow. Only a networking communication module, however, is hardcoded and available by default, and its hash is 74d7f1af69fb706e87ff0116b8e4fa3a9b87275505e2ee7a32a8628a2d066549 (2022-12-19 07:31:39 UTC).
After the injection, the backdoor decrypts a hardcoded configuration and a hardcoded networking module using RC4. The RC4 key is also hardcoded and available directly in the shellcode.
The configuration contains details about which server to contact, what ports to use, the length of delays that should be set between commands/requests, among others. The domain for communication in this configuration is www.righttrak[.]net:443 and an IP address 185.248.160[.]141.
Decrypted network module configuration
The network module contains seven different commands that the attacker can use for instructing the backdoor about what to do. A complete list of commands accepted by the network module can be found in the table below. Note that each module that can be used by the backdoor contains such a command handler on its own.
Command
Description
3.0
Connect
3.1
Read socket
3.2
Write socket
3.3
Close socket
4
Close everything
6
Return 1
12
Load configuration
The modules are stored in an encrypted form in the registry, ensuring their persistence: HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\PCB
The backdoor also uses an import by hash obfuscation for resolving API functions. The hashing function is a simple algorithm that takes each byte of the exported function name, adds 1 to it, and then multiplies the previously calculated number (calculated_hash, starts with 0) by 131 and adds it to the byte:
The server www.righttrak[.]net:443 had, at the time, a valid certificate. Note for example the not-at-all-suspicious email address the authors used.
Certificate on www.righttrak[.]net:443 as shown by Censys
During our research, we have also found a 7zip SFX executable containing two files:
ms00.dat
notepad.exe
notepad.exe is a small binary that decrypts ms00.dat file using RC4 with a key V#@!1vw32. The decrypted ms00.dat file is the same Modular Backdoor malware as described above.
However, we have not seen this SFX executable being distributed by GuptiMiner. This indicates that this backdoor might be distributed by different infection vectors as well.
Related and Future Research
We’ve also observed other more or less related samples during our research.
PowerShell Scripts
Interestingly, we’ve found the C&C domain from the backdoor setup phase (in Puppeteer) in additional scripts as well which were not distributed by traditional GuptiMiner operation as we know it. We think this might be a different kind of attack sharing the GuptiMiner infrastructure, though it might be a different campaign. Formatted PowerShell script can be found below:
A PowerShell script targeting eScan (formatted)
In this case, the payload is downloaded and executed from the malicious domain only when an antivirus is installed, and its name has more than 4 letters and starts with eS. One does not have to be a scrabble champion to figure out that the malware authors are targeting the eScan AV once again. The malicious code is also run when the name of the installed AV has less than 5 letters.
We’ve found this script being run via a scheduled task with a used command: "cmd.exe" /c type "\<domain>\SYSVOL\<domain>\scripts\gpon.inc" | "\<domain>\SYSVOL\<domain>\scripts\powAMD64.dat" -nop - where powAMD64.dat is a copy of powershell.exe. The task name and location was C:\Windows\System32\Tasks\ScheduledDefrag
Usage of Stolen Certificates
We have found two stolen certificates used for signing GuptiMiner payloads. Interestingly, one of the used stolen certificates originates in Winnti operations. In this particular sample, the digital signature has a hash: 529763AC53562BE3C1BB2C42BCAB51E3AD8F8A56
This certificate is the same as mentioned by Kaspersky more than 10 years ago. However, we’ve also seen this certificate to be used in multiple malware samples than just GuptiMiner, though, indicating a broader leak.
A complete list of stolen certificates and their usage can be found in the table below:
During our research, we’ve also found an information stealer which holds a rather similar PDB path as was used across the whole GuptiMiner campaign (MainWork): F:\!PROTECT\Real\startW-2008\MainWork\Release\MainWork.pdb
However, we haven’t seen it distributed by GuptiMiner and, according to our data, it doesn’t belong to the same operation and infection chain. This malware performs stealing activities like capturing every keystroke, harvesting HTML forms from opened browser tabs, noting times of opened programs, etc., and stores them in log files.
What is truly interesting, however, is that this information stealer might come from Kimsuky operations. Also known as Black Banshee, among other aliases, Kimsuky is a North Korean state-backed APT group.
It contains the similar approach of searching for AhnLab real-time detection window class name 49B46336-BA4D-4905-9824-D282F05F6576 as mentioned by both AhnLab as well as Cisco Talos Intelligence in their Information-gathering module section. If such a window is found, it will be terminated/hidden from the view of the infected user.
Function that searches and terminates AhnLab’s real-time detection window class
Furthermore, the stealer contains an encrypted payload in resources, having a hash: d5bc6cf988c6d3c60e71195d8a5c2f7525f633bb54059688ad8cfa1d4b72aa6c (2021-02-19 19.02.2021 15:00:47 UTC) and it has this PDB path: F:\PROTECT\Real\startW-2008\HTTPPro\Release\HTTPPro.pdb
This module is decrypted using the standard RC4 algorithm with the key messi.com. The module is used for downloading additional stages. One of the used URLs are: http://stwu.mygamesonline[.]org/home/sel.php http://stwu.mygamesonline[.]org/home/buy.php?filename=%s&key=%s
The domain mygamesonline[.]org is commonly used by Kimsuky (with variety of subdomains).
The keylogger also downloads next stage called ms12.acm:
The next stage is downloaded with a name ms12.acm
With this, we see a possible pattern with the naming convention and a link to Modular Backdoor. As described in the Other Infection Vectors section, the 7z SFX archive contains an encrypted file called ms00.dat with which we struggle to ignore the resemblance.
Last but not least, another strong indicator for a possible attribution is the fact that the Kimsuky keylogger sample dddc57299857e6ecb2b80cbab2ae6f1978e89c4bfe664c7607129b0fc8db8b1f, which is mentioned in the same blogpost from Talos, contains a section called .vlizer, as seen below:
Kimsuky keylogger sections
During the GuptiMiner installation process (Stage 0), we wrote about the threat actors introducing Code Virtualization in 2018. This was done by using a dedicated section called .v_lizer.
Conclusion
In this analysis, we described our findings regarding a long-standing threat we called GuptiMiner, in detail. This sophisticated operation has been performing MitM attacks targeting an update mechanism of the eScan antivirus vendor. We disclosed the security vulnerability to both eScan and the India CERT and received confirmation on 2023-07-31 from eScan that the issue was fixed and successfully resolved.
During the GuptiMiner operation, the attackers were deploying a wide chain of stages and functionalities, including performing DNS requests to the attacker’s DNS servers, sideloading, extracting payloads from innocent-looking images, signing its payloads with a custom trusted root anchor certification authority, among others.
Two different types of backdoors were discovered, targeting large corporate networks. The first provided SMB scanning of the local network, enabling lateral movement over the network to potentially exploit vulnerable Windows 7 and Windows Server 2008 systems on the network. The second backdoor is multi-modular, accepting commands on background to install more modules as well as focusing on stealing stored private keys and cryptowallets.
Interestingly, the final payload distributed by GuptiMiner was also XMRig which is a bit unexpected for such a thought-through operation.
