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April 29, 2026

Darktrace Malware Analysis: Jenkins Honeypot Reveals Emerging Botnet Targeting Online Games

Darktrace analysts observed attackers exploiting a Jenkins honeypot to deploy a new DDoS botnet targeting video game servers. Leveraging Jenkins scriptText abuse, the malware installs a multi-platform payload, evades detection, and launches UDP, TCP, and application-layer attacks, highlighting ongoing risks from opportunistic botnet activity across internet-facing environments.
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
Nathaniel Bill
Malware Research Engineer
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29
Apr 2026

DDoS Botnet discovery

To observe adversary behavior in real time, Darktrace operates a global honeypot network known as “CloudyPots”, designed to capture malicious activity across a wide range of services, protocols, and cloud platforms. These honeypots provide valuable insights into the techniques, tools, and malware actively targeting internet‑facing infrastructure.

How attackers used a Jenkins honeypot to deploy the botnet

One such software honeypotted by Darktrace is Jenkins, a CI build system that allows developers to build code and run tests automatically. The instance of Jenkins in Darktrace’s honeypot is intentionally configured with a weak password, allowing attackers to obtain remote code execution on the service.

In one instance observed by Darktrace on March 18, 2026, a threat actor seemingly attempted to target Darktrace’s Jenkins honeypot to deploy a distributed denial-of-service (DDoS) botnet. Further analysis by Darktrace’s Threat Research team revealed the botnet was intended to specifically target video game servers.

How the Jenkins scriptText endpoint was used for remote code execution

The Jenkins build system features an endpoint named scriptText, which enables users to programmatically send new jobs, in the form of a Groovy script. Groovy is a programming language with similar syntax to Java and runs using the Java Virtual Machine (JVM). An attacker can abuse the scriptText endpoint to run a malicious script, achieving code execution on the victim host.

Request sent to the scriptText endpoint containing the malicious script.
Figure 1: Request sent to the scriptText endpoint containing the malicious script.

The malicious script is sent using the form-data content type, which results in the contents of the script being URL encoded. This encoding can be decoded to recover the original script, as shown in Figure 2, where Darktrace Analysts decoded the script using CyberChef,

The malicious script decoded using CyberChef.
Figure 2: The malicious script decoded using CyberChef.

What happens after Jenkins is compromised

As Jenkins can be deployed on both Microsoft Windows and Linux systems, the script includes separate branches to target each platform.

In the case of Windows, the script performs the following actions:

  • Downloads a payload from 103[.]177.110.202/w.exe and saves it to C:\Windows\Temp\update.dat.
  • Renames the “update.dat” file to “win_sys.exe” (within the same folder)
  • Runs the Unblock-File command is used to remove security restrictions typically applied to files downloaded from the internet.
  • Adds a firewall allow rule is added for TCP port 5444, which the payload uses for command-and-control (C2) communications.

On Linux systems, the script will instead use a Bash one-liner to download the payload from 103[.]177.110.202/bot_x64.exe to /tmp/bot and execute it.

Why this botnet uses a single IP for delivery and command and control

The IP 103[.]177.110.202 belongs to Webico Company Limited, specifically its Tino brand, a Vietnamese company that offers domain registrar services and server hosting. Geolocation data indicates that the IP is located in Ho Chi Minh City. Open-source intelligence (OSINT) analysis revealed multiple malicious associations tied to the IP [1].

Darktrace’s analysis found that the IP 103[.]177.110.202 is used for multiple stages of an attack, including spreading and initial access, delivering payloads, and C2 communication. This is an unusual combination, as many malware families separate their spreading servers from their C2 infrastructure. Typically, malware distribution activity results in a high volume of abuse complaints, which may result in server takedowns or service suspension by internet providers. Separate C2 infrastructure ensures that existing infections remain controllable even if the spreading server is disrupted.

