Author: Nikolaos Pantazopoulos
Co-author: Stefano Antenucci (@Antelox)
And in close collaboration with NCC’s RIFT.

1. Introduction

Publicly discovered in late April 2020, the Team9 malware family (also known as ‘Bazar [1]’) appears to be a new malware being developed by the group behind Trickbot. Even though the development of the malware appears to be recent, the developers have already developed two components with rich functionality. The purpose of this blog post is to describe the functionality of the two components, the loader and the backdoor.

About the Research and Intelligence Fusion Team (RIFT): RIFT leverages our strategic analysis, data science, and threat hunting capabilities to create actionable threat intelligence, ranging from IOCs and detection rules to strategic reports on tomorrow’s threat landscape. Cyber security is an arms race where both attackers and defenders continually update and improve their tools and ways of working. To ensure that our managed services remain effective against the latest threats, NCC Group operates a Global Fusion Center with Fox-IT at its core. This multidisciplinary team converts our leading cyber threat intelligence into powerful detection strategies.

2. Early variant of Team9 loader

We assess that this is an earlier variant of the Team9 loader (35B3FE2331A4A7D83D203E75ECE5189B7D6D06AF4ABAC8906348C0720B6278A4) because of its simplicity and the compilation timestamp. The other variant was compiled more recently and has additional functionality. It should be noted that in very early versions of the loader binaries (2342C736572AB7448EF8DA2540CDBF0BAE72625E41DAB8FFF58866413854CA5C), the developers were using the Windows BITS functionality in order to download the backdoor. However, we believe that this functionality has been dropped.

Before proceeding to the technical analysis part, it is worth mentioning that the strings are not encrypted. Similarly, the majority of the Windows API functions are not loaded dynamically.

When the loader starts its execution, it checks if another instance of itself has infected the host already by attempting to read the value ‘BackUp Mgr’ in the ‘Run’ registry key ‘Software\Microsoft\Windows\CurrentVersion\Run’ (Figure 1). If it exists, it validates if the current loaders file path is the same as the one that has already been set in the registry value’s data (BackUp Mgr). Assuming that all of the above checks were successful, the loader proceeds to its core functionality.

Figure 1 – Loader verifies if it has already infect the host

However, if any of the above checks do not meet the requirements then the loader does one of the following actions:

1. Copy itself to the %APPDATA%\Microsoft folder, add this file path in the registry ‘Run’ key under the value ‘BackUp Mgr’ and then execute the loader from the copied location.
2. If the loader cannot access the %APPDATA% location or if the loader is running from this location already, then it adds the current file path in the ‘Run’ registry key under the value ‘BackUp Mgr’ and executes the loader again from this location.

When the persistence operation finishes, the loader deletes itself by writing a batch file in the Windows temporary folder with the file name prefix ‘tmp’ followed by random digits. The batch file content:

@echo off
set Module=%1
:Repeat
del %Module%
if exist %Module% goto Repeat
del %0

Next, the loader fingerprints the Windows architecture. This is a crucial step because the loader needs to know what version of the backdoor to download (32-bit or 64-bit). Once the Windows architecture has been identified, the loader carries out the download.

The core functionality of the loader is to download the Team9 backdoor component. The loader contains two ‘.bazar’ top-level domains which point to the Team9 backdoor. Each domain hosts two versions of the Team9 backdoor on different URIs, one for each Windows architecture (32-bit and 64-bit), the use of two domains is highly likely to be a backup method.

Any received files from the command and control server are sent in an encrypted format. In order to decrypt a file, the loader uses a bitwise XOR decryption with the key being based on the infected host’s system time (Year/Month/Day) (Figure 2).

Figure 2 – Generate XOR key based on infected host’s time

As a last step, the loader verifies that the executable file was decrypted successfully by validating the PE headers. If the Windows architecture is 32-bit, the loader injects the received executable file into ‘calc.exe’ (Windows calculator) using the ‘Process Hollowing’ technique. Otherwise, it writes the executable file to disk and executes it.

The following tables summarises the identified bazar domains and their URIs found in the early variants of the loader.

URI	Description
/api/v108	Possibly downloads the 64-bit version of the Team9 backdoor
/api/v107	Possibly downloads the 32-bit version of the Team9 backdoor
/api/v5	Possibly downloads an updated 32-bit version of the Team9 loader
/api/v6	Possibly downloads an updated 64-bit version of the Team9 loader
/api/v7	Possibly downloads the 32-bit version of the Team9 backdoor
/api/v8	Possibly downloads the 64-bit version of the Team9 backdoor

Table 1 – Bazar URIs found in early variants of the loader

The table below (table 2) summarises the identified domains found in the early variants of the loader.

Bazar domains

bestgame[.]bazar

forgame[.]bazar

zirabuo[.]bazar

tallcareful[.]bazar

coastdeny[.]bazar

Table 2 – Bazar domains found in early variants of the loader

Lastly, another interesting observation is the log functionality in the binary file that reveals the following project file path:

d:\\development\\team9\\team9_restart_loader\\team9_restart_loader

3. Latest variant of Team9 loader

In this section, we describe the functionality of a second loader that we believe to be the latest variant of the aforementioned Team9 loader. This assessment is based on three factors:

1. Similar URIs in the backdoor requests
2. Similar payload decryption technique
3. Similar code blocks

Unlike its previous version, the strings are encrypted and the majority of Windows API functions are loaded dynamically by using the Windows API hashing technique.

Once executed, the loader uses a timer in order to delay the execution. This is likely used as an anti-sandbox method. After the delayed time has passed, the loader starts executing its core functionality.

Before the malware starts interacting with the command and control server, it ensures that any other related files produced by a previous instance of the loader will not cause any issues. As a result the loader appends the string ‘_lyrt’ to its current file path and deletes any file with this name. Next, the loader searches for the parameter ‘-p’ in the command line and if found, it deletes the scheduled task ‘StartDT’. The loader creates this scheduled task later for persistence during execution. The loader also attempts to execute hijacked shortcut files, which will eventually execute an instance of Team9 loader. This functionality is described later.

The loader performs a last check to ensure that the operating systems keyboard and language settings are not set to Russian and creates a mutex with a hardcoded name ‘ld_201127’. The latter is to avoid double execution of its own instance.

As mentioned previously, the majority of Windows API functions are loaded dynamically. However, in an attempt to bypass any API hooks set by security products, the loader manually loads ‘ntdll’ from disk, reads the opcodes from each API function and compares them with the ones in memory (Figure 3). If the opcodes are different, the loader assumes a hook has been applied and removes it. This applies only to 64-bit samples reviewed to date.

Figure 3 – Scan for hooks in Windows API functions

The next stage downloads from the command and control server either the backdoor or an updated version of the loader. It is interesting to note that there are minor differences in the loader’s execution based on the identified Windows architecture and if the ‘-p’ parameter has been passed into the command line.

Assuming that the ‘-p’ parameter has not been passed into the command line, the loader has two loops. One for 32-bit and the other for 64-bit, which download an updated version of the loader. The main difference between the two loops is that in case of a Windows x64 infection, there is no check of the loader’s version.