We have also found possible ties to Kimsuky, a notorious North Korean APT group, while observing similarities between Kimsuky keylogger and fragments discovered during the analysis of the GuptiMiner operation.
eScan follow-up
We have shared our findings and our research with eScan prior to publishing this analysis. For the sake of completeness, we are including their statement on this topic:
“I would also like to highlight some key points: 1. Our records indicate that the last similar report was received towards the end of the year 2019. 2. Since 2020, we have implemented a stringent checking mechanism that utilizes EV Signing to ensure that non-signed binaries are rejected. 3. Multiple heuristic rules have been integrated into our solution to detect and block any instances of legitimate processes being used for mining, including the forking of unsigned binaries. 4. While our internal investigations did not uncover instances of the XRig miner, it is possible that this may be due to geo-location factors. 5. Our latest solution versions employ secure (https) downloads, ensuring encrypted communication when clients interact with our cloud-facing servers for update downloads.”
According to our telemetry, we continue to observe new infections and GuptiMiner builds within our userbase. This may be attributable to eScan clients on these devices not being updated properly.
Indicators of Compromise (IoCs)
In this section, we would like to summarize the Indicators of Compromise mentioned in this analysis. As they are indicators, it doesn’t automatically mean the mentioned files and/or domains are malicious on their own.
For more detailed list of IoCs of the whole GuptiMiner campaign, please visit our GitHub.
Last Week in Security is a summary of the interesting cybersecurity news, techniques, tools and exploits from the past week. This post covers 2024-04-16 to 2024-04-22.
How we built the new Find My Device network with user security and privacy in mind - Google enters the "Find My" crowdsourced device-locating network game with the similarly named "Find My Device" network. It support the standard which allows trackers to be detected by iOS devices (and vice-versa) so unwanted trackers will alert users.
GitHub comments abused to push malware via Microsoft repo URLs - The fact that GitHub will upload a file to a publically accessable URL during comment editing, actors don't need to publish comments to get files hosted under trusted projects URLs. If you're ok with giving your payload to Microsoft (GitHub), this is a pretty sneaky way to host it.
SSO tax, cut - Tailscale is the best VPN solution there is (unsponsored opinion). Between this change and Tailnet lock, they have eliminated all issues I had with their service. If you're a self-hosting true purist, there is still headscale.
An Introduction to the Canadian Program for Cyber Security Certification (CPCSC) - Starting at the end of 2024, Canadian defense industry suppliers will need to be certified under the Canadian Program for Cyber Security Certification (CPCSC) to bid on certain government contracts, an initiative designed to enhance security measures within the nation's federal contracting processes.
CVE-2023-6345: Integer overflow in Skia MeshOp::onCombineIfPossible - An intiger overflow in the Skia graphics library has been used to exploit Chrome. The fact that it would not appear in debug builds due to assert calls that are not compiled with release builds is interesting. Make sure you are fuzzing release binaries!
LSA Whisperer - Some seriously indepth research into the local security authority (LSA) of Windows which leads to all kinds of functionality. My favorite is the possible use of CacheLogon to cache a specific NT hash into an active logon session which will allow for stable Pass-the-hash without having to patch LSASS memory (but will require injection into LSASS). I can only imagine the amount of reverse-engineering it took to get to the lsa-whisperer.
Passbolt: a bold use of HaveIBeenPwned - Passbolt is a password manager that uses the HaveIBeenPwned API to check if a password has been compromised. This post goes into the details of how they implemented it.
ROPGadget: Writing a ROPDecoder - This post discusses creating a ROPDecoder from scratch, detailing the selection and use of ROP gadgets to encode and decode shellcode, and automating the process to handle bad characters effectively in exploit dev.
The Windows Registry Adventure #1: Introduction and research results - Wild. Mateusz Jurczyk of Google Project Zero audited the Windows Registry for local privilege escalation bugs over 20 months, identifying multiple vulnerabilities now fixed as 44 CVEs by Microsoft, utilizing methods from fuzzing to manual review in an extensive security research effort.
State of DevSecOps - Datadog's State of DevSecOps report is out. TLDR - Java/JS account for tons of issues, automated security scanners are just noise, the industry sucks at prioritizing what to fix, manual cloud deployments (no IaC) is still very common, and more.
lsa-whisperer - Tools for interacting with authentication packages using their individual message protocols.
KExecDD - Admin to Kernel code execution using the KSecDD driver.
CloudConsoleCartographer - Released at Black Hat Asia on April 18, 2024, Cloud Console Cartographer is a framework for condensing groupings of cloud events (e.g. CloudTrail logs) and mapping them to the original user input actions in the management console UI for simplified analysis and explainability.
PasteBomb - PasteBomb C2-less RAT. The creator of this project is only 13 years old. Impressive! Great work.
poutine - poutine is a security scanner that detects misconfigurations and vulnerabilities in the build pipelines of a repository. It supports parsing CI workflows from GitHub Actions and Gitlab CI/CD.
panos-scanner - Determine the Palo Alto PAN-OS software version of a remote GlobalProtect portal or management interface.
LetMeowIn - A sophisticated, covert Windows-based credential dumper using C++ and MASM x64.
MagicDot - A set of rootkit-like abilities for unprivileged users, and vulnerabilities based on the DOT-to-NT path conversion known issue.
New to Me and Miscellaneous
This section is for news, techniques, write-ups, tools, and off-topic items that weren't released last week but are new to me. Perhaps you missed them too!
smugglefuzz - A rapid HTTP downgrade smuggling scanner written in Go.
netz - Discover internet-wide misconfigurations while drinking coffee.
cognito-scanner - A simple script which implements different Cognito attacks such as Account Oracle or Privilege Escalation.
Living Off the Pipeline - "....to inventory how development tools (typically CLIs), commonly used in CI/CD pipelines, have lesser-known RCE-By-Design features ("foot guns"), or more generally, can be used to achieve arbitrary code execution by running on untrusted code changes or following a workflow injection. "
BAADTokenBroker post-exploitation tool designed to leverage device-stored keys (Device key, Transport key etc..) to authenticate to Microsoft Entra ID.
Techniques, tools, and exploits linked in this post are not reviewed for quality or safety. Do your own research and testing.
Microsoft Exchange 2019 prior to March 2024 used the Oracle Outside-In libraries to parse specific file types when attached to emails if an attachment inspection mail flow was configured. By default, Exchange has no mail flow rules configured. Several file types were identified to be processed by the server using the Outside-In SDK. Specifically, the following libraries loaded after using the sample Outside-In files as test attachments.
vspdf.dll
vshtml.dll
vseshr.dll
vsw97.dll
vspp12.dll
vsviso.dll
vspp97.dll
vsw12.dll
vsxl5.dll
These libraries are built to parse files and return any plaintext data they can. They live in a folder labelled TE_vXXX in the Exchange installation directory. However, they are repackaged Oracle Outside-In Content Access libraries. You can readily download the Content Access SDK and demo applications from Oracle directly. Other applications integrate this SDK as well, such as Oracle SQL Server and Oracle WebCenter Content Server.
Previous research into Outside-In by Joshua Drake and Will Dorman a decade ago showed that more digging into the framework could be fruitful. Atredis has identified several files that caused the Exchange file scanner to crash when using the OutsideInModule.dll library to parse attachments.
Exchange itself is configured to prefer Outside-In for some filetypes.
However, we were unable to reproduce jpg and tiff vulnerabilities we found in the Outside-In libraries through Exchange. We also noticed that jpg and tiff were removed from the file types supported by file inspection by Exchange mail flows sometime after 2021. These configurations relative to tiff and jpg could be old and useless.
