tl;dr Run our new tool by adding -javaagent:log4j-jndi-be-gone-1.0.0-standalone.jar to all of your JVM Java stuff to stop log4j from loading classes remotely over LDAP. This will prevent malicious inputs from triggering the “Log4Shell” vulnerability and gaining remote code execution on your systems.
Hello internet, it’s been a rough week. As you have probably learned, basically every Java app in the world uses a library called “log4j” to handle logging, and that any string passed into those logging calls will evaluate magic ${jndi:ldap://...} sequences to remotely load (malicious) Java class files over the internet (CVE-2021-44228, “Log4Shell”). Right now, while the SREs are trying to apply the not-quite-a-fix official fix and/or implement egress filtering without knocking their employers off the internet, most people are either blaming log4j for even having this JNDI stuff in the first place and/or blaming the issue on a lack of support for the project that would have helped to prevent such a dangerous behavior from being so accessible. In reality, the JNDI stuff is regrettably more of an “enterprise” feature than one that developers would just randomly put in if left to their own devices. Enterprise Java is all about antipatterns that invoke code in roundabout ways to the point of obfuscation, and supporting ever more dynamic ways to integrate weird protocols like RMI to load and invoke remote code dynamically in weird ways. Even the log4j format “Interpolator” wraps a bunch of handlers, including the JNDI handler, in reflection wrappers. So, if anything, more “(financial) support” for the project would probably just lead to more of these kinds of things happening as demand for one-off formatters for new systems grows among larger users. Welcome to Enterprise Java Land, where they’ve already added log4j variable expansion for Docker and Kubernetes. Alas, the real problem is that log4j 2.x (the version basically everyone uses) is designed in such a way that all string arguments after the main format string for the logging call are also treated as format strings. Basically all log4j calls are equivalent to if the following C:
printf("%s\n", "clobbering some bytes %n");
were implemented as the very unsafe code below:
char *buf;
asprintf(&buf, "%s\n", "clobbering some bytes %n");
printf(buf);
Basically, log4j never got the memo about format string vulnerabilities and now it’s (probably) too late. It was only a matter of time until someone realized they exposed a magic format string directive that led to code execution (and even without the classloading part, it is still a means of leaking expanded variables out through other JNDI-compatible services, like DNS), and I think it may only be a matter of time until another dangerous format string handler gets introduced into log4j. Meanwhile, even without JNDI, if someone has access to your log4j output (wherever you send it), and can cause their input to end up in a log4j call (pretty much a given based on the current havoc playing out) they can systematically dump all sorts of process and system state into it including sensitive application secrets and credentials. Had log4j not implemented their formatting this way, then the JNDI issue would only impact applications that concatenated user input into the format string (a non-zero amount, but much less than 100%).
The “Fixes”
The main fix is to update to the just released log4j 2.16.0. Prior to that, the official mitigation from the log4j maintainers was:
“In releases >=2.10, this behavior can be mitigated by setting either the system property log4j2.formatMsgNoLookups or the environment variable LOG4J_FORMAT_MSG_NO_LOOKUPS to true. For releases from 2.0-beta9 to 2.10.0, the mitigation is to remove the JndiLookup class from the classpath: zip -q -d log4j-core-*.jar org/apache/logging/log4j/core/lookup/JndiLookup.class.”
So to be clear, the fix given for older versions of log4j (2.0-beta9 until 2.10.0) is to find and purge the JNDI handling class from all of your JARs, which are probably all-in-one fat JARs because no one uses classpaths anymore, all to prevent it from being loaded.
A tool to mitigate Log4Shell by disabling log4j JNDI
To try to make the situation a little bit more manageable in the meantime, we are releasing log4j-jndi-be-gone, a dead simple Java agent that disables the log4j JNDI handler outright. log4j-jndi-be-gone uses the Byte Buddy bytecode manipulation library to modify the at-issue log4j class’s method code and short circuit the JNDI interpolation handler. It works by effectively hooking the at-issue JndiLookup class’ lookup() method that Log4Shell exploits to load remote code, and forces it to stop early without actually loading the Log4Shell payload URL. It also supports Java 6 through 17, covering older versions of log4j that support Java 6 (2.0-2.3) and 7 (2.4-2.12.1), and works on read-only filesystems (once installed or mounted) such as in read-only containers.