The download process is the same with the previous variant, the loader resolves the command and control server IP address using a hardcoded list of DNS servers and then downloads the corresponding file. An interesting addition, in the latest samples, is the use of an alternative command and control server IP address, in case the primary one fails. The alternative IP address is generated by applying a bitwise XOR operation to each byte of the resolved command and control IP address with the byte 0xFE. In addition, as a possible anti-behaviour method, the loader verifies that the command and control server IP address is not ‘127.0.0.1’. Both of these methods are also present in the latest Team9 backdoor variants.

As with the previous Team9 loader variant, the command and control server sends back the binary files in an encrypted format. The decryption process is similar with its previous variant but with a minor change in the XOR key generation, the character ‘3’ is added between each hex digit of the day format (Figure 4). For example:

332330332330330335331338 (ASCII format, host date: 2020-05-18)

Figure 4 – Add the character ‘3’ in the generated XOR key

If the ‘-p’ parameter has been passed into the command line, the loader proceeds to download the Team9 backdoor directly from the command and control server. One notable addition is the process injection (hollow process injection) when the backdoor has been successfully downloaded and decrypted. The loader injects the backdoor to one of the following processes:

1. Svchost
2. Explorer
3. cmd

Whenever a binary file is successfully downloaded and properly decrypted, the loader adds or updates its persistence in the infected host. The persistence methods are available in table 3.

Persistence Method	Persistence Method Description
Scheduled task	The loader creates two scheduled tasks, one for the updated loader (if any) and one for the downloaded backdoor. The scheduled task names and timers are different.
Winlogon hijack	Add the malware’s file path in the ‘Userinit’ registry value. As a result, whenever the user logs in the malware is also executed.
Shortcut in the Startup folder	The loaders creates a shortcut, which points to the malware file, in the Startup folder. The name of the shortcut is ‘adobe’.
Hijack already existing shortcuts	The loader searches for shortcut files in Desktop and its subfolders. If it finds one then it copies the malware into the shortcut’s target location with the application’s file name and appends the string ‘__’ at the end of the original binary file name. Furthermore, the loader creates a ‘.bin’ file which stores the file path, file location and parameters. The ‘.bin’ file structure can be found in the Appendix section. When this structure is filled in with all required information, It is encrypted with the XOR key 0x61.

Table 3 – Persistence methods loader

The following tables summarises the identified bazar domains and their URIs for this Team9 loader variant.

URI	Description
/api/v117	Possibly downloads the 32-bit version of the Team9 loader
/api/v118	Possibly downloads the 64-bit version of the Team9 loader
/api/v119	Possibly downloads the 32-bit version of the Team9 backdoor
/api/v120	Possibly downloads the 64-bit version of the Team9 backdoor
/api/v85	Possibly downloads the 32-bit version of the Team9 loader
/api/v86	Possibly downloads the 64-bit version of the Team9 loader
/api/v87	Possibly downloads the 32-bit version of the Team9 backdoor
/api/v88	Possibly downloads the 64-bit version of the Team9 backdoor

Table 4 – Identified URIs for Team9 loader variant

Bazar domain

bestgame[.]bazar

forgame[.]bazar

Table 5 – Identified domains for Team9 loader variant

4. Team9 backdoor

We are confident that this is the backdoor which the loader installs onto the compromised host. In addition, we believe that the first variants of the Team9 backdoor started appearing in the wild in late March 2020. Each variant does not appear to have major changes and the core of the backdoor remains the same.

During analysis, we identified the following similarities between the backdoor and its loader:

1. Creates a mutex with a hardcoded name in order to avoid multiple instances running at the same time (So far the mutex names which we have identified are ‘mn_185445’ and ‘{589b7a4a-3776-4e82-8e7d-435471a6c03c}’)
2. Verifies that the keyboard and the operating system language is not Russian
3. Use of Emercoin domains with a similarity in the domain name choice

Furthermore, the backdoor generates a unique ID for the infected host. The process that it follows is:

1. Find the creation date of ‘C:\Windows’ (Windows FILETIME structure format). The result is then converted from a hex format to an ASCII representation. An example is shown in figures 5 (before conversion) and 6 (after conversion).
2. Repeat the same process but for the folder ‘C:\Windows\System32’
3. Append the second string to the first with a bullet point as a delimiter. For example, 01d3d1d8 b10c2916.01d3d1d8 b5b1e079
4. Get the NETBIOS name and append it to the previous string from step 3 along with a bullet point as a delimiter. For example: 01d3d1d8 b10c2916.01d3d1d8 b5b1e079.DESKTOP-4123EEB.
5. Read the volume serial number of C: drive and append it to the previous string. For example: 01d3d1d8 b10c2916.01d3d1d8 b5b1e079.DESKTOP-SKCF8VA.609fbbd5
6. Hash the string from step 5 using the MD5 algorithm. The output hash is the bot ID.

Note: In a few samples, the above algorithm is different. The developers use hard-coded dates, the Windows directory file paths in a string format (‘C:\Windows’ and ‘C:\Windows\system32’) and the NETBIOS name. Based on the samples’ functionality, there are many indications that these binary files were created for debugging purposes.

Figure 5 – Before conversion

Figure 6 – After conversion

4.1 Network communication

The backdoor appears to support network communication over ports 80 (HTTP) and 443(HTTPS). In recent samples, a certificate is issued from the infected host for communication over HTTPS. Each request to the command and control server includes at least the following information:

1. A URI path for requesting tasks (/2) or sending results (/3).
2. Group ID. This is added in the ‘Cookie’ header.

Lastly, unlike the loader which decrypts received network replies from the command and control server using the host’s date as the key, the Team9 backdoor uses the bot ID as the key.

4.2 Bot commands

The backdoor supports a variety of commands. These are summarised in the table below.

Command ID	Description	Parameters
0	Set delay time for the command and control server requests	Time to delay the requests
1	Collect infected host information	Memory buffer to fill in the collected data
10	Download file from an address and inject into a process using either hollowing process injection or Doppelgänging process injection	DWORD value that represents the corresponding execution method. This includes: Process hollowing injection Process Doppelgänging injection Write the file into disk and execute it Process mask – DWORD value that represents the process name to inject the payload. This can be one of the following: Explorer Cmd Calc (Not used in all variants) Svchost notepad Address from which the file is downloaded Command line
11	Download a DLL file and execute it	Timeout value Address to download the DLL Command line Timeout time
12	Execute a batch file received from the command and control server	DWORD value to determine if the batch script is to be stored into a Windows pipe (run from memory) or in a file into disk Timeout value. Batch file content
13	Execute a PowerShell script received from the command and control server	DWORD value to determine if the PowerShell script is to be stored into a Windows pipe (run from memory) or in a file into disk Timeout value. PowerShell script content
14	Reports back to the command and control server and terminates any handled tasks	None
15	Terminate a process	PID of the process to terminate
16	Upload a file to the command and control server. Note: Each variant of the backdoor has a set file size they can handle.	Path of the file to read and upload to the command and control server.
100	Remove itself	None

Table 6 – Supported backdoor commands

Table 7 summarises the report structure of each command when it reports back (POST request) to the command and control server. Note: In a few samples, the backdoor reports the results to an additional IP address (185.64.106[.]73) If it cannot communicate with the Bazar domains.