In order to fuzz the libraries, we used two different methods. One method was statically instrumenting the Linux version of the Outside-In Content Access libraries, then fuzzing with AFL. The other option chosen was to dynamically instrument and fuzz with Jackalope on Windows.
AFL
In order to fuzz with AFL, we used afl-dyninst. We used an older version of AFL and dyninst in these examples because it's what we've used in the past and knew it worked. However, a coworker has shown that AFL++ has pretty good dyninst support too. In the future, we'll certainly give it a try. To get things running quickly, we will instrument a simple binary shipped with Outside-In as a demo application called memoryio. It simply accepts a file as an argument and spits out any plaintext contents.
Note the -m 8 in the above command. It was found that instrumenting every basic block caused serious instability. Instead, we are instrumenting any basic blocks 8 bytes or larger. During testing, we also noticed that the library will actively write to ~/.oit while running for no useful reason. Creating the directory, but making it read-only, effectively bypassed this. Otherwise it caused stuttering and hangs.
After copying the instrumented libraries into the correct spots, we can run afl-fuzz. We use AFL_SKIP_BIN_CHECK because we used afl-dyninst to instrument an already-compiled utility called memoryio, rather than compiling our own harness.
AFL_SKIP_BIN_CHECK=1 screen afl-fuzz -i in -o out -m none -M mainA:1/3 -- ./test @@
AFL_SKIP_BIN_CHECK=1 screen afl-fuzz -i in -o out -m none -M mainB:2/3 -- ./test @@
AFL_SKIP_BIN_CHECK=1 screen afl-fuzz -i in -o out -m none -M mainC:3/3 -- ./test @@
AFL_SKIP_BIN_CHECK=1 screen afl-fuzz -i in -o out -m none -S subX -- ./test @@
We started 3 main fuzzers in parallel, each running on different deterministic phases. In general, running multiple main fuzzers is a bad idea since they will all perform the same work. However, in the configuration we set up, each fuzzer focuses on different deterministic stages. We then set up 10 sub fuzzers. These fuzzers perform a different kind of strategy than the deterministic main fuzzers. They make random changes to the inputs and just see what happens.
The machine we are fuzzing the Linux libraries on has 32 cores, so we have a bit more leg room. After those fuzzers were set up, a slightly different AFL configuration was used for spice.
AFL_SHUFFLE_QUEUE=1 AFL_SKIP_BIN_CHECK=1 screen afl-fuzz -i in -o out -m none -S subXX -- ./test @@
The AFL_SHUFFLE_QUEUE option was added onto 10 more sub fuzzers. This option takes the queue and randomizes the order of the inputs. In general, you shouldn't need to do this. However, we have so many fuzzers running that it's not going to hurt us in this particular instance and could easily help us.
After that, we get about 100 executions per second per fuzzer across 23 fuzzers. It's not amazing, but it'll do. If we wanted to speed things up in the future, we could implement our own harness with AFL persistent mode.
Preparing an environment to fuzz with Jackalope was straight forward. This was done by first cloning the most up to date Jackalope repository and following the build instructions. The fuzzing corpus used for most file formats was acquired from strongcourage's fuzzing-corpus repository. Additional corpus for the `xl5` format was also acquired from the internet with google dorks (Ex:?index.of? xls 1999). The batch file used to execute the fuzzer can be seen below:
Shared memory sample delivery was also used improve fuzzer performance as can be seen by the -delivery shmem flag option. The memoryio sample program provided by Oracle as part of its Outside In library was modified to accept shared memory as input. In addition to shared memory delivery, persistent fuzzing was utilized as can be seen by the -persist flag to fuzz in persistent mode for an added performance boost.
Results
We reported to Microsoft three crashes through our vector on Exchange. However, since the issues were in Oracle’s software, Oracle issued the vulnerability ID CVE-2024-21118.
Exchange will not try to use Outside-In on every file type the library itself supports. These crashes were reproduced by sending an email to the Exchange server with the malicious file attached. As stated previously, a mail flow inspection rule must be configured.
Microsoft subsequently disabled the Outside-In libraries in Exchange in the March 2024 Patch Tuesday updates. You can read more about the patches and advisory here. Atredis would specifically like to thank Lisa Olson and the whole Microsoft Security team for their heroic efforts in working through the disclosure.
Be sure to catch Ali talking more in-depth about our bug hunting and debugging process at RVAsec this summer.
Today on Cyber Work, we are talking operational technology, or OT, security with guest, Robin Berthier of Network Perception. From his earliest studies to his time as an academic researcher, Berthier has dedicated his career to securing the intersection between operational technology and network security, with some pretty imaginative solutions to show for it. In today’s episode, Berthier explains why modern OT security means thinking more about the mechanics of the machinery than the swiftness of the software solutions, the big conversation that infrastructure and ICS Security need to have about nation-state attackers (and finally are having!) and Berthier's best piece of career advice turns into some excellent thoughts on the importance of maintaining your network… and I don’t mean routing and switching!
0:00 - Industrial control systems cybersecurity 1:54 - How Robin Berthier got into tech 3:38 - Majoring in cybersecurity 4:55 - Intrusion detection systems 9:18 - Mechanical and cybersecurity tools 12:33 Launching Network Perception 17:03 - Current state of ICS and OT infrastructure 20:24 - Cyberattacks on industrial control systems 28:35 -Skills needed to work in industrial control systems 35:19 - Where are ICS security jobs? 36:39 - Getting into local OT systems 37:55 - Skills gaps in ICS 39:21 - Best piece of career advice 41:01 - Cultivating a work network 43:28 - What is Network Perception? 45:27 - Learn more about Robin Berthier 45:58 - Outro
About Infosec Infosec’s mission is to put people at the center of cybersecurity. We help IT and security professionals advance their careers with skills development and certifications while empowering all employees with security awareness and phishing training to stay cyber-safe at work and home. More than 70% of the Fortune 500 have relied on Infosec Skills to develop their security talent, and more than 5 million learners worldwide are more cyber-resilient from Infosec IQ’s security awareness training. Learn more at infosecinstitute.com.
Organizations are increasingly turning to cloud computing for IT agility, resilience and scalability. Amazon Web Services (AWS) stands at the forefront of this digital transformation, offering a robust, flexible and cost-effective platform that helps businesses drive growth and innovation.
However, as organizations migrate to the cloud, they face a complex and growing threat landscape of sophisticated and cloud-conscious threat actors. Organizations with ambitious digital transformation strategies must be prepared to address these security challenges from Day One. The potential threat of compromise underscores the critical need to understand and implement security best practices tailored to the unique challenges of cloud environments.
Central to understanding and navigating these challenges is the AWS shared responsibility model. AWS is responsible for delivering security of the cloud, including the security of underlying infrastructure and services. Customers are responsible for protecting their data, applications and resources running in the cloud. This model highlights the importance of proactive security measures at every phase of cloud migration and operation and helps ensure businesses maintain a strong security posture.
In this blog, we cover five best practices for securing AWS resources to help you gain a better understanding of how to protect your cloud environments as you build in the cloud.
Best Practice #1: Know All of Your Assets
Cloud assets are not limited to compute instances (aka virtual machines) — they extend to all application workloads spanning compute, storage, networking and an extensive portfolio of managed services.