The benefit of this Java agent is that a single command line flag can negate the vulnerability regardless of which version of log4j is in use, so long as it isn’t obfuscated (e.g. with proguard), in which case you may not be in a good position to update it anyway. log4j-jndi-be-gone is not a replacement for the -Dlog4j2.formatMsgNoLookups=true system property in supported versions, but helps to deal with those older versions that don’t support it.
Using it is pretty simple, just add -javaagent:path/to/log4j-jndi-be-gone-1.0.0-standalone.jar to your Java commands. In addition to disabling the JNDI handling, it also prints a message indicating that a log4j JNDI attempt was made with a simple sanitization applied to the URL string to prevent it from becoming a propagation vector. It also “resolves” any JNDI format strings to "(log4j jndi disabled)" making the attempts a bit more grep-able.
log4j-jndi-be-gone is available from our GitHub repo, https://github.com/nccgroup/log4j-jndi-be-gone. You can grab a pre-compiled log4j-jndi-be-gone agent JAR from the releases page, or build one yourself with ./gradlew, assuming you have a recent version of Java installed.
NCC Group, as well as many other researchers noticed a rise in Android malware last year, especillay Android banking malware. Within the Treat Intelligence team of NCC Group we’re looking closely to several of these malware families to provide valuable information to our customers about these threats. Next to the more popular Android banking malware NCC Group’s Threat Intelligence team also watches new trends and new families that arise and could be potential threats to our customers.
One of these ‘newer’ families is an Android banking malware called SharkBot. During our research NCC Group noticed that this malware was disturbed via the official Google play store. After discovery NCC Group immediately notified Google and decided to share our knowledge via this blog post.
Summary
SharkBot is an Android banking malware found at the end of October 2021 by the Cleafy Threat Intelligence Team. At the moment of writing the SharkBot malware doesn’t seem to have any relations with other Android banking malware like Flubot, Cerberus/Alien, Anatsa/Teabot, Oscorp, etc.
The Cleafy blogpost stated that the main goal of SharkBot is to initiate money transfers (from compromised devices) via Automatic Transfer Systems (ATS). As far as we observed, this technique is an advanced attack technique which isn’t used regularly within Android malware. It enables adversaries to auto-fill fields in legitimate mobile banking apps and initate money transfers, where other Android banking malware, like Anatsa/Teabot or Oscorp, require a live operator to insert and authorize money transfers. This technique also allows adversaries to scale up their operations with minimum effort.
The ATS features allow the malware to receive a list of events to be simulated, and them will be simulated in order to do the money transfers. Since this features can be used to simulate touches/clicks and button presses, it can be used to not only automatically transfer money but also install other malicious applications or components. This is the case of the SharkBot version that we found in the Google Play Store, which seems to be a reduced version of SharkBot with the minimum required features, such as ATS, to install a full version of the malware some time after the initial install.
Because of the fact of being distributed via the Google Play Store as a fake Antivirus, we found that they have to include the usage of infected devices in order to spread the malicious app. SharkBot achieves this by abusing the ‘Direct Reply‘ Android feature. This feature is used to automatically send reply notification with a message to download the fake Antivirus app. This spread strategy abusing the Direct Reply feature has been seen recently in another banking malware called Flubot, discovered by ThreatFabric.
What is interesting and different from the other families is that SharkBot likely uses ATS to also bypass multi-factor authentication mechanisms, including behavioral detection like bio-metrics, while at the same time it also includes more classic features to steal user’s credentials.
Money and Credential Stealing features
SharkBot implements the four main strategies to steal banking credentials in Android:
Injections (overlay attack): SharkBot can steal credentials by showing a WebView with a fake log in website (phishing) as soon as it detects the official banking app has been opened.
Keylogging: Sharkbot can steal credentials by logging accessibility events (related to text fields changes and buttons clicked) and sending these logs to the command and control server (C2).
SMS intercept: Sharkbot has the ability to intercept/hide SMS messages.