Command ID/Description	Command execution results structure
1/ Collect infected host information	The POST request includes the following information: Operating system information Operating system architecture NETBIOS name of the infected host Username of the infected user Backdoor’s file path Infected host time zone Processes list Keyboard language Antivirus name and installed applications Infected host’s external IP Shared drives Shared drives in the domain Trust domains Infected host administrators Domain admins
11/ Download a DLL file and execute it	The POST request includes the following parameters: Command execution errors (Passed in the parameter ‘err’) Process identifier (Passed in the ‘pid’ parameter) Command execution output (Passed in the parameter ‘stdout’, if any) Additional information from the command execution (Passed in the parameter ‘msg’, if any)
12/ Execute a batch file received from the command and control server	Same as the previous command (11/ Download a DLL file and execute it)
13/ Execute a PowerShell script received from the command and control server	Same as the previous command (11/ Download a DLL file and execute it)
14/ Reports back to the command and control server and terminate any handled tasks	POST request with the string ‘ok’
15/ Terminate a process	Same as the previous command (11/ Download a DLL file and execute it)
16/ Upload a file to the command and control server	No parameters. The file’s content is sent in a POST request.
100/ Remove itself	POST request with the string ‘ok’ or ‘process termination error’

Table 7 – Report structure

5. Appendix

5.1 struct shortcut_bin

struct shortcut_bin 

{ 
BYTE junk_data[434];
BYTE file_path[520];
BYTE filepath_dir[520];
BYTE file_loader_parameters[1024];
};

5.2 IOCs

File hashes

Description	SHA-256 Hash
Team9 backdoor (x64)	4F258184D5462F64C3A752EC25FB5C193352C34206022C0755E48774592B7707
Team9 backdoor (x64)	B10DCEC77E00B1F9B1F2E8E327A536987CA84BCB6B0C7327C292F87ED603837D
Team9 backdoor (x64)	363B6E0BC8873A6A522FE9485C7D8B4CBCFFA1DA61787930341F94557487C5A8
Team9 backdoor (x64)	F4A5FE23E21B6B7D63FA2D2C96A4BC4A34B40FD40A921B237A50A5976FE16001
Team9 backdoor (x64)	A0D0CFA8BF0BC5B8F769D8B64EAB22D308B108DD8A4D59872946D69C3F8C58A5
Team9 backdoor (x64)	059519E03772D6EEEA9498625AE8B8B7CF2F01FC8179CA5D33D6BCF29D07C9F4
Team9 backdoor (x64)	0F94B77892F22D0A0E7095B985F30B5EDBE17AB5B8D41F798EF0C708709636F4
Team9 backdoor (x64)	2F0F0956628D7787C62F892E1BD9EDDA8B4C478CF8F1E65851052C7AD493DC28
Team9 backdoor (x64)	37D713860D529CBE4EAB958419FFD7EBB3DC53BB6909F8BD360ADAA84700FAF2
Team9 backdoor (x64)	3400A7DF9EC3DC8283D5AC7ACCB6935691E93FEDA066CC46C6C04D67F7F87B2B
Team9 backdoor (x64)	5974D938BC3BBFC69F68C979A6DC9C412970FC527500735385C33377AB30373A
Team9 backdoor (x64)	C55F8979995DF82555D66F6B197B0FBCB8FE30B431FF9760DEAE6927A584B9E3
Team9 backdoor (x86)	94DCAA51E792D1FA266CAE508C2C62A2CA45B94E2FDFBCA7EA126B6CD7BC5B21
Team9 backdoor (x86)	4EE0857D475E67945AF2C5E04BE4DEC3D6D3EB7C78700F007A7FF6F8C14D4CB3
Team9 backdoor (x86)	8F552E9CA2BEDD90CE9935A665758D5DE2E86B6FDA32D98918534A8A5881F91A
Team9 backdoor (x86)	AE7DAA7CE3188CCFE4069BA14C486631EEA9505B7A107A17DDEE29061B0EDE99
Team9 backdoor (x86)	F3C6D7309F00CC7009BEA4BE6128F0AF2EA6B87AB7A687D14092F85CCD35C1F5
Team9 backdoor (x86)	6CBF7795618FB5472C5277000D1C1DE92B77724D77873B88AF3819E431251F00
Team9 backdoor (x86)	B0B758E680E652144A78A7DDECC027D4868C1DC3D8D7D611EC4D3798358B0CE5
Team9 backdoor (x86)	959BA7923992386ABF2E27357164672F29AAC17DDD4EE1A8AD4C691A1C566568
Team9 backdoor (x86)	3FE61D87C9454554B0CE9101F95E18ABAD8AC6C62DCC88DC651DDFB20568E060
Team9 loader (x64)	B3764EF42D526A1AE1A4C3B0FE198F35C6BC5C07D5F155D15060B94F8F6DC695
Team9 loader (x64)	210C51AAB6FC6C52326ECE9DBD3DDAB5F58E98432EF70C46936672C79542FBD0
Team9 loader (x64)	11B5ADAEFD04FFDACEB9539F95647B1F51AEC2117D71ECE061F15A2621F1ECE9
Team9 loader (x64)	534D60392E0202B24D3FDAF992F299EF1AF1FB5EFEF0096DD835FE5C4E30B0FA
Team9 loader (x64)	9D3A265688C1A098DD37FE77C139442A8EB02011DA81972CEDDC0CF4730F67CF
Team9 loader (x64)	CE478FDBD03573076394AC0275F0F7027F44A62A306E378FE52BEB0658D0B273
Team9 loader (x64)	5A888D05804D06190F7FC408BEDE9DA0423678C8F6ECA37ECCE83791DE4DF83D
Team9 loader (x64)	EB62AD35C613A73B0BD28C1779ACE80E2BA587A7F8DBFEC16CF5BF520CAA71EE
Team9 loader (x64)	A76426E269A2DEFABCF7AEF9486FF521C6110B64952267CFE3B77039D1414A41
Team9 loader (x64)	65CDBDD03391744BE87AC8189E6CD105485AB754FED0B069A1378DCA3E819F28
Team9 loader (x64)	38C9C3800DEA2761B7FAEC078E4BBD2794B93A251513B3F683AE166D7F186D19
Team9 loader (x64)	8F8673E6C6353187DBB460088ADC3099C2F35AD868966B257AFA1DF782E48875
Team9 loader (x86)	35B3FE2331A4A7D83D203E75ECE5189B7D6D06AF4ABAC8906348C0720B6278A4
Team9 loader (x86)	65E44FC8527204E88E38AB320B3E82694D1548639565FDAEE53B7E0F963D3A92
Team9 loader (x86)	F53509AF91159C3432C6FAF4B4BE2AE741A20ADA05406F9D4E9DDBD48C91EBF9
Team9 loader (x86)	73339C130BB0FAAD27C852F925AA1A487EADF45DF667DB543F913DB73080CD5D
Team9 loader (x86)	2342C736572AB7448EF8DA2540CDBF0BAE72625E41DAB8FFF58866413854CA5C
Team9 loader (x86)	079A99B696CC984375D7A3228232C44153A167C1936C604ED553AC7BE91DD982
Team9 loader (x86)	0D8AEACF4EBF227BA7412F8F057A8CDDC54021846092B635C8D674B2E28052C6
Team9 loader (x86)	F83A815CE0457B50321706957C23CE8875318CFE5A6F983A0D0C580EBE359295
Team9 loader (x86)	3FA209CD62BACC0C2737A832E5F0D5FD1D874BE94A206A29B3A10FA60CEB187D
Team9 loader (x86)	05ABD7F33DE873E9630F9E4F02DBD0CBC16DD254F305FC8F636DAFBA02A549B3