Understanding and maintaining an accurate inventory of your AWS assets is foundational to securing your cloud environment. Given the dynamic nature of cloud computing, it’s not uncommon for organizations to inadvertently lose track of assets running in their AWS accounts, which can lead to risk exposure and attacks on unprotected resources. In some cases, accounts created early in an organization’s cloud journey may not have the standard security controls that were implemented later on. In another common scenario, teams may forget about and unintentionally remove mitigations put in place to address application-specific exceptions, exposing those resources to potential attack.
To maintain adequate insight and awareness of all AWS assets in production, organizations should consider implementing the following:
Conduct asset inventories: Use tools and processes that provide continuous visibility into all cloud assets. This can help maintain an inventory of public and private cloud resources and ensure all assets are accounted for and monitored. AWS Resource Explorer and Cost Explorer can help discover new resources as they’re provisioned.
Implement asset tagging and management policies: Establish and enforce policies for tagging cloud resources. This practice aids in organizing assets based on criticality, sensitivity and ownership, making it easier to manage and prioritize security efforts across the cloud environment. In combination with the AWS Identity and Access Management (IAM) service, tagging can also be used to dynamically grant access to resources via attribute-based access control (ABAC).
Integrate security tools for holistic visibility: Combine the capabilities of cloud security posture management (CSPM) with other security tools like endpoint detection and response (EDR) solutions. Integration of these tools can provide a more comprehensive view of the security landscape, enabling quicker identification of misconfigurations, vulnerabilities and threats across all AWS assets. AWS services including Trusted Advisor, Security Hub, GuardDuty, Config and Inspector provide actionable insights to help security and operations teams improve their security posture.
CrowdStrike Falcon® Cloud Security makes it easy to implement these practices by offering a consolidated platform that integrates with AWS features to maintain coverage across a customer’s entire multi-account environment. Falcon Cloud Security offers CSPM, which leverages AWS EventBridge, IAM cross-account roles and CloudTrail API audit telemetry to provide continuous asset discovery, scan for misconfigurations and suspicious behavior, improve least-privilege controls and deploy runtime protection on EC2 and EKS clusters as they’re provisioned. It guides customers on how to secure their cloud environments to accelerate the learning of cloud security skills and the time-to-value for cloud initiatives. Cloud Operations teams can deploy AWS Security Hub with the CrowdStrike Falcon® Integration Gateway to view Falcon platform detections and trigger custom remediations inside AWS. AWS GuardDuty leverages CrowdStrike Falcon® Adversary Intelligence indicators of compromise and can provide an additional layer of visibility and protection for cloud teams.
Best Practice #2: Enforce Multifactor Authentication (MFA) and Use Role-based Access Control in AWS
Stolen credentials pose a severe threat — whether they are user names and passwords or API key IDs and secrets — allowing adversaries to impersonate legitimate users and bypass identity-based access controls. This risk is exacerbated by scenarios where administrator credentials and hard-coded passwords are inadvertently stored in public-facing locations or within code repositories accessible online. Such exposures give attackers the opportunity to intercept live access keys, which they can use to authenticate to cloud services, posing as trusted users.
In cloud environments, as well as on-premises, organizations should adopt identity security best practices such as avoiding use of shared credentials, assigning least-privilege access policies and using a single source of truth through identity provider federation and single sign-on (SSO). AWS services such as IAM, Identity Center and Organizations can facilitate secure access to AWS services by supporting the creation of granular access policies, enabling temporary session tokens, and reporting on cross-account trusts and excessively permissive policies, thus minimizing the likelihood and impact of access key exposure. By implementing MFA in conjunction with SSO, role-based access and temporary sessions, organizations make it much harder for attackers to steal credentials and, more importantly, to effectively use them.
Falcon Cloud Security includes cloud infrastructure entitlement management (CIEM), which evaluates whether IAM roles are overly permissive and provides the visibility to make changes with awareness of which resources will be impacted. Additionally, Falcon Cloud Security conducts pre-runtime scanning of container images and infrastructure-as-code (IaC) templates to uncover improperly elevated Kubernetes pod privileges and hard-coded credentials to prevent credential theft and lateral movement. Adding the CrowdStrike Falcon® Identity Protection module delivers strong protection for Active Directory environments, dynamically identifying administrator and service accounts and anomalous or malicious use of credentials, and allowing integration with workload detection and response actions.
Best Practice #3: Automatically Scan AWS Resources for Excessive Public Exposure
The inadvertent public exposure and misconfiguration of cloud resources such as EC2 instances, Relational Database Service (RDS) and containers on ECS and EKS through overly permissive network access policies pose a risk to the security of cloud workloads. Such lapses can accidentally open the door to unauthorized access to vulnerable services, providing attackers with opportunities to exploit weaknesses for data theft, launching further attacks and moving laterally within the cloud environment.
To mitigate these risks and enhance cloud security posture, organizations should:
Implement automated security audits: Utilize tools like AWS Trusted Advisor, AWS Config and AWS IAM Access Analyzer to continuously audit the configurations of AWS resources and identify and remediate excessive public exposure or misconfigurations.
Secure AWS resources with proper security groups: Configure security groups for logical groups of AWS resources to restrict inbound and outbound traffic to only necessary and known IPs and ports. Whenever possible, use network access control lists (NACLs) to restrict inbound and outbound access across entire VPC subnets to prevent data exfiltration and block communication with potentially malicious external entities. Services like AWS Firewall Manager provide a single pane of glass for configuring network access for all resources in an AWS account using VPC Security Groups, Web Application Firewall (WAF) and Network Firewall.
Collaborate across teams: Security teams should work closely with IT and DevOps to understand the necessary external services and configure permissions accordingly, balancing operational needs with security requirements.
Falcon Cloud Security continuously monitors AWS service configurations for best practices, both in live environments and in pre-runtime IaC templates as part of a CI/CD or GitOps pipeline. Overly permissive network security policies are dynamically discovered and recorded as indicators of misconfiguration (IOMs), which are automatically correlated with all other security telemetry in the environment, along with insight into how the misconfiguration can be mitigated by the customer or maliciously used by the adversary.
Best Practice #4: Prioritize Alerts Based on Risk
Adversaries are becoming more skilled in attacking cloud environments, as evidenced by a 75% increase in cloud intrusions year-over-year in 2023. They are also growing faster: The average breakout time for eCrime operators to move laterally from one breached host to another host was just 62 minutes in 2023. The rise of new technologies, such as generative AI, has the potential to lower the barrier to entry for less-skilled adversaries, making it easier to launch sophisticated attacks. Amid these evolving trends, effective alert management is paramount.
Cloud services are built to deliver a constant stream of API audit and service access logs, but sifting through all of this data can overwhelm security analysts and detract from their ability to focus on genuine threats. While some logs may indicate high-severity attacks that demand immediate response, most tend to be informational and often lack direct security implications. Generating alerts based on this data can be imprecise, potentially resulting in many false positives, each of which require SecOps investigation. Alert investigations can consume precious time and scarce resources, leading to a situation where noisy security alerts prevent timely detection and effective response.
To navigate this complex landscape and enhance the effectiveness of cloud security operations, several best practices can be adopted to manage and prioritize alerts efficiently:
Prioritize alerts strategically: Develop a systematic approach to capture and prioritize high-fidelity alerts. Implementing a triage process based on the severity of events helps focus resources on the most critical investigations.