How the malware evades detection and maintains persistence

Analysis of the Linux payload (bot _x64)

The sample begins by setting the environmental variables BUILD_ID and JENKINS_NODE_COOKIE to “dontKillMe”. By default, Jenkins terminates long-running scripts after a defined timeout period; however, setting these variables to “dontKillMe” bypasses this check, allowing the script to continue running uninterrupted.

The script then performs several stealth behaviors to evade detection. First, it deletes the original executable from disk and then renames itself to resemble the legitimate kernel processes “ksoftirqd/0” or “kworker”, which are found on Linux installations by default. It then uses a double fork to daemonize itself, enabling it to run in the background, before redirecting standard input, standard output, and standard error to /dev/null, hiding any logging from the malware. Finally, the script creates a signal handler for signals such as SIGTERM, causing them to be ignored and making it harder to stop the process.

Stealth component of the main function
Figure 3: Stealth component of the main function

How the botnet communicates with command and control (C2)

The sample then connects to the C2 server and sends the detected architecture of the system on which the agent was installed. The malware then enters a loop to handle incoming commands.

The sample features two types of commands, utility commands used to manage the malware, and commands to trigger attacks. Three special commands are defined: “PING” (which replies with PONG as a keep-alive mechanism), “!stop” which causes the malware to exit, and “!update”, which triggers the malware to download a new version from the C2 server and restart itself.

Initial connection to the C2 sever.
Figure 4: Initial connection to the C2 sever.

What DDoS attack techniques this botnet uses

The attack commands consist of the following:

Many of these commands invoke the same function despite appearing to be different attack techniques. For example, specialized attacks such as Cloudflare bypass (cfbypass, uam) use the exact same function as a standard HTTP attack. This may indicate the threat actor is attempting to make the botnet look like it has more capabilities than it actually has, or it could suggest that these commands are placeholders for future attack functionality that has yet to be implemented

All the commands take three arguments: IP, port to attack, and the duration of the attack.

attack_udp and attack_udp_pps

The attack_udp and attack_udp_pps functions both use a basic loop and sendto system call to send UDP packets to the victim’s IP, either targeting a predetermined port or a random port. The attack_udp function sends packets with 1,450 bytes of data, aimed at bandwidth saturation, while the attack_udp_pps function sends smaller 64-byte packets. In both cases, the data body of the packet consists of entirely random data.

Code for the UDP attack method
Figure 5: Code for the UDP attack method

attack_dayz

The attack_dayz function follows a similar structure to the attack_udp function; however, instead of sending random data, it will instead send a TSource Engine Query. This command is specific to Valve Source Engine servers and is designed to return a large volume of data about the targeted server. By repeatedly flooding this request, an attacker can exhaust the resources of a server using a comparatively small amount of data.

The Valve Source Engine server, also called Source Engine Dedicated server, is a server developed by video game company Valve that enables multiplayer gameplay for titles built using the Source game engine, which is also developed by Valve. The Source engine is used in games such as Counterstrike and Team Fortress 2. Curiously, the function attack_dayz, appears to be named after another popular online multiplayer game, DayZ; however, DayZ does not use the Valve Source Engine, making it unclear why this name was chosen.

The code for the “attack_dayz” attack function.
Figure 6: The code for the attack_dayz” attack function.

attack_tcp_push

The attack_tcp_push function establishes a TCP socket with the non-blocking flag set, allowing it to rapidly call functions such as connect() and send() without waiting for their completion. For the duration of the attack, it enters a while loop in which it repeatedly connects to the victim, sends 1,024 bytes of random data, and then closes the connection. This process repeats until the attack duration ends. If the mode flag is set to 1, the function also configures the socket with TCP no-delay enabled, allowing for packets to be sent immediately without buffering, resulting in a higher packet rate and a more effective attack.

The code for the TCP attack function.
Figure 7: The code for the TCP attack function.

attack_http

Similar to attach_tcp_push, attack_http configures a socket with no-delay enabled and non-blocking set. After establishing the connection, it sends 64 HTTP GET requests before closing the socket.