Table 8 – File hashes

Identified Emercoin domains

Domains

newgame[.]bazar

thegame[.]bazar

portgame[.]bazar

workrepair[.]bazar

realfish[.]bazar

eventmoult[.]bazar

bestgame[.]bazar

forgame[.]bazar

Zirabuo[.]bazar

Table 9 – Identified Emercoin domains

Command and Control IPs

C&C IPs

34.222.222[.]126

71.191.52[.]192

77.213.120[.]90

179[.]43.134.164

185[.]65.202.62

220[.]32.32.128

34[.]222.222.126

51[.]81.113.26

71[.]191.52.192

77[.]213.120.90

85[.]204.116.58

Table 10 – Command and Control IPs

Identified DNS IPs

DNS IPs

51[.]254.25.115

193[.]183.98.66

91[.]217.137.37

87[.]98.175.85

185[.]121.177.177

169[.]239.202.202

198[.]251.90.143

5[.]132.191.104

111[.]67.20.8

163[.]53.248.170

142[.]4.204.111

142[.]4.205.47

158[.]69.239.167

104[.]37.195.178

192[.]99.85.244

158[.]69.160.164

46[.]28.207.199

31[.]171.251.118

81[.]2.241.148

82[.]141.39.32

50[.]3.82.215

46[.]101.70.183

5[.]45.97.127

130[.]255.78.223

144[.]76.133.38

139[.]59.208.246

172[.]104.136.243

45[.]71.112.70

163[.]172.185.51

5[.]135.183.146

51[.]255.48.78

188[.]165.200.156

147[.]135.185.78

92[.]222.97.145

51[.]255.211.146

159[.]89.249.249

104[.]238.186.189

139[.]59.23.241

94[.]177.171.127

45[.]63.124.65

212[.]24.98.54

178[.]17.170.179

185[.]208.208.141

82[.]196.9.45

146[.]185.176.36

89[.]35.39.64

89[.]18.27.167

77[.]73.68.161

185[.]117.154.144

176[.]126.70.119

139[.]99.96.146

217[.]12.210.54

185[.]164.136.225

192[.]52.166.110

63[.]231.92.27

66[.]70.211.246

96[.]47.228.108

45[.]32.160.206

128[.]52.130.209

35[.]196.105.24

172[.]98.193.42

162[.]248.241.94

107[.]172.42.186

167[.]99.153.82

138[.]197.25.214

69[.]164.196.21

94[.]247.43.254

94[.]16.114.254

151[.]80.222.79

176[.]9.37.132

192[.]71.245.208

195[.]10.195.195

Table 11 – Identified DNS IPs

Mutexes

Component	Mutex name
Team9 backdoor	mn_185445
Team9 backdoor	{589b7a4a-3776-4e82-8e7d-435471a6c03c}
Team9 loader	ld_201127

Table 12 – Mutex names Team9 components

Host IOCs

1. Files ending with the string ‘_lyrt’
2. Scheduled tasks with names ‘StartAT’ and ‘StartDT’
3. Shortcut with file name ‘adobe’ in the Windows ‘StartUp’ folder
4. Registry value name ‘BackUp Mgr’ in the ‘Run’ registry key

Network detection

Source(s):

[1] https://www.bleepingcomputer.com/news/security/bazarbackdoor-trickbot-gang-s-new-stealthy-network-hacking-malware/

Written by Rindert Kramer

Introduction

A while back during a penetration test of an internal network, we encountered physically segmented networks. These networks contained workstations joined to the same Active Directory domain, however only one network segment could connect to the internet. To control workstations in both segments remotely with Cobalt Strike, we built a tool that uses the shared Active Directory component to build a communication channel. For this, it uses the LDAP protocol which is commonly used to manage Active Directory, effectively routing beacon data over LDAP. This blogpost will go into detail about the development process, how the tool works and provides mitigation advice.

Scenario

A couple of months ago, we did a network penetration test at one of our clients. This client had multiple networks that were completely firewalled, so there was no direct connection possible between these network segments. Because of cost/workload efficiency reasons, the client chose to use the same Active Directory domain between those network segments. This is what it looked like from a high-level overview.

We had physical access on workstations in both segment A and segment B. In this example, workstations in segment A were able to reach the internet, while workstations in segment B could not. While we did have physical access on workstation in both network segments, we wanted to control workstations in network segment B from the internet.

Active Directory as a shared component

Both network segments were able to connect to domain controllers in the same domain and could interact with objects, authenticate users, query information and more. In Active Directory, user accounts are objects to which extra information can be added. This information is stored in attributes. By default, user accounts have write permissions on some of these attributes. For example, users can update personal information such as telephone numbers or office locations for their own account. No special privileges are needed for this, since this information is writable for the identity SELF, which is the account itself. This is configured in the Active Directory schema, as can be seen in the screenshot below.

Personal information, such as a telephone number or street address, is by default readable for every authenticated user in the forest. Below is a screenshot that displays the permissions for public information for the Authenticated Users identity.

The permissions set in the screenshot above provide access to the attributes defined in the Personal-Information property set. This property set contains 40+ attributes that users can read from and write to. The complete list of attributes can be found in the following article: https://docs.microsoft.com/en-us/windows/win32/adschema/r-personal-information
By default, every user that has successfully been authenticated within the same forest is an ‘authenticated user’. This means we can use Active Directory as a temporary data store and exchange data between the two isolated networks by writing the data to these attributes and then reading the data from the other segment.
If we have access to a user account, we can use that user account in both network segments simultaneously to exchange data over Active Directory. This will work, regardless of the security settings of the workstation, since the account will communicate directly to the domain controller instead of the workstation.

To route data over LDAP we need to get code execution privileges first on workstations in both segments. To achieve this, however, is up to the reader and beyond the scope of this blogpost.
To route data over LDAP, we would write data into one of the attributes and read the data from the other network segment.
In a typical scenario where we want to execute ipconfigon a workstation in network Segment B from a workstation in network Segment A, we would write the ipconfig command into an attribute, read the ipconfig command from network segment B, execute the command and write the results back into the attribute.

