December 15, 2023

How Darktrace Halted A DarkGate in MS Teams

Discover how Darktrace thwarted DarkGate malware in Microsoft Teams. Stay informed on the latest cybersecurity measures and protect your business.

Written by

Natalia Sánchez Rocafort

Cyber Security Analyst

Inside the SOC

Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.

Written by

Natalia Sánchez Rocafort

Cyber Security Analyst

Dec 2023

Securing Microsoft Teams and SharePoint

Given the prevalence of the Microsoft Teams and Microsoft SharePoint platforms in the workplace in recent years, it is essential that organizations stay vigilant to the threat posed by applications vital to hybrid and remote work and prioritize the security and cyber hygiene of these services. For just as the use of these platforms has increased exponentially with the rise of remote and hybrid working, so too has the malicious use of them to deliver malware to unassuming users.

Researchers across the threat landscape have begun to observe these legitimate services being leveraged by malicious actors as an initial access method. Microsoft Teams can easily be exploited to send targeted phishing messages to individuals within an organization, while appearing legitimate and safe. Although the exact contents of these messages may vary, the messages frequently use social engineering techniques to lure users to click on a SharePoint link embedded into the message. Interacting with the malicious link will then download a payload [1].

Darktrace observed one such malicious attempt to use Microsoft Teams and SharePoint in September 2023, when a device was observed downloading DarkGate, a commercial trojan that is known to deploy other strains of malware, also referred to as a commodity loader [2], after clicking on SharePoint link. Fortunately for the customer, Darktrace’s suite of products was perfectly poised to identify the initial signs of suspicious activity and Darktrace RESPOND™ was able to immediately halt the advancement of the attack.

DarkGate Attack Overview

On September 8, 2023, Darktrace DETECT™ observed around 30 internal devices on a customer network making unusual SSL connections to an external SharePoint site which contained the name of a person, 'XXXXXXXX-my.sharepoint[.]com' (107.136[.]8, 13.107.138[.]8). The organization did not have any employees who went by this name and prior to this activity, no internal devices had been seen contacting the endpoint.

At first glance, this initial attack vector would have appeared subtle and seemingly trustworthy to users. Malicious actors likely sent various users a phishing message via Microsoft Teams that contained the spoofed SharePoint link to the personalized SharePoint link ''XXXXXXXX-my.sharepoint[.]com'.

Figure 1: Advanced Search query showing a sudden spike in connections to ''XXXXXXXX -my.sharepoint[.]com'.

Darktrace observed around 10 devices downloading approximately 1 MB of data during their connections to the Sharepoint endpoint. Darktrace DETECT observed some of the devices making subsequent HTTP GET requests to a range of anomalous URIs. The devices utilized multiple user-agents for these connections, including ‘curl’, a command line tool that allows individuals to request and transfer data from a specific URL. The connections were made to the IP 5.188.87[.]58, an endpoint that has been flagged as an indicator of compromise (IoC) for DarkGate malware by multiple open-source intelligence (OSINT) sources [3], commonly associated with HTTP GET requests:

GET request over port 2351 with the User-Agent header 'Mozilla/4.0 (compatible; Win32; WinHttp.WinHttpRequest.5)' and the target URI '/bfyxraav' to 5.188.87[.]58
GET request over port 2351 with the user-agent header 'curl' and the target URI '/' to 5.188.87[.]58
GET request over port 2351 with the user-agent header 'curl/8.0.1' and the target URI '/msibfyxraav' to 5.188.87[.]58

The HTTP GET requests made with the user-agent header 'curl' and the target URI '/' to 5.188.87[.]58 were responded to with a filename called 'Autoit3.exe'. The other requests received script files with names ending in '.au3, such as 'xkwtvq.au3', 'otxynh.au3', and 'dcthbq.au3'. DarkGate malware has been known to make use of legitimate AutoIt files, and typically runs multiple AutoIt scripts (‘.au3’) [4].