Remote control/ATS: Sharkbot has the ability to obtain full remote control of an Android device (via Accessibility Services).
For most of these features, SharkBot needs the victim to enable the Accessibility Permissions & Services. These permissions allows Android banking malware to intercept all the accessibility events produced by the interaction of the user with the User Interface, including button presses, touches, TextField changes (useful for the keylogging features), etc. The intercepted accessibility events also allow to detect the foreground application, so banking malware also use these permissions to detect when a targeted app is open, in order to show the web injections to steal user’s credentials.
Delivery
Sharkbot is distributed via the Google Play Store, but also using something relatively new in the Android malware: ‘Direct reply‘ feature for notifications. With this feature, the C2 can provide as message to the malware which will be used to automatically reply the incoming notifications received in the infected device. This has been recently introduced by Flubot to distribute the malware using the infected devices, but it seems SharkBot threat actors have also included this feature in recent versions.
In the following image we can see the code of SharkBot used to intercept new notifications and automatically reply them with the received message from the C2.
In the following picture we can see the ‘autoReply’ command received by our infected test device, which contains a shortten Bit.ly link which redirects to the Google Play Store sample.
We detected the SharkBot reduced version published in the Google Play on 28th February, but the last update was on 10th February, so the app has been published for some time now. This reduced version uses a very similar protocol to communicate with the C2 (RC4 to encrypt the payload and Public RSA key used to encrypt the RC4 key, so the C2 server can decrypt the request and encrypt the response using the same key). This SharkBot version, which we can call SharkBotDropper is mainly used to download a fully featured SharkBot from the C2 server, which will be installed by using the Automatic Transfer System (ATS) (simulating click and touches with the Accessibility permissions).
This malicious dropper is published in the Google Play Store as a fake Antivirus, which really has two main goals (and commands to receive from C2):
Spread the malware using ‘Auto reply’ feature: It can receive an ‘autoReply’ command with the message that should be used to automatically reply any notification received in the infected device. During our research, it has been spreading the same Google Play dropper via a shorten Bit.ly URL.
Dropper+ATS: The ATS features are used to install the downloaded SharkBot sample obtained from the C2. In the following image we can see the decrypted response received from the C2, in which the dropper receives the command ‘b‘ to download the full SharkBot sample from the provided URL and the ATS events to simulate in order to get the malware installed.
With this command, the app installed from the Google Play Store is able to install and enable Accessibility Permissions for the fully featured SharkBot sample it downloaded. It will be used to finally perform the ATS fraud to steal money and credentials from the victims.
The fake Antivirus app, the SharkBotDropper, published in the Google Play Store has more than 1,000 downloads, and some fake comments like ‘It works good’, but also other comments from victims that realized that this app does some weird things.
Technical analysis
Protocol & C2
The protocol used to communicate with the C2 servers is an HTTP based protocol. The HTTP requests are made in plain, since it doesn’t use HTTPs. Even so, the actual payload with the information sent and received is encrypted using RC4. The RC4 key used to encrypt the information is randomly generated for each request, and encrypted using the RSA Public Key hardcoded in each sample. That way, the C2 can decrypt the encrypted key (rkey field in the HTTP POST request) and finally decrypt the sent payload (rdata field in the HTTP POST request).
If we take a look at the decrypted payload, we can see how SharkBot is simply using JSON to send different information about the infected device and receive the commands to be executed from the C2. In the following image we can see the decrypted RC4 payload which has been sent from an infected device.
Two important fields sent in the requests are:
ownerID
botnetID
Those parameters are hardcoded and have the same value in the analyzed samples. We think those values can be used in the future to identify different buyers of this malware, which based on our investigation is not being sold in underground forums yet.
Domain Generation Algorithm
SharkBot includes one or two domains/URLs which should be registered and working, but in case the hardcoded C2 servers were taken down, it also includes a Domain Generation Algorithm (DGA) to be able to communicate with a new C2 server in the future.
The DGA uses the current date and a specific suffix string (‘pojBI9LHGFdfgegjjsJ99hvVGHVOjhksdf’) to finally encode that in base64 and get the first 19 characters. Then, it append different TLDs to generate the final candidate domain.