This process is visualized in the following overview:

A sample script to utilize this can be found on our GitHub page: https://github.com/fox-it/LDAPFragger/blob/master/LDAPChannel.ps1

While this works in practice to communicate between segmented networks over Active Directory, this solution is not ideal. For example, this channel depends on the replication of data between domain controllers. If you write a message to domain controller A, but read the message from domain controller B, you might have to wait for the domain controllers to replicate in order to get the data. In addition, in the example above we used to info-attribute to exchange data over Active Directory. This attribute can hold up to 1024 bytes of information. But what if the payload exceeds that size? More issues like these made this solution not an ideal one.

Lastly, people already built some proof of concepts doing the exact same thing. Harmj0y wrote an excellent blogpost about this technique: https://www.harmj0y.net/blog/powershell/command-and-control-using-active-directory/

That is why we decided to build an advanced LDAP communication channel that fixes these issues.

Building an advanced LDAP channel

In the example above, the info-attribute is used. This is not an ideal solution, because what if the attribute already contains data or if the data ends up in a GUI somewhere?

To find other attributes, all attributes from the Active Directory schema are queried and:

• Checked if the attribute contains data;
• If the user has write permissions on it;
• If the contents can be cleared.

If this all checks out, the name and the maximum length of the attribute is stored in an array for later usage.

Visually, the process flow would look like this:

As for (payload) data not ending up somewhere in a GUI such as an address book, we did not find a reliable way to detect whether an attribute ends up in a GUI or not, so attributes such as telephoneNumber are added to an in-code blacklist. For now, the attribute with the highest maximum length is selected from the array with suitable attributes, for speed and efficiency purposes. We refer to this attribute as the ‘data-attribute’ for the rest of this blogpost.

Sharing the attribute name
Now that we selected the data-attribute, we need to find a way to share the name of this attribute from the sending network segment to the receiving side. As we want the LDAP channel to be as stealthy as possible, we did not want to share the name of the chosen attribute directly.

In order to overcome this hurdle we decided to use hashing. As mentioned, all attributes were queried in order to select a suitable attribute to exchange data over LDAP. These attributes are stored in a hashtable, together with the CRC representation of the attribute name. If this is done in both network segments, we can share the hash instead of the attribute name, since the hash will resolve to the actual name of the attribute, regardless where the tool is used in the domain.

Avoiding replication issues
Chances are that the transfer rate of the LDAP channel is higher than the replication occurrence between domain controllers. The easy fix for this is to communicate to the same domain controller.
That means that one of the clients has to select a domain controller and communicate the name of the domain controller to the other client over LDAP.

The way this is done is the same as with sharing the name of the data-attribute. When the tool is started, all domain controllers are queried and stored in a hashtable, together with the CRC representation of the fully qualified domain name (FQDN) of the domain controller. The hash of the domain controller that has been selected is shared with the other client and resolved to the actual FQDN of the domain controller.

Initially sharing data
We now have an attribute to exchange data, we can share the name of the attribute in an obfuscated way and we can avoid replication issues by communicating to the same domain controller. All this information needs to be shared before communication can take place.
Obviously, we cannot share this information if the attribute to exchange data with has not been communicated yet (sort of a chicken-egg problem).

The solution for this is to make use of some old attributes that can act as a placeholder. For the tool, we chose to make use of one the following attributes:

• primaryInternationalISDNNumber;
• otherFacsimileTelephoneNumber;
• primaryTelexNumber.

These attributes are part of the Personal-Information property set, and have been part of that since Windows 2000 Server. One of these attributes is selected at random to store the initial data.
We figured that the chance that people will actually use these attributes are low, but time will tell if that is really the case

Message feedback
If we send a message over LDAP, we do not know if the message has been received correctly and if the integrity has been maintained during the transmission. To know if a message has been received correctly, another attribute will be selected – in the exact same way as the data-attribute – that is used to exchange information regarding that message. In this attribute, a CRC checksum is stored and used to verify if the correct message has been received.

In order to send a message between the two clients – Alice and Bob –, Alice would first calculate the CRC value of the message that she is about to send herself, before she sends it over to Bob over LDAP. After she sent it to Bob, Alice will monitor Bob’s CRC attribute to see if it contains data. If it contains data, Alice will verify whether the data matches the CRC value that she calculated herself. If that is a match, Alice will know that the message has been received correctly.
If it does not match, Alice will wait up until 1 second in 100 millisecond intervals for Bob to post the correct CRC value.

The process on the receiving end is much simpler. After a new message has been received, the CRC is calculated and written to the CRC attribute after which the message will be processed.

Fragmentation
Another challenge that we needed to overcome is that the maximum length of the attribute will probably be smaller than the length of the message that is going to be sent over LDAP. Therefore, messages that exceed the maximum length of the attribute need to be fragmented.
The message itself contains the actual data, number of parts and a message ID for tracking purposes. This is encoded into a base64 string, which will add an additional 33% overhead.
The message is then fragmented into fragments that would fit into the attribute, but for that we need to know how much information we can store into said attribute.
Every attribute has a different maximum length, which can be looked up in the Active Directory schema. The screenshot below displays the maximum length of the info-attribute, which is 1024.

At the start of the tool, attribute information such as the name and the maximum length of the attribute is saved. The maximum length of the attribute is used to fragment messages into the correct size, which will fit into the attribute. If the maximum length of the data-attribute is 1024 bytes, a message of 1536 will be fragmented into a message of 1024 bytes and a message of 512 bytes.
After all fragments have been received, the fragments are put back into the original message. By also using CRC, we can send big files over LDAP. Depending on the maximum length of the data-attribute that has been selected, the transfer speed of the channel can be either slow or okay.

Autodiscover
The working of the LDAP channel depends on (user) accounts. Preferably, accounts should not be statically configured, so we needed a way for clients both finding each other independently.
Our ultimate goal was to route a Cobalt Strike beacon over LDAP. Cobalt Strike has an experimental C2 interface that can be used to create your own transport channel. The external C2 server will create a DLL injectable payload upon request, which can be injected into a process, which will start a named pipe server. The name of the pipe as well as the architecture can be configured. More information about this can be read at the following location: https://www.cobaltstrike.com/help-externalc2

Until now, we have gathered the following information:

• 8 bytes – Hash of data-attribute
• 8 bytes – Hash of CRC-attribute
• 8 bytes – Hash of domain controller FQDN

Since the name of the pipe as well as the architecture are configurable, we need more information:

• 8 bytes – Hash of the system architecture
• 8 bytes – Pipe name

The hash of the system architecture is collected in the same way as the data, CRC and domain controller attribute. The name of the pipe is a randomized string of eight characters. All this information is concatenated into a string and posted into one of the placeholder attributes that we defined earlier:

• primaryInternationalISDNNumber;
• otherFacsimileTelephoneNumber;
• primaryTelexNumber.