Following these unusual file downloads, the devices proceeded to make hundreds of HTTP POST requests to the target URI '/' using the user-agent header 'Mozilla/4.0 (compatible; Synapse)' to 5.188.87[.]58. The contents of these requests, along with the contents of the responses, appear to be heavily obfuscated.

Figure 2: Example of obfuscated response, as shown in a packet capture downloaded from Darktrace.

While Microsoft’s Safe Attachments and Safe Links settings were unable to detect this camouflaged malicious activity, Darktrace DETECT observed the unusual over-the-network connectivity that occurred. While Darktrace DETECT identified multiple internal devices engaging in this anomalous behavior throughout the course of the compromise, the activity observed on one device in particular best showcases the overall kill chain of this attack.

The device in question was observed using two different user agents (curl/8.0.1 and Mozilla/4.0 (compatible; Win32; WinHttp.WinHttpRequest.5)) when connecting to the endpoint 5.188.87[.]58 and target URI ‘/bfyxraav’. Additionally, Darktrace DETECT recognized that it was unusual for this device to be making these HTTP connections via destination port 2351.

As a result, Darktrace’s Cyber AI Analyst™ launched an autonomous investigation into the suspicious activity and was able to connect the unusual external connections together, viewing them as one beaconing incident as opposed to isolated series of connections.

Figure 3: Cyber AI Analyst investigation summarizing the unusual repeated connections made to 5.188.87[.]58 via destination port 2351.

Darktrace then observed the device downloading the ‘Autoit3.exe’ file. Darktrace RESPOND took swift mitigative action by blocking similar connections to this endpoint, preventing the device from downloading any additional suspicious files.

Figure 4: Suspicious ‘Autoit3.exe’ downloaded by the source device from the malicious external endpoint.

Just one millisecond later, Darktrace observed the device making suspicious HTTP GET requests to URIs including ‘/msibfyxraav’. Darktrace recognized that the device had carried out several suspicious actions within a relatively short period of time, breaching multiple DETECT models, indicating that it may have been compromised. As a result, RESPOND took action against the offending device by preventing it from communicating externally [blocking all outbound connections] for a period of one hour, allowing the customer’s security team precious time to address the issue.

It should be noted that, at this point, had the customer subscribed to Darktrace’s Proactive Threat Notification (PTN) service, the Darktrace Security Operations Center (SOC) would have investigated these incidents in greater detail, and likely would have sent a notification directly to the customer to inform them of the suspicious activity.

Additionally, AI Analyst collated various distinct events and suggested that these stages were linked as part of an attack. This type of augmented understanding of events calculated at machine speed is extremely valuable since it likely would have taken a human analyst hours to link all the facets of the incident together.

Figure 5: AI Analyst investigation showcasing the use of the ‘curl’ user agent to connect to the target URI ‘/msibfyxraav’.

Figure 6: Darktrace RESPOND moved to mitigate any following connections by blocking all outgoing traffic for 1 hour.

Following this, an automated investigation was launched by Microsoft Defender for Endpoint. Darktrace is designed to coordinate with multiple third-party security tools, allowing for information on ongoing incidents to be seamlessly exchanged between Darktrace and other security tools. In this instance, Microsoft Defender identified a ‘low severity’ incident on the device, this automatically triggered a corresponding alert within DETECT, presented on the Darktrace Threat Visuallizer.

The described activity occurred within milliseconds. At each step of the attack, Darktrace RESPOND took action either by enforcing expected patterns of life [normality] on the affected device, blocking connections to suspicious endpoints for a specified amount of time, and/or blocking all outgoing traffic from the device. All the relevant activity was detected and promptly stopped for this device, and other compromised devices, thus containing the compromise and providing the security team invaluable remediation time.

Figure 7: Overview of the compromise activity, all of which took place within a matter of miliseconds.