The date elements used are:
Week of the year (v1.get(3) in the code)
Year (v1.get(1) in the code)
It uses the ‘+’ operator, but since the week of the year and the year are Integers, they are added instead of appended, so for example: for the second week of 2022, the generated string to be base64 encoded is: 2 + 2022 + “pojBI9LHGFdfgegjjsJ99hvVGHVOjhksdf” = 2024 + “pojBI9LHGFdfgegjjsJ99hvVGHVOjhksdf” = “2024pojBI9LHGFdfgegjjsJ99hvVGHVOjhksdf”.
In previous versions of SharkBot (from November-December of 2021), it only used the current week of the year to generate the domain. Including the year to the generation algorithm seems to be an update for a better support of the new year 2022.
Commands
SharkBot can receive different commands from the C2 server in order to execute different actions in the infected device such as sending text messages, download files, show injections, etc. The list of commands it can receive and execute is as follows:
smsSend: used to send a text message to the specified phone number by the TAs
updateLib: used to request the malware downloads a new JAR file from the specified URL, which should contain an updated version of the malware
updateSQL: used to send the SQL query to be executed in the SQLite database which Sharkbot uses to save the configuration of the malware (injections, etc.)
stopAll: used to reset/stop the ATS feature, stopping the in progress automation.
updateConfig: used to send an updated config to the malware.
uninstallApp: used to uninstall the specified app from the infected device
changeSmsAdmin: used to change the SMS manager app
getDoze: used to check if the permissions to ignore battery optimization are enabled, and show the Android settings to disable them if they aren’t
sendInject: used to show an overlay to steal user’s credentials
getNotify: used to show the Notification Listener settings if they are not enabled for the malware. With this permissions enabled, Sharkbot will be able to intercept notifications and send them to the C2
APP_STOP_VIEW: used to close the specified app, so every time the user tries to open that app, the Accessibility Service with close it
downloadFile: used to download one file from the specified URL
updateTimeKnock: used to update the last request timestamp for the bot
localATS: used to enable ATS attacks. It includes a JSON array with the different events/actions it should simulate to perform ATS (button clicks, etc.)
Automatic Transfer System
One of the distinctive parts of SharkBot is that it uses a technique known as Automatic Transfer System (ATS). ATS is a relatively new technique used by banking malware for Android.
To summarize ATS can be compared with webinject, only serving a different purpose. Rather then gathering credentials for use/scale it uses the credentials for automatically initiating wire transfers on the endpoint itself (so without needing to log in and bypassing 2FA or other anti-fraud measures). However, it is very individually tailored and request quite some maintenance for each bank, amount, money mules etc. This is probably one of the reasons ATS isn’t that popular amongst (Android) banking malware.
How does it work?
Once a target logs into their banking app the malware would receive an array of events (clicks/touches, button presses, gestures, etc.) to be simulated in an specific order. Those events are used to simulate the interaction of the victim with the banking app to make money transfers, as if the user were doing the money transfer by himself.
This way, the money transfer is made from the device of the victim by simulating different events, which make much more difficult to detect the fraud by fraud detection systems.
IoCs
Sample Hashes:
a56dacc093823dc1d266d68ddfba04b2265e613dcc4b69f350873b485b9e1f1c (Google Play SharkBotDropper)
A recently uncovered malware sample dubbed ‘Saitama’ was uncovered by security firm Malwarebytes in a weaponized document, possibly targeted towards the Jordan government. This Saitama implant uses DNS as its sole Command and Control channel and utilizes long sleep times and (sub)domain randomization to evade detection. As no server-side implementation was available for this implant, our detection engineers had very little to go on to verify whether their detection would trigger on such a communication channel. This blog documents the development of a Saitama server-side implementation, as well as several approaches taken by Fox-IT / NCC Group’s Research and Intelligence Fusion Team (RIFT) to be able to detect DNS-tunnelling implants such as Saitama. The developed implementation as well as recordings of the implant are shared on the Fox-IT GitHub.