Create context around alerts: Enhance alert quality by enriching them with correlated data and context. This additional information increases confidence in the criticality of alerts, enabling more informed decision-making regarding their investigation.
Integrate and correlate telemetry sources: Improve confidence in prioritizing or deprioritizing alerts by incorporating details from other relevant data sources or security tools. This combination allows for a more comprehensive understanding of the security landscape, aiding in the accurate identification of genuine threats.
Outsource to a competent third party: For organizations overwhelmed by the volume of alerts, partnering with a managed detection and response (MDR) provider can be a viable solution. These partners can absorb the event burden, alleviating the bottleneck and allowing in-house teams to focus on strategic security initiatives.
AWS Services like AWS GuardDuty, which is powered in part by CrowdStrike Falcon Adversary Intelligence indicators of compromise (IOCs), help surface and alert on suspicious and malicious activity within AWS accounts, prioritizing indicators of attack (IOAs) and IOCs based on risk severity.
Falcon Cloud Security is a complete cloud security platform that unifies world-class threat intelligence and elite threat hunters. Falcon Cloud Security correlates telemetry and detections across IOMs, package vulnerabilities, suspicious behavior, adversary intelligence and third-party telemetry ingested through a library of data connectors to deliver a context-based risk assessment, which reduces false positives and automatically responds to stop breaches.
Best Practice #5: Enable Comprehensive Logging
Adversaries that gain access to a compromised account can operate virtually undetected, limited only by the permissions granted to the account they used to break in. This stealthiness is compounded by the potential for log tampering and manipulation, where malicious actors may alter or delete log files to erase evidence of their activities. Such actions make it challenging to trace the adversary’s movements, evaluate the extent of data tampering or theft, and understand the full scope of the security incident. The lack of a comprehensive audit trail due to disabled or misconfigured logging mechanisms hinders the ability to maintain visibility over cloud operations, making it more difficult to detect and respond to threats.
In response, organizations can:
Enable comprehensive logging across the environment: Ensure AWS CloudTrail logs, S3 server access logs, Elastic Load Balancer (ELB) access logs, CloudFront logs and VPC flow logs are activated to maintain a detailed record of all activities and transactions.
Ingest and alert on logs in your SIEM: Integrate and analyze logs within your security information and event management (SIEM) system to enable real-time alerts on suspicious activities. Retain logs even if immediate analysis capabilities are lacking, as they may provide valuable insights in future investigations.
Ensure accuracy of logged data: For services behind proxies, like ELBs, ensure the logging captures original IP addresses from the X-Forwarded-For field to preserve crucial information for analysis.
Detect and prevent log tampering: Monitor for API calls that attempt to disable logging and for unexpected changes in cloud services or account settings that could undermine logging integrity, in line with recommendations from the MITRE ATT&CK® framework. In addition, features such as MFA-Delete provide additional protection by requiring two-factor authentication to allow deletion of S3 buckets and critical data.
CrowdStrike Falcon Cloud Security for AWS
Falcon Cloud Security integrates with over 50 AWS services to deliver effective protection at every stage of the cloud journey, combining multi-account deployment automation, sensor-based runtime protection, agentless API attack and misconfiguration detection, and pre-runtime scanning of containers, Lambda functions and IaC templates.
CrowdStrike leverages real-time IOAs, threat intelligence, evolving adversary tradecraft and enriched telemetry from across vectors such as endpoint, cloud, identity and more. This not only enhances threat detection, it also facilitates automated protection, remediation and elite threat hunting, aligned closely with understanding AWS assets, enforcing strict access control and authentication measures, and ensuring meticulous monitoring and management of cloud resources.
You can try Falcon Cloud Security through a Cloud Security Health Check, during which you’ll engage in a one-on-one session with a cloud security expert, evaluate your current cloud environment, and identify misconfigurations, vulnerabilities and potential cloud threats.
Protecting AWS Resources with Falcon Next-Gen SIEM
CrowdStrike Falcon® Next-Gen SIEM unifies data, AI, automation and intelligence in one AI-native platform to stop breaches. Falcon Next-Gen SIEM extends CrowdStrike’s industry-leading detection and response and expert services to all data, including AWS logs, for complete visibility and protection. Your team can detect and respond to cloud-based threats in record time with real-time alerts, live dashboards and blazing-fast search. Native workflow automation lets you streamline analysis of cloud incidents and say goodbye to tedious tasks.
For the first time ever, your analysts can investigate cloud-based threats from the same console they use to manage cloud workload security and CSPM. CrowdStrike consolidates multiple security tools, including next-gen SIEM and cloud security, on one platform to cut complexity and costs. Watch a 3-minute demo of Falcon Next-Gen SIEM to see it in action.
As Porter Airlines scaled its business, it needed a unified cybersecurity platform to eliminate the challenges of juggling multiple cloud, identity and endpoint security products.
Porter consolidated its cybersecurity strategy with the single-agent, single-console architecture of the AI-native CrowdStrike Falcon® XDR platform.
With the Falcon platform, the airline has reduced cost and complexity while driving better security outcomes across its business and partner network.
All passengers on Porter Airlines travel in style with complimentary beer and wine, free premium snacks, free WiFi, free inflight entertainment, no middle seats — the list goes on.
With these perks, it’s no wonder Porter is growing fast. Headquartered in Toronto, Porter revolutionized short-haul flying in 2006. Since then, the airline has stretched its wings, amassing 58 aircraft, 3,200 employees and 33 destinations across North America.
Early success has only fueled the company’s ambitions. Porter plans to double its workforce by 2026 and blanket all major U.S. cities and beyond. While this growth brings exciting business opportunities, it also creates new cybersecurity challenges, as the company piles on more data, devices and attack surfaces to protect.
“When we started, we weren’t really a target for attackers, but we’re seeing more activity today,” said Jason Deluce, Director of Information Technology at Porter Airlines.
To secure its growing business, Porter relies on the AI-native CrowdStrike Falcon platform and CrowdStrike Falcon® Complete for 24/7 managed detection and response (MDR). This is the story of how CrowdStrike delivers the flexible and scalable cybersecurity that Porter needs to secure its business today and into the open skies ahead.
New Security Requirements
The move to CrowdStrike was born out of necessity. Porter’s previous security stack centered on a noisy endpoint detection and response (EDR) solution. Alerts overwhelmed Deluce’s lean security team, and the vendor wasn’t much help. Then, after three years without contact, the sales rep dropped a high renewal bill.
Porter used a separate cybersecurity platform for vulnerability management and log management. But according to Deluce, “it was all manual. It detects vulnerabilities, but it doesn’t do anything about them. That wasn’t enough for us.”
Furthermore, none of the solutions were integrated, leaving Deluce and his team with multiple agents and multiple consoles to operate. “They kind of talk about the same thing, but there’s nothing to marry them together in one place. You have to go to separate places, try to make sense of the data and determine if it’s accurate or not.”
With the business taking off and cyber threats surging, Porter needed a modern cybersecurity platform to reduce the noise and stop breaches. With its single-agent, cloud-native architecture, the Falcon platform gave Porter exactly what it needed: one agent and one console for complete visibility and protection across the company’s expanding security estate.
And whereas the previous cybersecurity vendors left Deluce with more questions than answers, Falcon Complete MDR acts as a force multiplier for Porter’s security team, providing around-the-clock expert management, monitoring, proactive threat hunting and end-to-end remediation, delivered by CrowdStrike’s team of dedicated security experts.