Darktrace identified similar activity on other devices in this customer’s network, as well as across Darktrace’s fleet around the same time in early September.

On a different customer environment, Darktrace DETECT observed more than 25 ‘.au3’ files being downloaded; this activity can be seen in Figure 9.

Figure 8: High volume of file downloads following GET request and 'curl' commands.

Figure 9 provides more details of this activity, including the source and destination IP addresses (5.188.87[.]58), the destination port, the HTTP method used and the MIME/content-type of the file

Figure 9: Additional information of the anomalous connections.

A compromised server in another customer deployment was seen establishing unusual connections to the external IP address 80.66.88[.]145 – an endpoint that has been associated with DarkGate by OSINT sources [5]. This activity was identified by Darktrace/DETECT as a new connection for the device via an unusual destination port, 2840. As the device in question was a critical server, Darktrace DETECT treated it with suspicion and generated an ‘Anomalous External Activity from Critical Network Device’ model breach.

Figure 10: Model breach and model breach event log for suspicious connections to additional endpoint.

Conclusion

While Microsoft Teams and SharePoint are extremely prominent tools that are essential to the business operations of many organizations, they can also be used to compromise via living off the land, even at initial intrusion. Any Microsoft Teams user within a corporate setting could be targeted by a malicious actor, as such SharePoint links from unknown senders should always be treated with caution and should not automatically be considered as secure or legitimate, even when operating within legitimate Microsoft infrastructure.

Malicious actors can leverage these commonly used platforms as a means to carry out their cyber-attacks, therefore organizations must take appropriate measures to protect and secure their digital environments. As demonstrated here, threat actors can attempt to deploy malware, like DarkGate, by targeting users with spoofed Microsoft Teams messages. By masking malicious links as legitimate SharePoint links, these attempts can easily convince targets and bypass traditional security tools and even Microsoft’s own Safe Links and Safe Attachments security capabilities.

When the chain of events of an attack escalates within milliseconds, organizations must rely on AI-driven tools that can quickly identify and automatically respond to suspicious events without latency. As such, the value of Darktrace DETECT and Darktrace RESPOND cannot be overstated. Given the efficacy and efficiency of Darktrace’s detection and autonomous response capabilities, a more severe network compromise in the form of the DarkGate commodity loader was ultimately averted.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Zoe Tilsiter.