The code for the HTTP attack function.
Figure 8: The code for the HTTP attack function.

attack_special

The attack_special function creates a UDP socket and sets the port and payload based on the value of the mode flag:

  • Mode 0: Port 53 (DNS), sending a 10-byte malformed data packet.
  • Mode 1: Port 27015 (Valve Source Engine), sending the previously observed TSource Engine Query packet.
  • Mode 2: Port 123 (NTP), sending the start of an NTP control request.
The code for the attack_special function.
Figure 9: The code for the attack_special function.

What this botnet reveals about opportunistic attacks on internet-facing systems

Jenkins is one of the less frequently exploited services honeypotted by Darktrace, with only a handful campaigns observed. Nonetheless, the emergence of this new DDoS botnet demonstrates that attackers continue to opportunistically exploit any internet-facing misconfiguration at scale to grow the botnet strength.

While the hosts most commonly affected by these opportunistic attacks are usually “lower-value” systems, this distinction is largely irrelevant for botnets, where numbers alone are more important to overall effectiveness

The presence of game-specific DoS techniques further highlights that the gaming industry continues to be extensively targeted by cyber attackers, with Cloudflare reporting it as the fourth most targeted industry [2]. This botnet has likely already been used against game servers, serving as a reminder for server operators to ensure appropriate mitigations are in place.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Content Manager)

Indicators of Compromise (IoCs)

103[.]177.110.202 - Attacker and command-and-control IP

F79d05065a2ba7937b8781e69b5859d78d5f65f01fb291ae27d28277a5e37f9b – bot_x64

References

[1] https://www.virustotal.com/gui/url/86db2530298e6335d3ecc66c2818cfbd0a6b11fcdfcb75f575b9fcce1faa00f1/detection

[2] - https://blog.cloudflare.com/ddos-threat-report-2025-q4/

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
Nathaniel Bill
Malware Research Engineer

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August 26, 2026

AI Agents: Securing the Path from Intent to Action

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The UK’s National Cyber Security Centre (NCSC) recently published guidance on managing the cyber risk of agentic AI. While the document is framed as interim advice as more formal guidance is developed, the framing reflects the current state of the industry: organizations are already deploying agents into production environments while standards, controls, and operating models for autonomous systems remain unsettled. Governance is evolving alongside adoption rather than preceding it, a reality which underscores the importance of robust controls.  

The NCSC’s guidance recommends aligning controls to an agent's level of autonomy, assigning distinct identities, limiting permissions, constraining access to systems and data, monitoring activity, maintaining human oversight, and preserving the ability to intervene when necessary. Most of these recommendations will sound familiar to security teams. The challenge is not the novelty of the controls. It is the type of system those controls now need to govern.

The shift from model security to agent security

For several years, AI security discussions have focused heavily on models. Can a model be manipulated? Jailbroken? Trusted? Can it expose information it should not? Those questions remain important, but they capture only part of the problem. A model generating text is one thing. A system connected to identities, applications, tools, workflows, and business data is another.

The difference becomes clearer when comparing a chatbot that answers questions with an agent that can retrieve customer records, update tickets, invoke tools, trigger workflows, and interact with external systems. The underlying model may be identical. Its access is not. The security question begins to shift from what the model knows to what the system can do.

The same theme appears in the Five Eyes statement released earlier this year, describing AI as a force multiplier that is accelerating both offensive and defensive cyber operations. The NCSC guidance explores what that reality looks like when autonomous systems begin operating inside enterprise environments.

Securing AI agents in operation

The NCSC spends relatively little time debating model behavior and considerably more time discussing identity, permissions, monitoring, oversight, containment, and response. Agents are treated as participants within an environment rather than isolated pieces of technology.  

That's broadly consistent with how we think about the problem at Darktrace.