Stopping Breaches in the Cloud with the Falcon Platform
A few years back, Porter made the strategic move to use Amazon Web Services (AWS) for hosting its business applications and corporate data. While this cloud strategy delivers the scalability and flexibility Porter needs to grow, it also introduces new security risks.
With the lightweight Falcon agent already deployed, Deluce was able to easily add CrowdStrike Falcon® Cloud Security to its arsenal of protections. And because CrowdStrike and Amazon are strategic partners with many product integrations, deployment was a breeze.
“The one-click deployment is pretty amazing,” said Deluce. “We were able to deploy Falcon Cloud Security to a bunch of servers very quickly.”
Falcon Cloud Security is the industry’s only unified agent and agentless platform for code-to-cloud protection, integrating pre-runtime and runtime protection, and agentless technology in a single platform. Being able to collect and see all of that information in a single console provided immediate value, according to Deluce.
Porter soon looked to expand its cloud protections with CrowdStrike Falcon® Application Security Posture Management (ASPM). While evaluating the product, Deluce gained visibility into dependencies, vulnerabilities, data types and changes his team previously had no visibility into, ranging from low risk to high risk. The company moved fast to deploy Falcon ASPM.
With ASPM delivered as part of Falcon Cloud Security, Porter gets comprehensive risk visibility and protection across its entire cloud estate, from its AWS cloud infrastructure to the applications and services running inside of it — delivered from the unified Falcon platform.
Better Visibility and Protection
Porter has deployed numerous CrowdStrike protections to fortify the airline against cyber threats. Recently, that included CrowdStrike Falcon® Identity Protection to improve visibility of identity threats, stop lateral movement and extend multifactor authentication (MFA).
Deluce noted that previously, he had no easy way of knowing about stale accounts or service accounts. He’d have to do an Active Directory dump and go through each line to see what was happening. With Falcon Identity Protection, Deluce saw that Porter had over 200 privileged accounts, which didn’t add up, given his small number of domain admins.
“I saw that a large group had been given print operator roles, which would have allowed them to move laterally to domain admins,” noted Deluce. “With Falcon Identity Protection, I was able to change those permissions quickly to reduce our risk. I also started enforcing MFA from the solution, which is something I couldn’t do before with the products we had.”
Gaining better visibility has been an important theme for Porter. The company also uses CrowdStrike Falcon® Exposure Management to gain comprehensive visibility to assets, attack surfaces and vulnerabilities with AI-powered vulnerability management.
“We’re taking on new vendors faster than we’re taking on airplanes, so we need to limit our exposures,” said Deluce. “With Falcon Exposure Management, I can scan our digital estate to see which assets we have exposed to the internet, as well as any exposures belonging to our subsidiaries and partners, so we can reduce those risks.”
The solution provided quick value when Deluce noticed one of his APIs was exposed to the internet, which shouldn’t have been the case. He also found that many of the assets connected to the company’s network belonged to third parties, which is a major risk, given that any attack against those devices could affect Porter.
“Falcon Exposure Management shows us our vulnerabilities and exposures, and how we can reduce them,” said Deluce. “This is key as we continue to build out the company and expand our partner network.”
Securing the Future with CrowdStrike
Safety is paramount to airlines — and that includes keeping customer data safe. With its investment in CrowdStrike, Porter is demonstrating its commitment to safety and security.
But for cybersecurity leaders like Deluce, the work is never done. Adversaries continue to get bolder, faster and stealthier. To stay ahead of evolving threats, Porter continues to lean into CrowdStrike, recently testing Charlotte AI and CrowdStrike Falcon® Adversary Intelligence, among other capabilities designed to help teams work faster and smarter.
Deluce reflected on how far the company has come in its cybersecurity journey and the role that security plays in enabling future growth.
“We’ve gone from multiple tools, high complexity and spending a lot for poor visibility to a single pane of glass where we can do a bunch of new things with one platform,” concluded Deluce. “Cybersecurity is key to scaling the company and we know CrowdStrike is there for us.”
Organizations are increasingly concerned about high-profile employees’ information being exposed on the deep and dark web. The CrowdStrike Counter Adversary Operations team is often asked to find fake social media accounts and personally identifiable information (PII) that might be exposed.
Impersonations and leaked PII can unravel lives and ruin the reputations of individuals and their organizations. Through surface, deep and dark web monitoring, CrowdStrike is able to provide timely alerts to our customers, helping them take quick action to mitigate the potential damage caused by these posts.
The CrowdStrike Counter Adversary Operations team has created thousands of monitoring rules that protect our customers, and nearly 20% of them focus solely on high-profile employees. In this blog, we break down the data source categories that generate the most actionable notifications — including the type of data being posted — and name the actors that are posting most frequently on those sites.
CrowdStrike Counter Adversary Operations Analysis
The analysis and graph below represent only the true positive notifications from the Counter Adversary Operations team. A true positive notification is one that has been determined to be malicious in nature and actionable for customers.
By analyzing true positive notifications, we can identify the top actionable sources and their effects on organizations.
Actionable Source Types
Figure 1. Percentage of true positives by source type, February 2021-February 2024
Chat Mediums
The most common chat site with true positives seen in our monitoring is Telegram.
Telegram included data that could be used to target high-profile employees and potentially their organizations. Telegram, unlike other sites, is not the most directly targeted source — high-profile employee data is found within the site, but this data is within combined lists of millions of other people’s data, so it is unlikely the author knows they have captured the sensitive data of high-profile employees. Otherwise, actors would individualize the credentials for purchase at a premium price. The majority of the sensitive data identified within Telegram includes email addresses and passwords for third-party applications. Although this does not directly tie into targeting high-profile employees to undermine their companies’ technical infrastructure, if these employees use the same password for their personal and corporate accounts, it can have catastrophic consequences.
There are numerous authors on Telegram posting what we would classify as true positive notifications for high-profile employees. These notifications typically include email and password combinations that are currently being used by the high-profile employees.
Criminal Marketplaces
Like Telegram, criminal marketplaces include data that could have an immediate impact on high-profile employees. Specifically, the majority of the exposed data on criminal marketplaces comes from multiple large breaches of credit card information, and threat actors look to sell credit card data individually and indiscriminately. Threat actors parse the data individually by credit card owner name and list each one for sale, typically for less than $1 USD.
Threat actors do not appear to do research based on the accounts they are selling, which leads us to believe that bot farms are being used to perform automated collection. For instance, if a threat actor knew they had a working credit card — along with the purchasing information of a CEO or high-ranking official — they would likely either raise the price for that individual or attempt to further exploit the information.
“Carder Market” is a broker site that sells exposed credit card information, including the PII data needed to make online transactions. The site is nondiscriminatory and lists all accounts available after a purchase of $0.25 USD. This alludes to bot behavior, which is confirmed when attempting to identify the perpetrators — in this case, the perpetrator is one account identified as “Admin.” Though accounts are indiscriminately posted, high-profile employees could be targeted by searching for a specific name and suspected home of record within the data.
Public Repositories
Unlike chat mediums and criminal marketplaces, public repositories include highly targeted information. On public repositories, Counter Adversary Operations observed data of three types of high-profile employees: government officials, influential figures and C-suite personnel. The exposed PII included residence addresses, phone numbers, IP addresses, Social Security numbers, personal email addresses, detailed credit card information (including expiration date and security code) and vehicle plate numbers.