Appendices

Darktrace DETECT Model Detections

[Model Breach: Device / Initial Breach Chain Compromise 100% –– Breach URI: /#modelbreach/114039 ] (Enhanced Monitoring)· [Model Breach: Device / Initial Breach Chain Compromise 100% –– Breach URI: /#modelbreach/114124 ] (Enhanced Monitoring)
[Model Breach: Device / New User Agent and New IP 62% –– Breach URI: /#modelbreach/114030 ]
[Model Breach: Anomalous Connection / Application Protocol on Uncommon Port 46% –– Breach URI: /#modelbreach/114031 ]
[Model Breach: Anomalous Connection / New User Agent to IP Without Hostname 62% –– Breach URI: /#modelbreach/114032 ]
[Model Breach: Device / New User Agent 32% –– Breach URI: /#modelbreach/114035 ]
[Model Breach: Device / Three Or More New User Agents 31% –– Breach URI: /#modelbreach/114036 ]
[Model Breach: Anomalous Server Activity / Anomalous External Activity from Critical Network Device 62% –– Breach URI: /#modelbreach/612173 ]
[Model Breach: Anomalous File / EXE from Rare External Location 61% –– Breach URI: /#modelbreach/114037 ]
[Model Breach: Anomalous Connection / Multiple Connections to New External TCP Port 61% –– Breach URI: /#modelbreach/114042 ]
[Model Breach: Security Integration / Integration Ransomware Detected 100% –– Breach URI: /#modelbreach/114049 ]
[Model Breach: Compromise / Beaconing Activity To External Rare 62% –– Breach URI: /#modelbreach/114059 ]
[Model Breach: Compromise / HTTP Beaconing to New Endpoint 30% –– Breach URI: /#modelbreach/114067 ]
[Model Breach: Security Integration / C2 Activity and Integration Detection 100% –– Breach URI: /#modelbreach/114069 ]
[Model Breach: Anomalous File / EXE from Rare External Location 55% –– Breach URI: /#modelbreach/114077 ]
[Model Breach: Compromise / High Volume of Connections with Beacon Score 66% –– Breach URI: /#modelbreach/114260 ]
[Model Breach: Security Integration / Low Severity Integration Detection 59% –– Breach URI: /#modelbreach/114293 ]
[Model Breach: Security Integration / Low Severity Integration Detection 33% –– Breach URI: /#modelbreach/114462 ]
[Model Breach: Security Integration / Integration Ransomware Detected 100% –– Breach URI: /#modelbreach/114109 ]· [Model Breach: Device / Three Or More New User Agents 31% –– Breach URI: /#modelbreach/114118 ]· [Model Breach: Anomalous Connection / Application Protocol on Uncommon Port 46% –– Breach URI: /#modelbreach/114113 ] · [Model Breach: Anomalous Connection / New User Agent to IP Without Hostname 62% –– Breach URI: /#modelbreach/114114 ]· [Model Breach: Device / New User Agent 32% –– Breach URI: /#modelbreach/114117 ]· [Model Breach: Anomalous File / EXE from Rare External Location 61% –– Breach URI: /#modelbreach/114122 ]· [Model Breach: Security Integration / Low Severity Integration Detection 54% –– Breach URI: /#modelbreach/114310 ]
[Model Breach: Security Integration / Integration Ransomware Detected 65% –– Breach URI: /#modelbreach/114662 ]Darktrace/Respond Model Breaches
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious File Block 61% –– Breach URI: /#modelbreach/114033 ]
[Model Breach: Antigena / Network::External Threat::Antigena File then New Outbound Block 100% –– Breach URI: /#modelbreach/114038 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block 100% –– Breach URI: /#modelbreach/114040 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block 87% –– Breach URI: /#modelbreach/114041 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach 87% –– Breach URI: /#modelbreach/114043 ]
[Model Breach: Antigena / Network::External Threat::Antigena Ransomware Block 100% –– Breach URI: /#modelbreach/114052 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Significant Security Integration and Network Activity Block 87% –– Breach URI: /#modelbreach/114070 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block 87% –– Breach URI: /#modelbreach/114071 ]
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious Activity Block 87% –– Breach URI: /#modelbreach/114072 ]
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious File Block 53% –– Breach URI: /#modelbreach/114079 ]
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block 64% –– Breach URI: /#modelbreach/114539 ]
[Model Breach: Antigena / Network::External Threat::Antigena Ransomware Block 66% –– Breach URI: /#modelbreach/114667 ]
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious Activity Block 79% –– Breach URI: /#modelbreach/114684 ]·
[Model Breach: Antigena / Network::External Threat::Antigena Ransomware Block 100% –– Breach URI: /#modelbreach/114110 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block 87% –– Breach URI: /#modelbreach/114111 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach 87% –– Breach URI: /#modelbreach/114115 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block 87% –– Breach URI: /#modelbreach/114116 ]·
[Model Breach: Antigena / Network::External Threat::Antigena Suspicious File Block 61% –– Breach URI: /#modelbreach/114121 ]·
[Model Breach: Antigena / Network::External Threat::Antigena File then New Outbound Block 100% –– Breach URI: /#modelbreach/114123 ]·
[Model Breach: Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block 100% –– Breach URI: /#modelbreach/114125 ]