The tool will query the Active Directory domain for accounts where one of each of these attributes contains data. If found and parsed successfully, both clients have found each other but also know which domain controller is used in the process, which attribute will contain the data, which attribute will contain the CRC checksums of the data that was received but also the additional parameters to create a payload with Cobalt Strike’s external C2 listener. After this process, the information is removed from the placeholder attribute.
Until now, we have not made a distinction between clients. In order to make use of Cobalt Strike, you need a workstation that is allowed to create outbound connections. This workstation can be used to act as an implant to route the traffic over LDAP to another workstation that is not allowed to create outbound connections. Visually, it would something like this.

Let us say that we have our tool running in segment A and segment B – Alice and Bob. All information that is needed to communicate over LDAP and to generate a payload with Cobalt Strike is already shared between Alice and Bob. Alice will forward this information to Cobalt Strike and will receive a custom payload that she will transfer to Bob over LDAP. After Bob has received the payload, Bob will start a new suspended child process and injects the payload into this process, after which the named pipe server will start. Bob now connects to the named pipe server, and sends all data from the pipe server over LDAP to Alice, which on her turn will forward it to Cobalt Strike. Data from Cobalt Strike is sent to Alice, which she will forward to Bob over LDAP, and this process will continue until the named pipe server is terminated or one of the systems becomes unavailable for whatever reason. To visualize this in a nice process flow, we used the excellent format provided in the external C2 specification document.

After a new SMB beacon has been spawned in Cobalt Strike, you can interact with it just as you would normally do. For example, you can run MimiKatz to dump credentials, browse the local hard drive or start a VNC stream.
The tool has been made open source. The source code can be found here: https://github.com/fox-it/LDAPFragger

The tool is easy to use: Specifying the cshost and csport parameter will result in the tool acting as the proxy that will route data from and to Cobalt Strike. Specifying AD credentials is not necessary if integrated AD authentication is used. More information can be found on the Github page. Please do note that the default Cobalt Strike payload will get caught by modern AVs. Bypassing AVs is beyond the scope of this blogpost.

Why a C2 LDAP channel?

This solution is ideal in a situation where network segments are completely segmented and firewalled but still share the same Active Directory domain. With this channel, you can still create a reliable backdoor channel to parts of the internal network that are otherwise unreachable for other networks, if you manage to get code execution privileges on systems in those networks. Depending on the chosen attribute, speeds can be okay but still inferior to the good old reverse HTTPS channel. Furthermore, no special privileges are needed and it is hard to detect.

Remediation

In order to detect an LDAP channel like this, it would be necessary to have a baseline identified first. That means that you need to know how much traffic is considered normal, the type of traffic, et cetera. After this information has been identified, then you can filter out the anomalies, such as:

• Unusual amount of traffic from clients to a domain controller;
• A high volume of changes to AD objects, so monitor for event ID 5136 on domain controllers;
• Enable and inspect LDAP logging. For more information on how to do that, see the following article: https://support.microsoft.com/en-us/help/314980/how-to-configure-active-directory-and-lds-diagnostic-event-logging
• Use Advanced Threat Protection from Microsoft. It has some neat functionality that makes it easier to detect malicious LDAP queries. See for more information the following article: https://techcommunity.microsoft.com/t5/Microsoft-Defender-ATP/Hunting-for-reconnaissance-activities-using-LDAP-search-filters/ba-p/824726

Monitor the usage of the three static placeholders mentioned earlier in this blogpost might seem like a good tactic as well, however, that would be symptom-based prevention as it is easy for an attacker to use different attributes, rendering that remediation tactic ineffective if attackers change the attributes.

Author: Ruud van Luijk

Attacks need to have a form of communication with their victim machines, also known as Command and Control (C2) [1]. This can be in the form of a continuous connection or connect the victim machine directly. However, it’s convenient to have the victim machine connect to you. In other words: It has to communicate back. This blog describes a method to detect one technique utilized by many popular attack frameworks based solely on connection metadata and statistics, in turn enabling this technique to be used on multiple log sources.

Many attack frameworks use beaconing

Frameworks like Cobalt Strike, PoshC2, and Empire, but also some run-in-the-mill malware, frequently check-in at the C2 server to retrieve commands or to communicate results back. In Cobalt Strike this is called a beacon, but concept is similar for many contemporary frameworks. In this blog the term ‘beaconing’ is used as a general term for the call-backs of malware. Previous fingerprinting techniques shows that there are more than a thousand Cobalt Strike servers online in a month that are actively used by several threat actors, making this an important point to focus on.

While the underlying code differs slightly from tool to tool, they often exist of two components to set up a pattern for a connection: a sleep and a jitter. The sleep component indicates how long the beacon has to sleep before checking in again, and the jitter modifies the sleep time so that a random pattern emerges. For example: 60 seconds of sleep with 10% jitter results in a uniformly random sleep between 54 and 66 seconds (PoshC2 [3], Empire [4]) or a uniformly random sleep between 54 and 60 seconds (Cobalt Strike [5]). Note the slight difference in calculation.

This jitter weakens the pattern but will not dissolve the pattern entirely. Moreover, due to the uniform distribution used for the sleep function the jitter is symmetrical. This is in our advantage while detecting this behaviour!

Detecting the beacon

While static signatures are often sufficient in detecting attacks, this is not the case for beaconing. Most frameworks are very customizable to your needs and preferences. This makes it hard to write correct and reliable signatures. Yet, the pattern does not change that much. Therefore, our objective is to find a beaconing pattern in seemingly pattern less connections in real-time using a more anomaly-based method. We encourage other blue teams/defenders to do the same.

Since the average and median of the time between the connections is more or less constant, we can look for connections where the times between consecutive connections constantly stay within a certain range. Regular traffic should not follow such pattern. For example, it makes a few fast-consecutive connections, then a longer time pause, and then again, some interaction. Using a wider range will detect the beacons with a lot of jitter, but more legitimate traffic will also fall in the wider range. There is a clear trade-off between false positives and accounting for more jitter.

In order to track the pattern of connections, we create connection pairs. For example, an IP that connects to a certain host, can be expressed as ’10.0.0.1 -> somerandomhost.com”. This is done for all connection pairs in the network. We will deep dive into one connection pair.

The image above illustrates a beacon is simulated for the pair ’10.0.0.1 -> somerandomhost.com” with a sleep of 1 second and a jitter of 20%, i.e. having a range between 0.8 and 1.2 seconds and the model is set to detect a maximum of 25% jitter. Our model follows the expected timing of the beacon as all connections remain within the lower and upper bound. In general, the more a connection reside within this bandwidth, the more likely it is that there is some sort of beaconing. When a beacon has a jitter of 50% our model has a bandwidth of 25%, it is still expected that half of the beacons will fall within the specified bandwidth.

Even when the configuration of the beacon changes, this method will catch up. The figure above illustrates a change from one to two seconds of sleep whilst maintaining a 10% beaconing. There is a small period after the change where the connections break through the bandwidth, but after several connections the model catches up.

This method can work with any connection pair you want to track. Possibilities include IPs, HTTP(s) hosts, DNS requests, etc. Since it works on only the metadata, this will also help you to hunt for domain fronted beacons (keeping in mind your baseline).