With the abundance of information included on these sites, bad actors have a higher potential to exploit high-profile employees either by harassing them or using their credit card/SSN information.
The public repositories we observed included Doxbin, Pastebin and GitHub. All three public repositories allow users to post anonymously (and high-profile employee material was posted anonymously), allowing bad actors to easily obfuscate themselves and their intentions.
Forums
Malicious forum posts observed by our team are largely used to create and spread conspiracy theories or make derogatory statements related to individuals who have high name recognition. These posts are meant to degrade the reputation of an individual, but we rarely see posts containing sensitive data that could compromise the individual’s corporate credentials.
For this category, the source that generated the most true positive notifications is 4chan. All true positive notifications on 4chan are posted under anonymous accounts. These posts are not limited to PII, as with other source categories. Many 4chan posts concerning high-profile employees are antisemitic in nature and usually end up being linked to an existing conspiracy theory. Something that is also unique to 4chan is the posts almost always target CEOs and additional executive employees.
Counter Adversary Operations has witnessed cyber threats turn into physical acts of targeting on 4chan. For example, our team observed a political discourse that devolved into the author posting their disagreement with an individual’s political views, resulting in a call to arms where the home of the individual was targeted by a picket line.
Social Media
Social media sites also included targeted notifications where actors directly targeted high-profile individuals. However, this medium is less prone to data leaking than public repositories. Social media posts include direct harassment of individuals — in most cases, the harassment revolves around a political discourse that led to hate speech from individual actors. Unlike chat mediums, which can be listed privately, social media sites reach a higher swath of application users, and author discourse appears to be a popular topic to gain notice.
The most common site on which Counter Adversary Operations observed this behavior was X (formerly Twitter). Counter Adversary Operations has aided customers in preparing documentation to take down X profiles that are attempting to impersonate high-profile employees’ accounts. These impersonating accounts used employees’ profile photos and names, making them more convincing.
The takedown process for social media accounts requires ample evidence of malicious behavior, not just the use of a name and photo. This can create a barrier for the affected user in getting the account taken down.
How CrowdStrike Counter Adversary Operations Can Help
CrowdStrike Falcon® Adversary Intelligence enables customers to monitor these sites and immediately alerts customers when activity against a high-profile employee is detected. And because CrowdStrike Counter Adversary Operations works with surface, deep and dark web data every day, the team knows which sites to focus on and which are less concerning. CrowdStrike offers an option to add an assigned Counter Adversary Operations analyst to help customers hunt for external threats to brands, employees and sensitive data, allowing their cyber professionals to devote their time to handling actionable data rather than hunting through a complex and ever-changing criminal ecosystem.
Additional Resources
Watch this short demo to see how Falcon Adversary Intelligence enables organizations to proactively uncover fraud, data breaches and phishing campaigns to protect their brand from online threats that target their organization.
Over the last year there has been a prevailing sentiment that while AI will not necessarily be replacing humans, humans who use AI will replace those that don’t.
This sentiment also applies to the next era of cybersecurity, which has been rapidly unfolding over the last year. Recent breakthroughs in generative AI hold enormous promise for modern defenders. Amid the dual pressures of accelerating attacks — down to just over two minutes, in some instances — and persistent skills shortages, generative AI has the potential to be not just an accelerator, but a veritable force-multiplier for teams of all sizes and maturity levels. We’ve seen these impressive gains firsthand working with early adopters of Charlotte AI (made generally available last month), with users reporting speed gains of up to 75% across supported workflows.
Making humans as effective and efficient as possible begins with giving them the best tools for the job. Today’s AI landscape presents organizations with a rapidly growing and often dizzying landscape of foundational models developed by the open-source community, startups and large enterprises. Each model is unique in its strengths and applications, varying in speed, accuracy, training data, computational intensiveness and in the underlying risks they pose to end-users. Invariably, selecting just one model, or one family of models, can force users to accept trade-offs across any one of these variables.
Security teams shouldn’t have to compromise on the tools they use to protect their organizations. In an ideal world, their tools should support the maximum levels of speed and accuracy required across the myriad workflows they oversee, without trade-offs on performance and risk — and without placing the burden on defenders to calculate computational complexity.
This is one of the foundational principles on which we’ve designed Charlotte AI. To optimize Charlotte AI’s performance and minimize the drawbacks of using individual models, we’ve architected Charlotte AI with a multi-AI system; one that partitions workflows into discrete sub-tasks and enables our data scientists to isolate, test and compare how effectively different models perform across tasks. This approach enables our experts to dynamically interchange the foundational models applied across workflows, ensuring end-users can interact with an ever-improving AI assistant fueled by the industry’s latest generative AI technologies.
Charlotte AI’s multi-AI design is singular across the cybersecurity landscape, applying cutting-edge system design from the front lines of genAI research to CrowdStrike’s unsurpassed data moat of award-winning threat intelligence, cross-domain platform telemetry and over a decade of expert-labeled security telemetry. In this blog, we shed light on how it comes together.
Under the Hood: From Question to Answer with AI Agents
Charlotte AI enables users to unleash the transformative power of generative AI across security workflows. With a simple question, users can activate Charlotte AI to answer questions about their environments, generate scripts or analyze emerging threat intelligence; all grounded in the high-fidelity telemetry of the Falcon platform. Charlotte AI’s natural processing capabilities lower the level of skill and experience needed to make quick, accurate security decisions, while enabling even seasoned analysts to unlock incremental speed gains across every stage of their workflows — from surfacing time-sensitive detections, to investigating incidents to taking action with Real Time Response.
Under the hood, Charlotte AI orchestrates over a dozen task-oriented “AI agents” to interpret a user’s question, plan the steps required to assemble a complete answer and structure the end result (Figure 1). Each AI agent is a subsystem consisting of a model and surrounding code that enables it to perform specific tasks and interact with other agents. One can think of each AI agent’s LLM (or other class of underlying model) as its “brain,” and each agent’s unique functionality (enabled by its surrounding code) as the skills that enable it to execute specific tasks.
We can think of these AI agents much like the team of doctors working in concert in an operating theater, each overseeing specialized tasks; from administering anesthesia to operating on acute areas of focus. Similarly, each AI agent has a specific responsibility and area of expertise. Much like an operation that requires a team of specialists to collaborate, Charlotte AI’s dynamic task force of AI agents work together to support a growing number of workflows; from summarizing threat intelligence, to writing queries in CrowdStrike Query Language (CQL), to assisting incident investigations.
At a high level, Charlotte AI activates AI agents to structure answers in the following sequence:
Step 1: Understand the Question: Charlotte AI first activates AI agents tasked with understanding the user’s conversation context and extracting entities referenced in the question — such as threat actors, vulnerabilities or host features.
Step 2: Route Subtasks to AI Agents: Charlotte AI then activates a router agent, which determines which AI agent or agents to assign the user’s request.
Step 3a: Scan for Answers: If a user asks a question that requires data from one or more API calls, the request is passed to a dedicated agent within Charlotte AI that ensures the information is retrieved and available for further processing.
Step 3b: Plan Responses for Questions: If the user’s question doesn’t map to one or more API calls — for example, when asking Charlotte AI to generate a CQL query — Charlotte AI’s router agent can activate a number of other AI agents fine-tuned to accomplish specific tasks.