List of IoCs

IoC - Type - Description + Confidence

5.188.87[.]58 - IP address - C2 endpoint

80.66.88[.]145 - IP address - C2 endpoint

/bfyxraav - URI - Possible C2 endpoint URI

/msibfyxraav - URI - Possible C2 endpoint URI

Mozilla/4.0 (compatible; Win32; WinHttp.WinHttpRequest.5) - User agent - Probable user agent leveraged

curl - User agent - Probable user agent leveraged

curl/8.0.1 - User agent - Probable user agent leveraged

Mozilla/4.0 (compatible; Synapse) - User agent - Probable user agent leveraged

Autoit3.exe - Filename - Exe file

CvUYLoTv.au3

eDVeqcCe.au3

FeLlcFRS.au3

FTEZlGhe.au3

HOrzcEWV.au3

rKlArXHH.au3

SjadeWUz.au3

ZgOLxJQy.au3

zSrxhagw.au3

ALOXitYE.au3

DKRcfZfV.au3

gQZVKzek.au3

JZrvmJXK.au3

kLECCtMw.au3

LEXCjXKl.au3

luqWdAzF.au3

mUBNrGpv.au3

OoCdHeJT.au3

PcEJXfIl.au3

ssElzrDV.au3

TcBwRRnp.au3

TFvAUIgu.au3

xkwtvq.au3

otxynh.au3

dcthbq.au3 - Filenames - Possible exe files delivered in response to curl/8.0.1 GET requests with Target URI '/msibfyxraav

f3a0a85fe2ea4a00b3710ef4833b07a5d766702b263fda88101e0cb804d8c699 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

afa3feea5964846cd436b978faa7d31938e666288ffaa75d6ba75bfe6c12bf61 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

63aeac3b007436fa8b7ea25298362330423b80a4cb9269fd2c3e6ab1b1289208 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

ab6704e836a51555ec32d1ff009a79692fa2d11205f9b4962121bda88ba55486 - SHA256 file hash - Possible SHA256 hashes of 'Autoit3.exe' files

References

1. https://www.truesec.com/hub/blog/darkgate-loader-delivered-via-teams

2. https://feedit.cz/wp-content/uploads/2023/03/YiR2022_onepager_ransomware_loaders.pdf

‍3. https://www.virustotal.com/gui/ip-address/5.188.87[.]58

‍4. https://www.forescout.com/resources/darkgate-loader-malspam-campaign/

‍5. https://otx.alienvault.com/indicator/ip/80.66.88[.]145

Written by

Natalia Sánchez Rocafort

Cyber Security Analyst

Inside the SOC

Written by

Natalia Sánchez Rocafort

Cyber Security Analyst

•

April 17, 2025

Jeffrey Macre

Industrial Security Solutions Architect

•

April 16, 2025

Margaret Cunningham, PhD

Director, Security & AI Strategy, Field CISO

Watch the NIS2 Webinar

Blog

April 24, 2025

The Importance of NDR in Resilient XDR

As threat actors become more adept at targeting and disabling EDR agents, relying solely on endpoint detection leaves critical blind spots.

Network detection and response (NDR) offers the visibility and resilience needed to catch what EDR can’t especially in environments with unmanaged devices or advanced threats that evade local controls.

This blog explores how threat actors can disable or bypass EDR-based XDR solutions and demonstrates how Darktrace’s approach to NDR closes the resulting security gaps with Self-Learning AI that enables autonomous, real-time detection and response.

Threat actors see local security agents as targets

Recent research by security firms has highlighted ‘EDR killers’: tools that deliberately target EDR agents to disable or damage them. These include the known malicious tool EDRKillShifter, the open source EDRSilencer, EDRSandblast and variants of Terminator, and even the legitimate business application HRSword.

The attack surface of any endpoint agent is inevitably large, whether the software is challenged directly, by contesting its local visibility and access mechanisms, or by targeting the Operating System it relies upon. Additionally, threat actors can readily access and analyze EDR tools, and due to their uniformity across environments an exploit proven in a lab setting will likely succeed elsewhere.