An agent should not be treated as an extension of a user account. It develops its own behavioral patterns. It accesses systems, interacts with data, invokes tools, and moves across workflows in ways that can be observed independently. Understanding what an agent is permitted to do matters. Understanding how it actually behaves once deployed, and whether that behavior aligns with business intent, matters just as much.

Identity provides an obvious example. The NCSC recommends assigning distinct identities to agents rather than allowing them to disappear into surrounding human or service accounts. Most importantly, assigning agents distinct identities enables independent behavioral monitoring.

Development assumptions vs. real-world behavior

The same principle extends to monitoring. NCSC guidance places agent activity within normal security operations rather than treating it as a separate AI governance function. Many of the controls described are put in place before an agent begins operating. Sandboxing, credential design, approval workflows and human oversight all reflect judgments about how the system is expected to behave and what risks it is likely to create.

Actual use may challenge those assumptions. Access patterns change. Workflows expand. Systems begin interacting with resources they have never touched before. Processes that appeared reasonable during design behave differently in production. Human oversight requirements may turn out to be either excessive or inadequate once the system is operating at scale and operating within the context of unique business processes.

The Five Eyes statement points to a similar issue: organizations need confidence that controls continue to work as intended once systems are exposed to real users, data, tools and operational pressures. Often, the question is not whether an agent is technically allowed to perform an action, but whether its behavior remains consistent with the role it was intended to play.

Monitoring and governance of AI agents go hand-in-hand

This problem is exactly why monitoring and governance should be treated as part of the same process. Governance sets the initial parameters for deployment, while monitoring provides evidence about whether those parameters remain appropriate. That evidence should, in turn, inform changes to permissions, controls and oversight.

This matters increasingly as autonomous systems are integrated into business processes. The relevant risk is shaped not only by the model or agent itself, but by what it can access, what actions it can take, and how its behavior changes in practice.

Developing continuous oversight of AI agent behavior

The implication is clear: governance cannot end at deployment. Organizations need a way to understand how agents behave after deployment, test whether controls remain appropriate, and adjust them as conditions change. That requires visibility not just into technical activity, but into whether that activity makes sense in the context of the business process the agent is intended to support.

This is where business-centric behavioral security can become critical. Risk does not emerge from the model itself: it emerges from the actions an autonomous system takes within the enterprise and the downstream consequences of those actions.  

An agent can operate exactly as intended and still create risk if it accesses sensitive information in an unexpected context, exercises permissions in ways that create unintended exposure, or influences business processes in ways that were not anticipated during design and review.

Traditional governance vs. behavioral security

Traditional governance frameworks provide assurance at a point in time. Behavioral security can provide ongoing visibility into how autonomous systems interact with the organization they are meant to serve. Rather than focusing exclusively on model performance or policy compliance, organizations need to understand whether an agent's behavior aligns with business intent, operational expectations, and acceptable risk tolerances as conditions change.

As enterprises move from isolated AI deployments to interconnected ecosystems of agents, visibility into behavior becomes as important as visibility into code. Governance determines what an autonomous system is permitted to do. Behavioral analytics helps determine what it is doing, what business outcomes it is producing, and whether those outcomes remain aligned with the organization's objectives.

[related-resource]

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About the author
Margaret Cunningham, PhD
VP, Security & AI Strategy, Field CISO

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August 26, 2026

When AI Becomes the Lure: A Fake Gemini Installer Delivers Vidar

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Key takeaways

  • Darktrace observed a customer download a fake Google Gemini installer hosted on Google Colab, resulting in the execution of the Vidar information stealer.
  • Darktrace identified the compromise through behavioral indicators, including suspicious process activity, anomalous network communications, and indicators of credential theft, before autonomously containing the threat.
  • The incident highlights how threat actors are increasingly exploiting trusted platforms and a growing interest in AI tools to distribute malware through seemingly legitimate software acquisition workflows.