Keep in mind the false positives

Although most regular traffic will not follow a constant pattern, this method will most likely result in several false positives. Every connection that runs on a timer will result in the exact same pattern as beaconing. Example of such connections are windows telemetry, software updates, and custom update scripts. Therefore, some baselining is necessary before using this method for alerting. Still, hunting will always be possible without baselining!

Conclusion

Hunting for C2 beacons proves to be a worthwhile exercise. Real world scenarios confirm the effectiveness of this approach. Depending on the size of the network logs, this method can plow through a month of logs within an hour due to the simplicity of the method. Even when the hunting exercise did not yield malicious results, there are often other applications that act on specific time intervals and are also worth investigating, removing, or altering. While this method will not work when an adversary uses a 100% jitter. Keep in mind that this will probably annoy your adversary, so it’s still a win!

References:

[1]. https://attack.mitre.org/tactics/TA0011/

[2]. https://blog.fox-it.com/2019/02/26/identifying-cobalt-strike-team-servers-in-the-wild/

[3]. https://github.com/nettitude/PoshC2/blob/master/C2-Server.ps1

https://github.com/nettitude/PoshC2_Python/blob/4aea6f957f4aec00ba1f766b5ecc6f3d015da506/Files/Implant-Core.ps1

[4]. https://github.com/EmpireProject/Empire/blob/master/data/agent/agent.ps1

[5]. https://www.cobaltstrike.com/help-beacon

Combining both n-grams and random forest models to detect malicious activity.

Author: Haroen Bashir

An essential part of Managed Detection and Response at Fox-IT is the Security Operations Center. This is our frontline for detecting and analyzing possible threats. Our Security Operations Center brings together the best in human and machine analysis and we continually strive to improve both. For instance, we develop machine learning techniques for detecting malicious content such as DGA domains or unusual SMB traffic. In this blog entry we describe a possible method for random filename detection.

During traffic analysis of lateral movement we sometimes recognize random filenames, indicating possible malicious activity or content. Malicious actors often need to move through a network to reach their primary objective, more popularly known as lateral movement [1].

There is a variety of routes for adversaries to perform lateral movement. Attackers can use penetration testing frameworks such as Metasploit [3] or Microsoft Sysinternal application PsExec. This application creates the possibility for remote command execution over the SMB protocol [4].

Due to its malicious nature we would like to detect lateral movement as quickly as possible. In this blogpost we build on our previous blog entry [2] and we describe how we can apply the magic of machine learning in detection of random filenames in SMB traffic.

Supervised versus unsupervised detection models 

Machine learning can be applied in various domains. It is widely used for prediction and classification models, which suits our purpose perfectly. We investigated two possible machine learning architectures for random filename detection.

The first detection method for random filenames is set up by creating bigrams of filenames,  which you can find more information about in our previous post [2]. This detection method is based on unsupervised learning. After the model learns a baseline of common filenames, it can now detect when filenames don’t belong in its learned baseline.

This model has a drawback; it requires a lot of data. The solution can be found with supervised machine learning models. With supervised machine learning we feed a model data whilst simultaneously providing the label of the data. In our current case, we label data as either random or not-random.

A powerful supervised machine learning model is the random forest. We picked this architecture as it’s widely used for predictive models in both classification and regression problems. For an introduction into this technique we advise you to see [4]. The random forest is based on multiple decision trees, increasing the stability of a detection model. The following diagram illustrates the architecture of the detection model we built.

Similar to the first model, we create bigrams of the filenames. The model cannot train on bigrams however, so we have to map the bigrams into numerical vectors. After training and testing the model we then focus on fine-tuning hyperparameters. This is essential for increasing the stability of the model. An important hyperparameter of the random forest is depth. A greater depth will create more decision splits in the random forest, which can easily cause overfitting. It is therefore highly desirable to keep the depth as low as possible, whilst simultaneously maintaining high precision rates.Results

Proper data is one of the most essential parts in machine learning. We gathered our data by scraping nearly 180.000 filenames from SMB logs of our own network. Next to this, we generated 1.000 random filenames ourselves. We want to make sure that the models don’t develop a bias towards for example the extension “.exe”, so we stripped the extensions from the filenames.

As we stated earlier the bigrams model is based on our previously published DGA detection model. This model has been trained on 90% percent of filenames. It is then tested on the remaining filenames and 100% of random filenames.

The random forest has been trained and tested in multiple folds, which is a cross validation technique[6]. We evaluate our predictions in a joint confusion matrix which is illustrated below.

True positives are shown in the upper right column, the bigrams model detected 71% of random filenames and the random forest detected 81% of random filenames. As you can see the models produce low false positive rates, in both models ~0% of not random filenames have been incorrectly classified as random. This is great for use in our Security Operations Center, as this keeps the workload on the analysts consistent.

The F1-scores are 0.83 and 0.89 respectively. Because we focus on adding detection with low false positive rates, it is not our priority to reduce the false negative rates. In future work we will take a better look at the false negative rates of the models.

We were quite interested in differences in both detection models. Looking at the visualization below we can observe that both models equally detect 572 random filenames. They separately detect 236 and 141 random filenames respectively. The bigrams model might miss more random filenames due to its unsupervised architecture. It is possible that the bigrams model requires more data to create it’s baseline and therefore doesn’t perform as well as the supervised random forest.The overlap in both models and the low false positive rate gave us the idea to run both these models cooperatively for detection of random filenames. It doesn’t cost much processing and we would gain a lot! In practical setting this would mean that if a random filename slips by one detection model, it is still possible for the other model to detect this. In theory, we detect 90% of random filenames! The low false positive rates and complementary aspects of the detection models indicate that this setup could be really useful for detection in our Security Operations Center.

Conclusion

During traffic analysis in our Security Operations Center we sometimes recognize random filenames, indicating possible lateral movement. Malicious actors can use penetration testing frameworks (e.g. Metasploit) and Microsoft processes (e.g. PsExec) for lateral movement. If adversaries are able to do this, they can easily compromise a (sub)network of a target. Needless to say that we want to detect this behavior as quickly as possible.

In this blog entry we described how we applied machine learning in order to detect these random filenames. We showed two models for detection: a bigrams model and a random forest. Both these models yield good results in testing stage, indicated by the low false positive rates. We also looked at the overlap in predictions from which we concluded that we can detect 90% of random filenames in SMB traffic! This gave us the idea to run both detection models cooperatively in our Security Operations Center.

For future work we would like to research the usability of these models on endpoint data, as our current research is solely focused on detection in network traffic. There is for instance lots of malware that outputs random filenames on a local machine. This is just one of many possibilities which we can better investigate.

All in all, we can confidently conclude that machine learning methods are one of many efficient ways to keep up with adversaries and improve our security operations!

 

References

[1] – https://attack.mitre.org/tactics/TA0008/

[2] – https://blog.fox-it.com/2019/06/11/using-anomaly-detection-to-find-malicious-domains.