Sophos have performed deep research into the EDRShiftKiller tool, which ESET have separately shown became accessible to multiple threat actor groups. Cisco Talos have reported via TheRegister observing significant success rates when an EDR kill was attempted by ransomware actors.

With the local EDR agent silently disabled or evaded, how will the threat be discovered?

What are the limitations of relying solely on EDR?

Cyber attackers will inevitably break through boundary defences, through innovation or trickery or exploiting zero-days. Preventive measures can reduce but not completely stop this. The attackers will always then want to expand beyond their initial access point to achieve persistence and discover and reach high value targets within the business. This is the primary domain of network activity monitoring and NDR, which includes responsibility for securing the many devices that cannot run endpoint agents.

In the insights from a CISA Red Team assessment of a US CNI organization, the Red Team was able to maintain access over the course of months and achieve their target outcomes. The top lesson learned in the report was:

“The assessed organization had insufficient technical controls to prevent and detect malicious activity. The organization relied too heavily on host-based endpoint detection and response (EDR) solutions and did not implement sufficient network layer protections.”

This proves that partial, isolated viewpoints are not sufficient to track and analyze what is fundamentally a connected problem – and without the added visibility and detection capabilities of NDR, any downstream SIEM or MDR services also still have nothing to work with.

Why is network detection & response (NDR) critical?

An effective NDR finds threats that disable or can’t be seen by local security agents and generally operates out-of-band, acquiring data from infrastructure such as traffic mirroring from physical or virtual switches. This means that the security system is extremely inaccessible to a threat actor at any stage.

An advanced NDR such as Darktrace / NETWORK is fully capable of detecting even high-end novel and unknown threats.

Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK

On January 9th 2025, two new vulnerabilities were disclosed in Ivanti Connect Secure and Policy Secure appliances that were under malicious exploitation. Perimeter devices, like Ivanti VPNs, are designed to keep threat actors out of a network, so it's quite serious when these devices are vulnerable.

An NDR solution is critical because it provides network-wide visibility for detecting lateral movement and threats that an EDR might miss, such as identifying command and control sessions (C2) and data exfiltration, even when hidden within encrypted traffic and which an EDR alone may not detect.

Darktrace initially detected suspicious activity connected with the exploitation of CVE-2025-0282 on December 29, 2024 – 11 days before the public disclosure of the vulnerability, this early detection highlights the benefits of an anomaly-based network detection method.

Throughout the campaign and based on the network telemetry available to Darktrace, a wide range of malicious activities were identified, including the malicious use of administrative credentials, the download of suspicious files, and network scanning in the cases investigated.

Darktrace / NETWORK’s autonomous response capabilities played a critical role in containment by autonomously blocking suspicious connections and enforcing normal behavior patterns. At the same time, Darktrace Cyber AI Analyst™ automatically investigated and correlated the anomalous activity into cohesive incidents, revealing the full scope of the compromise.

This case highlights the importance of real-time, AI-driven network monitoring to detect and disrupt stealthy post-exploitation techniques targeting unmanaged or unprotected systems.

Unlocking adaptive protection for evolving cyber risks

Darktrace / NETWORK uses unique AI engines that learn what is normal behavior for an organization’s entire network, continuously analyzing, mapping and modeling every connection to create a full picture of your devices, identities, connections, and potential attack paths.

With its ability to uncover previously unknown threats as well as detect known threats using signatures and threat intelligence, Darktrace is an essential layer of the security stack. Darktrace has helped secure customers against attacks including 2024 threat actor campaigns against Fortinet’s FortiManager , Palo Alto firewall devices, and more.

Stay tuned for part II of this series which dives deeper into the differences between NDR types.

‍

Credit to Nathaniel Jones VP, Security & AI Strategy, FCISO & Ashanka Iddya, Senior Director of Product Marketing for their contribution to this blog.