Introduction
For its Managed Detection and Response (MDR) offering, Fox-IT is continuously building and testing detection coverage for the latest threats. Such detection efforts vary across all tactics, techniques, and procedures (TTP’s) of adversaries, an important one being Command and Control (C2). Detection of Command and Control involves catching attackers based on the communication between the implants on victim machines and the adversary infrastructure.
In May 2022, security firm Malwarebytes published a two1-part2 blog about a malware sample that utilizes DNS as its sole channel for C2 communication. This sample, dubbed ‘Saitama’, sets up a C2 channel that tries to be stealthy using randomization and long sleep times. These features make the traffic difficult to detect even though the implant does not use DNS-over-HTTPS (DoH) to encrypt its DNS queries.
Although DNS tunnelling remains a relatively rare technique for C2 communication, it should not be ignored completely. While focusing on Indicators of Compromise (IOC’s) can be useful for retroactive hunting, robust detection in real-time is preferable. To assess and tune existing coverage, a more detailed understanding of the inner workings of the implant is required. This blog will use the Saitama implant to illustrate how malicious DNS tunnels can be set-up in a variety of ways, and how this variety affects the detection engineering process.
To assist defensive researchers, this blogpost comes with the publication of a server-side implementation of Saitama on the Fox-IT GitHub. This can be used to control the implant in a lab environment. Moreover, ‘on the wire’ recordings of the implant that were generated using said implementation are also shared as PCAP and Zeek logs. This blog also details multiple approaches towards detecting the implant’s traffic, using a Suricata signature and behavioural detection.
Reconstructing the Saitama traffic
The behaviour of the Saitama implant from the perspective of the victim machine has already been documented elsewhere3. However, to generate a full recording of the implant’s behaviour, a C2 server is necessary that properly controls and instructs the implant. Of course, the source code of the C2 server used by the actual developer of the implant is not available.
If you aim to detect the malware in real-time, detection efforts should focus on the way traffic is generated by the implant, rather than the specific domains that the traffic is sent to. We strongly believe in the “PCAP or it didn’t happen” philosophy. Thus, instead of relying on assumptions while building detection, we built the server-side component of Saitama to be able to generate a PCAP.
The server-side implementation of Saitama can be found on the Fox-IT GitHub page. Be aware that this implementation is a Proof-of-Concept. We do not intend on fully weaponizing the implant “for the greater good”, and have thus provided resources to the point where we believe detection engineers and blue teamers have everything they need to assess their defences against the techniques used by Saitama.
Let’s do the twist
The usage of DNS as the channel for C2 communication has a few upsides and quite some major downsides from an attacker’s perspective. While it is true that in many environments DNS is relatively unrestricted, the protocol itself is not designed to transfer large volumes of data. Moreover, the caching of DNS queries forces the implant to make sure that every DNS query sent is unique, to guarantee the DNS query reaches the C2 server.
For this, the Saitama implant relies on continuously shuffling the character set used to construct DNS queries. While this shuffle makes it near-impossible for two consecutive DNS queries to be the same, it does require the server and client to be perfectly in sync for them to both shuffle their character sets in the same way.
On startup, the Saitama implant generates a random number between 0 and 46655 and assigns this to a counter variable. Using a shared secret key (“haruto” for the variant discussed here) and a shared initial character set (“razupgnv2w01eos4t38h7yqidxmkljc6b9f5”), the client encodes this counter and sends it over DNS to the C2 server. This counter is then used as the seed for a Pseudo-Random Number Generator (PRNG). Saitama uses the Mersenne Twister to generate a pseudo-random number upon every ‘twist’.
To encode this counter, the implant relies on a function named ‘_IntToString’. This function receives an integer and a ‘base string’, which for the first DNS query is the same initial, shared character set as identified in the previous paragraph. Until the input number is equal or lower than zero, the function uses the input number to choose a character from the base string and prepends that to the variable ‘str’ which will be returned as the function output. At the end of each loop iteration, the input number is divided by the length of the baseString parameter, thus bringing the value down.
Function used by Saitama client to convert an integer into an encoded string.