The Growing Abuse of Generative AI

As organizations are increasingly adopting generative AI tools into their daily workflows, attackers are adapting their distribution methods accordingly too. As part of their day-to-day work, users are now searching for AI assistants, programming tools, browser extensions, desktop applications, and productivity integrations.

Recent reports have highlighted campaigns that use fake AI software and AI-related installers to distribute malware and steal credentials [1]. Researchers have documented campaigns that exploit fake AI-themed websites and services to distribute information stealers and backdoors [2]. Security researchers have also observed attackers disguising malware as legitimate installers for AI software to increase the likelihood of victim interaction and execution [3].

In July 2026, Darktrace observed one such case within a customer environment in the Europe, Middle East and Africa (EMEA) region, where attackers used a fake generative AI installer to deliver the prolific information stealer Vidar. This incident highlights how threat actors are exploiting interest in AI services to distribute established malware using increasingly convincing social engineering techniques.

How a Fake Gemini Installer Delivered Vidar

Initial Access: From Search Result to Malware Download

Unlike many malware campaigns that begin with a phishing email, this activity appears to have originated from a user searching for and downloading software.

Darktrace first observed unusual activity on the customer network after a suspicious executable file was launched from a user’s Download folder. Further investigation revealed that the file purported to be a Google Gemini installer and was named “Download_Google_Gemini_For_Windows.exe”.

During the initial analysis, it was noted that the top search result for the suspicious filename associated pointed to a file hosted on Google Colab, a cloud-based Jupyter notebook platform, commonly used by developers, researchers, and data scientists to run code and machine learning workloads through a web browser. By leveraging another trusted Google platform, the attacker increased the likelihood that users would perceive the download as legitimate, making the lure more convincing to those searching for Gemini-related software.

Figure 1: The Google Colab page containing a download prompt for the fake Google Gemini installer.

Further investigation of the Google Colab page revealed that the download prompt redirected users to a secondary site, hxxps://micronsoftwares[.]com, which posed as a "Windows Software Hub" download page and offered the fake Gemini installer for download.

Figure 2: The secondary website posing as a "Windows Software Hub" download page, which likely hosted the fake Gemini installer.

While the investigation did not uncover any HTTP or file-download telemetry data that conclusively identified the download source, SSL communication sessions with Google Colab were detected immediately before the suspicious file was executed. The timing of these connections suggests that the user interacted with the Colab resource before being redirected to the secondary site from which the executable was downloaded.

The user was not simply tricked into opening an email attachment; instead, the attacker embedded malicious content into a process many users would consider entirely legitimate: searching for and downloading software associated with a trusted platform.

Weaponizing Trusted Platforms

At the time of review (July 15, 2026), Darktrace's Threat Research team confirmed that the Google Colab page was still active and prompting users to download a ZIP archive containing the binary file.

The archive also appeared to contain a README file instructing users to run the binary file with administrator privileges and add it to their antivirus software’s exception lists. These instructions suggest that the campaign relied heavily on social engineering, convincing users to take actions that would facilitate malware execution and potentially bypass security checks.

The use of a legitimate platform also complicates the user’s decision-making. Downloads associated with a trusted service are often perceived as less suspicious than those hosted on unfamiliar domains. When combined with the branding of a widely used AI tool, the lure becomes even more convincing.

Malware Analysis

Darktrace’s Threat Research team identified the executable file as the information-stealing malware Vidar. Analysis revealed that the binary file was a newer Go-compiled variant that communicated with Telegram-based infrastructure. Darktrace’s researchers also identified dtm[.]kijangturbo88[.]top as a command-and-control (C2) endpoint associated with the activity. While the malware itself was not novel, the lure and delivery mechanism was.

For a deeper look at the information stealer, see Darktrace’s 2023 analysis of Vidar.

Figure 3: Darktrace’s detection of the unusual outbound connection associated with the fake Gemini installer.