About the author

Nathaniel Jones

VP, Security & AI Strategy, Field CISO

Blog

April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

man looking at multiple computer screens

Out of all the services honeypotted by Darktrace, Docker is the most commonly attacked, with new strains of malware emerging daily. This blog will analyze a novel malware campaign with a unique obfuscation technique and a new cryptojacking technique.

What is obfuscation?

Obfuscation is a common technique employed by threat actors to prevent signature-based detection of their code, and to make analysis more difficult. This novel campaign uses an interesting technique of obfuscating its payload.

Docker image analysis

The attack begins with a request to launch a container from Docker Hub, specifically the kazutod/tene:ten image. Using Docker Hub’s layer viewer, an analyst can quickly identify what the container is designed to do. In this case, the container is designed to run the ten.py script which is built into itself.

Docker Hub Image Layers, referencing the script ten.py. — Figure 1: Docker Hub Image Layers, referencing the script *ten.py*.

To gain more information on the Python file, Docker’s built in tooling can be used to download the image (docker pull kazutod/tene:ten) and then save it into a format that is easier to work with (docker image save kazutod/tene:ten -o tene.tar). It can then be extracted as a regular tar file for further investigation.

Figure 2: Extraction of the resulting tar file.

The Docker image uses the OCI format, which is a little different to a regular file system. Instead of having a static folder of files, the image consists of layers. Indeed, when running the file command over the sha256 directory, each layer is shown as a tar file, along with a JSON metadata file.

Figure 3: Output of the file command over the sha256 directory.

As the detailed layers are not necessary for analysis, a single command can be used to extract all of them into a single directory, recreating what the container file system would look like:

find blobs/sha256 -type f -exec sh -c 'file "{}" | grep -q "tar archive" && tar -xf "{}" -C root_dir' \;

Figure 4: Result of running the command above.

The find command can then be used to quickly locate where the ten.py script is.

find root_dir -name ten.py

root_dir/app/ten.py

Details of the above ten.py script. — Figure 5: Details of the above *ten.py* script.

This may look complicated at first glance, however after breaking it down, it is fairly simple. The script defines a lambda function (effectively a variable that contains executable code) and runs zlib decompress on the output of base64 decode, which is run on the reversed input. The script then runs the lambda function with an input of the base64 string, and then passes it to exec, which runs the decoded string as Python code.

To help illustrate this, the code can be cleaned up to this simplified function:

def decode(input):
reversed = input[::-1]

decoded = base64.decode(reversed)
decompressed = zlib.decompress(decoded)
return decompressed

decoded_string = decode(the_big_text_blob)
exec(decoded_string) # run the decoded string

This can then be set up as a recipe in Cyberchef, an online tool for data manipulation, to decode it.

Use of Cyberchef to decode the ten.py script. — Figure 6: Use of Cyberchef to decode the *ten.py* script.

The decoded payload calls the decode function again and puts the output into exec. Copy and pasting the new payload into the input shows that it does this another time. Instead of copy-pasting the output into the input all day, a quick script can be used to decode this.

The script below uses the decode function from earlier in order to decode the base64 data and then uses some simple string manipulation to get to the next payload. The script will run this over and over until something interesting happens.

# Decode the initial base64

decoded = decode(initial)
# Remove the first 11 characters and last 3

# so we just have the next base64 string

clamped = decoded[11:-3]

for i in range(1, 100):
# Decode the new payload

decoded = decode(clamped)
# Print it with the current step so we

# can see what’s going on

print(f"Step {i}")

print(decoded)
# Fetch the next base64 string from the

# output, so the next loop iteration will

# decode it

clamped = decoded[11:-3]

Figure 7: Result of the 63rd iteration of this script.

After 63 iterations, the script returns actual code, accompanied by an error from the decode function as a stopping condition was never defined. It not clear what the attacker’s motive to perform so many layers of obfuscation was, as one round of obfuscation versus several likely would not make any meaningful difference to bypassing signature analysis. It’s possible this is an attempt to stop analysts or other hackers from reverse engineering the code. However, it took a matter of minutes to thwart their efforts.