Step 4: Validate the Plan and Retrieved Data: The runtime agent executes the API calls outlined by the prior AI agent. The output of this process is then reviewed by a validation agent, which determines whether the resulting information is complete or requires additional information. This AI agent may even issue a warning to the end user if the answer is incomplete.
Step 5: Generate an Answer: A final AI agent structures the response to the user’s question, taking into account intuitive ways of presenting information to the end user and generating a summary of information presented.
Figure 1. Charlotte AI uses task-specific AI agents to understand a user’s prompt and then assemble and validate the resulting answer.
Guardrails against LLM Overexposure
Systems that give users direct visibility to the output of LLMs (often referred to as “naked LLMs”) risk exposing users to inaccurate information when LLMs perform unexpectedly or hallucinate — a phenomenon where LLMs provide information that is not supported by, or even contradicts, source data. Inaccurate information can have devastating implications in security, ranging from impeded productivity, to a weakened security posture, to a major breach.
Charlotte AI’s multi-AI architecture plays a critical role in enabling a safe user experience, providing buffers that insulate end-users from the direct output of LLMs. First, by having the flexibility to apply diverse models across workflows, Charlotte AI enables CrowdStrike’s data science team to limit the ripple effects of unexpected changes in performance stemming from any one model. Another way Charlotte AI buffers users against direct LLM exposure is by using an agent tasked with validating answers before they are presented to end-users, verifying that answers are both consistent with the type of result the user is expecting and grounded in Falcon platform data.
Turbocharging Security Workflows: From Answer to Action
As large language models reach new levels of maturity and commoditization, security teams face a growing landscape of conversational AI assistants. Charlotte AI’s multi-AI architecture enables users to tap into the power of today’s best-of-breed foundational models and cutting-edge innovations across their workflows while minimizing the trade-offs of limiting their selection to any one model or model family. This architectural adaptability enables Charlotte AI to continuously elevate every analyst to new heights of efficiency, equipping them with the insight they need to make faster, more accurate decisions and reclaim a speed advantage against modern adversaries. For a deeper look at Charlotte AI’s architecture, download the white paper: “The Best AI for the Job: Inside Charlotte AI’s Multi-AI Architecture.”
Register for the Unstoppable Innovations Series: Hear how early adopters of Charlotte AI are turning hours of work into just minutes or even seconds. Register for the series.
CrowdStrike Falcon® Next-Gen SIEM enables companies to search, investigate and hunt down threats, including detection of advanced ransomware targeting VMware ESXi
Initial access to the ESXi infrastructure1 is typically gained through lateral movement using valid credentials
eCrime actors target and deploy ransomware in ESXi environments to increase the impact and scale of their attacks, which can be devastating for organizations
CrowdStrike Falcon Next-Gen SIEM, the definitive AI-native platform for detecting, investigating and hunting down threats, enables advanced detection of ransomware targeting VMware ESXi environments.
CrowdStrike has observed numerous eCrime actors exploiting ESXi infrastructure to encrypt virtual machine volumes from the hypervisor to deploy ransomware in organizations. Access to ESXi infrastructure typically takes place as part of lateral movement. For example, SCATTERED SPIDER often gains initial access to a Microsoft Entra ID identity via social engineering and then uses this identity to access internal information repositories such as SharePoint to search for ESXi related credentials.
CrowdStrike refers to this tactic to deploy ransomware as “Hypervisor Jackpotting,”2 as eCrime actors can use ESXi hosts to rapidly expand the scope of affected systems.
Falcon Next-Gen SIEM can ingest ESXi logs to reveal evidence of attacker activity, detect suspicious behavior related to use of encryption commands and tooling in near-real time, audit configuration changes and events indicative of early signs of pre-ransomware activity, and detect logins from malicious IP addresses to create and raise incidents for investigation.
Here, we provide an overview of how Falcon Next-Gen SIEM detects ransomware targeting ESXi environments.
Figure 1. Ransomware attack path (click to enlarge)
Start With Log Forwarding and Configuration
Forwarding ESXi logs to Falcon Next-Gen SIEM can help detect ransomware targeting ESXi environments. By ingesting and alerting on authentication and shell logs that ship with ESXi, we can find evidence of attackers gaining interactive shell access to the ESXi servers, running basic enumeration commands, shutting down virtual machines and using built-in tools like openssl to encrypt the virtual machine volumes.
We recommend using a syslog aggregation point, like the CrowdStrike® Falcon LogScale Collector, to forward logs to Falcon Next-Gen SIEM. Detailed instructions for doing this can be found in the CrowdStrike Tech Center.3
Once your log collector is set up, you can configure the ESXi infrastructure to forward the logs to your log collector. The default configuration for syslog for VMware ESXi 7.x and VMware vCenter 7.x is sufficient to get the correct logs to the unified, AI-native CrowdStrike Falcon® platform. You can find the instructions on how to enable remote syslog forwarding in VMware’s knowledge base.4
Detect the Use of Built-In Encryption Tools
Most of the activity prior to the encryption of critical virtual machine guest files could potentially be normal behavior like listing and shutting down virtual machines. These are important events to capture in an incident, but they are not indicative of an attack on their own.
To create a detection, we need to identify an event with a high enough confidence. The use of a tool like openssl with the encrypt option (enc) being run from the command line on critical guest virtual machine files is a high-confidence event that indicates something malicious is happening in our environment.
Falcon Next-Gen SIEM will detect this activity in near real time and create an incident for you. It will also gather surrounding activity that has happened on that host and include it in the incident.
Figure 2. Ransomware detected on a server running ESXi (click to enlarge)
In this detection, we see the use of openssl to encrypt sensitive files, the SSH connection, some virtual machine enumeration and the virtual machines being shut down before encryption.
Gain Insights Into Early Ransomware Signs
Detecting an in-progress attack within your environment is critical, but it is always better to get insights into a pending attack before it starts. We can look for configuration changes to an environment, especially those that don’t comply with best practices or that degrade the security posture of the environment.
One easily detectable signal would be if a user enables SSH on one of your servers running ESXi. In a best-case scenario, this is an auditable event that the security team can confirm is legitimate behavior. In a worst-case scenario, it is the first tip that someone has compromised your ESXi infrastructure. We can easily surface this activity by creating a scheduled search looking for the specific indicator. Running it on a short interval with an overlapping time period will ensure we get timely notifications without missing events.
Figure 3. Extended detection and response (XDR) scheduled search configuration in the Falcon platform (click to enlarge)
Figure 4. ESXi SSH-enabled detection (click to enlarge)
Reveal Unauthorized Logins from Malicious IP Addresses
Another indicator of a pending attack is the detection of logins to the ESXi management console that come from known malicious IP addresses where the console is purposely or accidentally exposed to the internet. Falcon Next-Gen SIEM leverages CrowdStrike’s integrated threat intelligence to identify any known indicators of compromise (IOCs) in critical data. By combining this with the ESXi login entries sent to the Falcon platform, we can detect the login attempts from known malicious sources.
Figure 5. Detection of a login from a malicious IP (click to enlarge)
By leveraging Falcon Next-Gen SIEM, you can provide your organization with multiple layers of detections, get unparalleled visbility into your ESXi infrastructure and get ahead of adversaries. Falcon Next-Gen SIEM leaves adversaries targeting ESXi environments with nowhere to hide, detecting suspicious behavior as early as possible, preventing attacks and stopping breaches.
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