To determine the initial seed, the server has to ‘invert’ this function to convert the encoded string back into its original number. However, information gets lost during the client-side conversion because this conversion rounds down without any decimals. The server tries to invert this conversion by using simple multiplication. Therefore, the server might calculate a number that does not equal the seed sent by the client and thus must verify whether the inversion function calculated the correct seed. If this is not the case, the server literately tries higher numbers until the correct seed is found.
Once this hurdle is taken, the rest of the server-side implementation is trivial. The client appends its current counter value to every DNS query sent to the server. This counter is used as the seed for the PRNG. This PRNG is used to shuffle the initial character set into a new one, which is then used to encode the data that the client sends to the server. Thus, when both server and client use the same seed (the counter variable) to generate random numbers for the shuffling of the character set, they both arrive at the exact same character set. This allows the server and implant to communicate in the same ‘language’. The server then simply substitutes the characters from the shuffled alphabet back into the ‘base’ alphabet to derive what data was sent by the client.
Server-side implementation to arrive at the same shuffled alphabet as the client.
Twist, Sleep, Send, Repeat
Many C2 frameworks allow attackers to manually set the minimum and maximum sleep times for their implants. While low sleep times allow attackers to more quickly execute commands and receive outputs, higher sleep times generate less noise in the victim network. Detection often relies on thresholds, where suspicious behaviour will only trigger an alert when it happens multiple times in a certain period.
The Saitama implant uses hardcoded sleep values. During active communication (such as when it returns command output back to the server), the minimum sleep time is 40 seconds while the maximum sleep time is 80 seconds. On every DNS query sent, the client will pick a random value between 40 and 80 seconds. Moreover, the DNS query is not sent to the same domain every time but is distributed across three domains. On every request, one of these domains is randomly chosen. The implant has no functionality to alter these sleep times at runtime, nor does it possess an option to ‘skip’ the sleeping step altogether.
Sleep configuration of the implant. The integers represent sleep times in milliseconds.
These sleep times and distribution of communication hinder detection efforts, as they allow the implant to further ‘blend in’ with legitimate network traffic. While the traffic itself appears anything but benign to the trained eye, the sleep times and distribution bury the ‘needle’ that is this implant’s traffic very deep in the haystack of the overall network traffic.
For attackers, choosing values for the sleep time is a balancing act between keeping the implant stealthy while keeping it usable. Considering Saitama’s sleep times and keeping in mind that every individual DNS query only transmits 15 bytes of output data, the usability of the implant is quite low. Although the implant can compress its output using zlib deflation, communication between server and client still takes a lot of time. For example, the standard output of the ‘whoami /priv’ command, which once zlib deflated is 663 bytes, takes more than an hour to transmit from victim machine to a C2 server.
Transmission between server implementation and the implant
Transmission between server implementation and the implant
The implant does contain a set of hardcoded commands that can be triggered using only one command code, rather than sending the command in its entirety from the server to the client. However, there is no way of knowing whether these hardcoded commands are even used by attackers or are left in the implant as a means of misdirection to hinder attribution. Moreover, the output from these hardcoded commands still has to be sent back to the C2 server, with the same delays as any other sent command.
Detection
Detecting DNS tunnelling has been the subject of research for a long time, as this technique can be implemented in a multitude of different ways. In addition, the complications of the communication channel force attackers to make more noise, as they must send a lot of data over a channel that is not designed for that purpose. While ‘idle’ implants can be hard to detect due to little communication occurring over the wire, any DNS implant will have to make more noise once it starts receiving commands and sending command outputs. These communication ‘bursts’ is where DNS tunnelling can most reliably be detected. In this section we give examples of how to detect Saitama and a few well-known tools used by actual adversaries.
Signature-based
Where possible we aim to write signature-based detection, as this provides a solid base and quick tool attribution. The randomization used by the Saitama implant as outlined previously makes signature-based detection challenging in this case, but not impossible. When actively communicating command output, the Saitama implant generates a high number of randomized DNS queries. This randomization does follow a specific pattern that we believe can be generalized in the following Suricata rule:
Only trigger if there are more than 50 queries in the last 3600 seconds. And only trigger once per 3600 seconds.