Cryptojacking 2.0?

Figure 8: Cleaned up version of the de-obfuscated code.

The cleaned up code indicates that the malware attempts to set up a connection to teneo[.]pro, which appears to belong to a Web3 startup company.

Teneo appears to be a legitimate company, with Crunchbase reporting that they have raised USD 3 million as part of their seed round [1]. Their service allows users to join a decentralized network, to “make sure their data benefits you” [2]. Practically, their node functions as a distributed social media scraper. In exchange for doing so, users are rewarded with “Teneo Points”, which are a private crypto token.

The malware script simply connects to the websocket and sends keep-alive pings in order to gain more points from Teneo and does not do any actual scraping. Based on the website, most of the rewards are gated behind the number of heartbeats performed, which is likely why this works [2].

Checking out the attacker’s dockerhub profile, this sort of attack seems to be their modus operandi. The most recent container runs an instance of the nexus network client, which is a project to perform distributed zero-knowledge compute tasks in exchange for cryptocurrency.

Typically, traditional cryptojacking attacks rely on using XMRig to directly mine cryptocurrency, however as XMRig is highly detected, attackers are shifting to alternative methods of generating crypto. Whether this is more profitable remains to be seen. There is not currently an easy way to determine the earnings of the attackers due to the more “closed” nature of the private tokens. Translating a user ID to a wallet address does not appear to be possible, and there is limited public information about the tokens themselves. For example, the Teneo token is listed as “preview only” on CoinGecko, with no price information available.

Conclusion

This blog explores an example of Python obfuscation and how to unravel it. Obfuscation remains a ubiquitous technique employed by the majority of malware to aid in detection/defense evasion and being able to de-obfuscate code is an important skill for analysts to possess.

We have also seen this new avenue of cryptominers being deployed, demonstrating that attackers’ techniques are still evolving - even tried and tested fields. The illegitimate use of legitimate tools to obtain rewards is an increasingly common vector. For example, as has been previously documented, 9hits has been used maliciously to earn rewards for the attack in a similar fashion.

Docker remains a highly targeted service, and system administrators need to take steps to ensure it is secure. In general, Docker should never be exposed to the wider internet unless absolutely necessary, and if it is necessary both authentication and firewalling should be employed to ensure only authorized users are able to access the service. Attacks happen every minute, and even leaving the service open for a short period of time may result in a serious compromise.

References

1. https://www.crunchbase.com/funding_round/teneo-protocol-seed--a8ff2ad4

2. https://teneo.pro/

About the author

Nate Bill

Threat Researcher

Your data. Our AI.

Elevate your network security with Darktrace AI

Get a demo

Check out this article by Darktrace: How Darktrace Halted A DarkGate in MS Teams

How Darktrace Halted A DarkGate in MS Teams

Securing Microsoft Teams and SharePoint

DarkGate Attack Overview

Conclusion

Appendices

Darktrace DETECT Model Detections

List of IoCs

References

Why Asset Visibility and Signature-Based Threat Detection Fall Short in ICS Security

Introducing Version 2 of Darktrace’s Embedding Model for Investigation of Security Threats (DEMIST-2)

Enjoying the blog?

More in this series

Bytesize Security: Insider Threats in Google Workspace

Behind the Veil: Darktrace's Detection of VPN Exploitation in SaaS Environments

Thread Hijacking: Infiltration Tactics Explained

Blog

April 24, 2025

The Importance of NDR in Resilient XDR

Threat actors see local security agents as targets

What are the limitations of relying solely on EDR?

Why is network detection & response (NDR) critical?

Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK

Unlocking adaptive protection for evolving cyber risks

Blog

April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

What is obfuscation?

Docker image analysis

Cryptojacking 2.0?

Conclusion

References