Shortly after execution, the process established communications with the external IP address 91.98.98[.]86 via port 443, directly linking the executable to suspicious network activity observed on the device. Subsequent open-source intelligence (OSINT) analysis of the revealed multiple malicious associations [5].

Additional Darktrace detections included unusual SSL activity from the affected device. Analysis of related SSL telemetry identified 91.98.111[.]49 as additional infrastructure associated  with the activity [6].

Subsequent alerts from the customer's Microsoft Defender for Endpoint integration later confirmed activity consistent with the theft of browser credentials and other sensitive data from the affected endpoint.

Taken together, these detections provided a clear picture of the attack, from the execution of a suspicious file and unusual network connections to indicators of C2 activity and credential theft.

Figure 4: Darktrace’s detection of anomalous activity following the execution of the fake Gemini installer, seen in the Model Alert Event Log.

Darktrace's Autonomous Response

Following the detection, Darktrace’s Autonomous Response took immediate containment action, including blocking communication with suspicious external infrastructure, including 91.98.98[.]86, and quarantining the compromised device.

Despite the apparent legitimacy of the activity, with the installer hosted on a trusted platform and resembling a routine software download, Darktrace was able to detect and contain the attack because the device's behavior deviated from its normal pattern.

Figure 5: Automated containment actions implemented by Darktrace's Autonomous Response following the detection of activity associated with the fake Gemini installer.

Conclusion

This investigation highlights how threat actors continue to adapt established malware delivery techniques to emerging technology trends. While the malware itself was not new, the distribution method was. By disguising Vidar as a Google Gemini installer and hosting the malicious content on a trusted platform, the attack aligned its lure with a growing behavioral trend: users actively searching for AI tools and services as part of their day-to-day work.

Although fake installers are not a new phenomenon, the rapid rise of generative AI has created new opportunities for threat actors. Rather than relying solely on traditional delivery methods, attackers can now target users who are actively searching for AI applications. As AI adoption continues to accelerate across enterprise environments, organizations should remain alert to campaigns that exploit this interest through fake applications, malicious websites, manipulated search results, the misuse of trusted platforms, and AI-themed social engineering.

Credit to Rushanth Ramanathan (Cyber Analyst) Joanna Ng (Detection Engineer)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Security Integration / C2 Activity and Integration Detection
  • Endpoint / New Suspicious Executable Launched
  • Endpoint / Process Connection / Unusual Connection from New Process
  • Anomalous Connection / Rare External SSL Self-Signed
  • Security Integration / High Severity Integration Detection
  • Antigena / Network / Significant Anomaly /  Antigena Significant Security Integration and Network Activity Block

•Antigena / Network / Significant Anomaly /  Antigena Significant Anomaly from Client Block

List of Indicators of Compromise (IoCs)

IoC Type Description
Download_Google_Gemini_For_Windows.exe File Fake Gemini-themed installer observed during the investigation.
GoogleAppInstaller.exe File Related executable identified through endpoint telemetry.
91.98.98[.]86 IP Address External destination contacted by the malicious executable.
91.98.111[.]49 IP Address Related infrastructure identified through SSL certificate pivoting.
dtm[.]kijangturbo88[.]top Domain Command-and-control endpoint identified during malware analysis.
1e13c2c9eac72daf63fd00a9946878949e159ae6ec51b54ec64f942d79d61913 SHA256 Malware sample associated with the fake Gemini installer.

MITRE ATT@CK Mapping


MITRE ATT&CK Mapping Tactic Technique
Initial Access T1204 User Execution
Execution T1204.002 User Execution: Malicious File
Defence Evasion T1036 Masquerading
Credential Access T1555 Credentials from Password Stores
Credential Access T1555.003 Credentials from Web Browsers
Command and Control T1071 Application Layer Protocol
Exfiltration T1041 Exfiltration Over C2 Channel
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About the author
Rushanth Ramanathan
Cyber Analyst
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