Table one: Content matches for Suricata IDS rule
The choice for 28-31 characters is based on the structure of DNS queries containing output. First, one byte is dedicated to the ‘send and receive’ command code. Then follows the encoded ID of the implant, which can take between 1 and 3 bytes. Then, 2 bytes are dedicated to the byte index of the output data. Followed by 20 bytes of base-32 encoded output. Lastly the current value for the ‘counter’ variable will be sent. As this number can range between 0 and 46656, this takes between 1 and 5 bytes.
Behaviour-based
The randomization that makes it difficult to create signatures is also to the defender’s advantage: most benign DNS queries are far from random. As seen in the table below, each hack tool outlined has at least one subdomain that has an encrypted or encoded part. While initially one might opt for measuring entropy to approximate randomness, said technique is less reliable when the input string is short. The usage of N-grams, an approach we have previously written about4, is better suited.
Api.abcdefgh0.123456.dns.example.com or post. 4c6f72656d20697073756d20646f6c6f722073697420616d65742073756e74207175697320756c6c616d636f20616420646f6c6f7220616c69717569702073756e7420636f6d6d6f646f20656975736d6f642070726.c123456.dns.example.com
Table two: Example DNS queries for various toolings that support DNS tunnelling
Unfortunately, the detection of randomness in DNS queries is by itself not a solid enough indicator to detect DNS tunnels without yielding large numbers of false positives. However, a second limitation of DNS tunnelling is that a DNS query can only carry a limited number of bytes. To be an effective C2 channel an attacker needs to be able to send multiple commands and receive corresponding output, resulting in (slow) bursts of multiple queries.
This is where the second step for behaviour-based detection comes in: plainly counting the number of unique queries that have been classified as ‘randomized’. The specifics of these bursts differ slightly between tools, but in general, there is no or little idle time between two queries. Saitama is an exception in this case. It uses a uniformly distributed sleep between 40 and 80 seconds between two queries, meaning that on average there is a one-minute delay. This expected sleep of 60 seconds is an intuitive start to determine the threshold. If we aggregate over an hour, we expect 60 queries distributed over 3 domains. However, this is the mean value and in 50% of the cases there are less than 60 queries in an hour.
To be sure we detect this, regardless of random sleeps, we can use the fact that the sum of uniform random observations approximates a normal distribution. With this distribution we can calculate the number of queries that result in an acceptable probability. Looking at the distribution, that would be 53. We use 50 in our signature and other rules to incorporate possible packet loss and other unexpected factors. Note that this number varies between tools and is therefore not a set-in-stone threshold. Different thresholds for different tools may be used to balance False Positives and False Negatives.
In summary, combining detection for random-appearing DNS queries with a minimum threshold of random-like DNS queries per hour, can be a useful approach for the detection of DNS tunnelling. We found in our testing that there can still be some false positives, for example caused by antivirus solutions. Therefore, a last step is creating a small allow list for domains that have been verified to be benign.
While more sophisticated detection methods may be available, we believe this method is still powerful (at least powerful enough to catch this malware) and more importantly, easy to use on different platforms such as Network Sensors or SIEMs and on diverse types of logs.
Conclusion
When new malware arises, it is paramount to verify existing detection efforts to ensure they properly trigger on the newly encountered threat. While Indicators of Compromise can be used to retroactively hunt for possible infections, we prefer the detection of threats in (near-)real-time. This blog has outlined how we developed a server-side implementation of the implant to create a proper recording of the implant’s behaviour. This can subsequently be used for detection engineering purposes.
Strong randomization, such as observed in the Saitama implant, significantly hinders signature-based detection. We detect the threat by detecting its evasive method, in this case randomization. Legitimate DNS traffic rarely consists of random-appearing subdomains, and to see this occurring in large bursts to previously unseen domains is even more unlikely to be benign.
Resources
With the sharing of the server-side implementation and recordings of Saitama traffic, we hope that others can test their defensive solutions.
The server-side implementation of Saitama can be found on the Fox-IT GitHub.
This repository also contains an example PCAP & Zeek logs of traffic generated by the Saitama implant. The repository also features a replay script that can be used to parse executed commands & command output out of a PCAP.
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