[3] – https://www.offensive-security.com/metasploit-unleashed/pivoting/

[4] – https://www.mindpointgroup.com/blog/lateral-movement-with-psexec/

[5] – https://medium.com/@williamkoehrsen/random-forest-simple-explanation-377895a60d2d

[6] – https://towardsdatascience.com/why-and-how-to-cross-validate-a-model-d6424b45261f

 

 

 

 

 

Author: Willem Zeeman

“Office 365 again?”. At the Forensics and Incident Response department of Fox-IT, this is heard often.  Office 365 breach investigations are common at our department.
You’ll find that this blog post actually doesn’t make a case for Office 365 being inherently insecure – rather, it discusses some of the predictability of Office 365 that adversaries might use and mistakes that organisations make. The final part of this blog describes a quick check for signs if you already are a victim of an Office 365 compromise. Extended details about securing and investigating your Office 365 environment will be covered in blogs to come.

Office 365 is predictable
A lot of adversaries seem to have a financial motivation for trying to breach an email environment. A typical adversary doesn’t want to waste too much time searching for the right way to access the email system, despite the fact that it is often enough to browse to an address like https://webmail.companyname.tld. But why would the adversary risk encountering a custom or extra-secure web page? Why would the adversary accept the uncertainty of having to deal with a certain email protocol in use by the particular organisation? Why guess the URL? It’s much easier to use the “Cloud approach”.

In this approach, an adversary first collects a list of valid credentials (email address and password), most frequently gathered with the help of a successful phishing campaign. When credentials have been captured, the adversary simply browses to https://office.com and tries them. If there’s no second type of authentication required, they are in. That’s it. The adversary is now in paradise, because after gaining access, they also know what to expect here. Not some fancy or out-dated email system, but an Office 365 environment just like all the others. There’s a good chance that the compromised account owns an Exchange Online mailbox too.

In predictable environments, like Office 365, it’s also much easier to automate your process of evil intentions. The adversary may create a script or use some tooling, complement it with the gathered list of credentials and sit back. Of course, an adversary may also target a specific on-premises system configuration, but seen from an opportunistic point of view, why would they? According to Microsoft, more than 180 million people are using their popular cloud-based solution. It’s far more effective to try another set of credentials and enter another predictable environment than it is to spend time in figuring out where information might be available, and how the environment is configured.

Office 365 is… secure?
Well, yes, Office 365 is a secure platform. The truth is that it has a lot more easy-to-deploy security capabilities than the most common on-premises solutions. The issue here is that organisations seem to not always realise what they could and should do to secure Office 365.

Best practices for securing your Office 365 environment will be covered in a later blog, but here’s a sneak preview: More than 90% of the Office 365 breaches investigated by Fox-IT would not have happened if the organisation would have had multi-factor authentication in place. No, implementation doesn’t need to be a hassle. Yes, it’s a free to use option. Other security measures like receiving automatic alerts on suspicious activity detected by built-in Office 365 processes are free as well, but often neglected.

Simple preventive solutions like these are not even commonly available in on-premises-situation environments. It almost seems that many companies assume that they can get perfect security right out of the box, rather than configuring the platform to their needs. This may be the reason for organisations to do not even bother configuring Office 365 in a more secure way. That’s a pity, especially when securing your environment is often just a few cloud-clicks away. Office 365 may not be less secure than an on-premises solution, but it might be more prone to being compromised though. Thanks to the lack of involved expertise, and thanks to adversaries who know how to take advantage of this. Microsoft already offers multi-factor authentication to reduce the impact of attacks like phishing. This is great news, because we know from experience that most of the compromises that we see could have been prevented if those companies had used MFA. However, compelling more organisations to adopt it remains an ongoing challenge, and how to drive increased adoption of MFA remains an open question.

A lot of organisations are already compromised. Are you?
At our department we often see that it may take months(!) for an organisation to realise that they have been compromised. In Office 365 breaches, the adversary is often detected due to an action that causes so much noise that it’s no longer possible for the adversary to hide. When the adversary thinks it’s no longer beneficial to persist, the next step is to try to get foothold into another organisation. In our investigations, we see that when this happens, the adversary has already tried reaching a financial goal. This financial goal is often achieved by successfully committing a payment related fraud in which they use an employee’s internal email account to mislead someone. Eventually, to advance into another organisation, a phishing email is sent by the adversary to a large part of the organisation’s address list. In the end, somebody will likely take the bait and leave their credentials on a malevolent and adversary-controlled website. If a victim does, the story starts over again, at the other organisation. For the adversary, it’s just a matter of repeating the steps.

The step to gain foothold in another organisation is also the moment that a lot of (phishing) email messages are flowing out of the organisation. Thanks to Office 365 intelligence, these are automatically blocked if the number of messages surpasses a given limit based on the user’s normal email behaviour. This is commonly the moment where the victim gets in touch with their system administrator, asking why they can’t send any email anymore. Ideally, the system administrator will quickly notice the email messages containing malicious content and report the incident to the security team.

For now, let’s assume you do not have the basic precautions set up, and you want to know if somebody is lurking in your Office 365 environment. You could hire experts to forensically scrutinize your environment, and that would be a correct answer. There actually is a relatively easy way to check if Microsoft’s security intelligence already detected some bad stuff. In this blog we will zoom in on one of these methods. Please keep in mind that a full discussion of these range of the available methods is beyond the scope of this blog post. This blog post describes the method that from our perspective gives quick insights in (afterwards) checking for signs of a breach. The not-so-obvious part of this step is that you will find the output in Microsoft Azure, rather than in Office 365. A big part of the Office 365 environment is actually based on Microsoft Azure, and so is its authentication. This is why it’s usually[1] possible to log in at the Azure portal and check for Risk events.

The steps:

1. Go to https://portal.azure.com and sign-in with your Office 365 admin account[2]
2. At the left pane, click Azure Active Directory
3. Scroll down to the part that says Security and click Risk events
4. If there are any risky events, these will be listed here. For example, impossible travels are one of the more interesting events to pay attention to. These may look like this:

This risk event type identifies two sign-ins from the same account, originating from geographically distant locations within a period in which the geographically distance cannot be covered. Other unusual sign-ins are also marked by machine learning algorithms. Impossible travel is usually a good indicator that an adversary was able to successfully sign in. However, false positives may occur when a user is traveling using a new device or using a VPN.

Apart from the impossible travel registrations, Azure also has a lot of other automated checks which might be listed in the Risk events section. If you have any doubts about these, or if a compromise seems likely: please get in contact with your security team as fast as possible. If your security team needs help in the investigation or mitigation, contact the FoxCERT team. FoxCERT is available 24/7 by phone on +31 (0)800 FOXCERT (+31 (0)800-3692378).

[1] Disregarding more complex federated setups, and assuming the licensing model permits.

[2] The risky sign-ins reports are available to users in the following roles: Security Administrator, Global Administrator, Security Reader. Source: https://docs.microsoft.com/en-us/azure/active-directory/reports-monitoring/concept-risky-sign-ins