Blog
/
Network
/
July 4, 2024

A Busy Agenda: Darktrace's Detection of Qilin Ransomware as a Service Operator

This blog breaks down how Darktrace detected and analyzed Qilin, a Ransomware-as-a-Service group behind recent high-impact attacks. You’ll see how Qilin affiliates customize attacks with flexible encryption, process termination, and double-extortion techniques, as well as why its cross-platform builds in Rust and Golang make it especially evasive. Darktrace highlights three real-world cases where its AI identified likely Qilin activity across customer environments, offering insights into how behavioral detection can spot novel ransomware before disruption occurs. Readers will gain a clear view of Qilin’s toolkit, tactics, and how self-learning defense adapts to these evolving threats.
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
Alexandra Sentenac
Cyber Analyst
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
04
Jul 2024

What is Qilin Ransomware and what's its impact?

Qilin ransomware has recently dominated discussions across the cyber security landscape following its deployment in an attack on Synnovis, a UK-based medical laboratory company. The ransomware attack ultimately affected patient services at multiple National Health Service (NHS) hospitals that rely on Synnovis diagnostic and pathology services. Qilin’s origins, however, date back further to October 2022 when the group was observed seemingly posting leaked data from its first known victim on its Dedicated Leak Site (DLS) under the name Agenda[1].

The Darktrace Threat Research team investigated network artifacts related to Qilin and identified three probable cases of the ransomware across the Darktrace customer base between June 2022 and May 2024.

How Qilin Ransowmare operates as RaaS

Qilin operates as a Ransomware-as-a-Service (RaaS) that employs double extortion tactics, whereby harvested data is exfiltrated and threatened of publication on the group's DLS, which is hosted on Tor. Qilin ransomware has samples written in both the Golang and Rust programming languages, making it compilable with various operating systems, and is highly customizable.

Techniques Qilin Ransomware uses to avoid detection

When building Qilin ransomware variants to be used on their target(s), affiliates can configure settings such as:

  • Encryption modes (skip-step, percent, or speed)
  • File extensions, directories, or processes to exclude
  • Unique company IDs used as extensions on encrypted files
  • Services or processes to terminate during execution [1] [2]

Trend Micro analysts, who were the first to discover Qilin samples in August 2022, when the name "Agenda" was still used in ransom notes, found that each analyzed sample was customized for the intended victims and that "unique company IDs were used as extensions of encrypted files" [3]. This information is configurable from within the Qilin's affiliate panel's 'Targets' section, shown below.

Qilin's affiliate panel and branding

The panel's background image features the eponym Chinese legendary chimerical creature Qilin (pronounced “Ke Lin”). Despite this Chinese mythology reference, Russian language was observed being used by a Qilin operator in an underground forum post aimed at hiring affiliates and advertising their RaaS operation[2].

Qilin ransomware’s affiliate panel.
Figure 1: Qilin ransomware’s affiliate panel.

Qilin’s affiliate payment model

Qilin's RaaS program purportedly has an attractive affiliates' payment structure,

  • Affiliates earn 80% of ransom payments under USD 3 million
  • Affiliates earn 85% of ransom payments above USD 3 million [2]

Publication of stolen data and ransom payment negotiations are purportedly handled by Qilin operators. Qilin affiliates have been known to target companies located around the world and within a variety of industries, including critical sectors such as healthcare and energy.

Qilin target industries and victims

As Qilin is a RaaS operation, the choice of targets does not necessarily reflect Qilin operators' intentions, but rather that of its affiliates.  

Similarly, the tactics, techniques, procedures (TTPs) and indicators of compromise (IoC) identified by Darktrace are associated with the given affiliate deploying Qilin ransomware for their own purpose, rather than TTPs and IoCs of the Qilin group. Likewise, initial vectors of infection may vary from affiliate to affiliate.

Previous studies show that initial access to networks were gained via spear phishing emails or by leveraging exposed applications and interfaces.

Differences have been observed in terms of data exfiltration and potential C2 external endpoints, suggesting the below investigations are not all related to the same group or actor(s).

[related-resource]

Darktrace’s threat research investigation

Qlin ransomware attack breakdown

June 2022: Qilin ransomware attack exploiting VPN and SCCM servers

Key findings

  • Initial access: VPN and compromised admin account
  • Lateral movement: SCCM and VMware ESXi hosts
  • Malware observed: SystemBC, Tofsee
  • Ransom notes: Linked to Qilin naming conventions
  • Darktrace visibility: Analysts worked with customer via Ask the Expert (ATE) to expand coverage, revealing unusual scanning, rare external connections, and malware indicators tied to Qilin

Full story

Darktrace first detected an instance of Qilin ransomware back in June 2022, when an attacker was observed successfully accessing a customer’s Virtual Private Network (VPN) and compromising an administrative account, before using RDP to gain access to the customer’s Microsoft System Center Configuration Manager (SCCM) server.

From there, an attack against the customer's VMware ESXi hosts was launched. Fortunately, a reboot of their virtual machines (VM) caught the attention of the security team who further uncovered that custom profiles had been created and remote scripts executed to change root passwords on their VM hosts. Three accounts were found to have been compromised and three systems encrypted by ransomware.  

Unfortunately, Darktrace was not configured to monitor the affected subnets at the time of the attack. Despite this, the customer was able to work directly with Darktrace analysts via the Ask the Expert (ATE) service to add the subnets in question to Darktrace’s visibility, allowing it to monitor for any further unusual behavior.

Once visibility over the compromised SCCM server was established, Darktrace observed:

  • A series of unusual network scanning activities  
  • The use of Kali (a Linux distribution designed for digital forensics and penetration testing).
  • Connections to multiple rare external hosts. Many of which were using the “[.]ru” Top Level Domain (TLD).

One of the external destinations the server was attempting to connect was found to be related to SystemBC, a malware that turns infected hosts into SOCKS5 proxy bots and provides command-and-control (C2) functionality.

Additionally, the server was observed making external connections over ports 993 and 143 (typically associated with the use of the Interactive Message Access Protocol (IMAP) to multiple rare external endpoints. This was likely due to the presence of Tofsee malware on the device.

After the compromise had been contained, Darktrace identified several ransom notes following the naming convention “README-RECOVER-<extension/company_id>.txt”” on the network. This naming convention, as well as the similar “<company_id>-RECOVER-README.txt” have been referenced by open-source intelligence (OSINT) providers as associated with Qilin ransom notes[5] [6] [7].

April 2023: Manufacturing sector breach with large-scale exfiltration

Key findings

  • Initial access & movement: Extensive scanning and lateral movement via SMB, RDP, and WMI
  • Credential abuse: Use of default credentials (admin, administrator)
  • Malware/Indicators: Evidence of Cobalt Strike; suspicious WebDAV user agent and JA3 fingerprint
  • Data exfiltration: ~30 GB stolen via SSL to MEGA cloud storage
  • Darktrace analysis: Detected anomalous SMB and DCE-RPC traffic from domain controller, high-volume RDP activity, and rare external connectivity to IPs tied to command-and-control (C2). Confirmed ransom notes followed Qilin naming conventions.

Full story

The next case of Qilin ransomware observed by Darktrace took place in April 2023 on the network of a customer in the manufacturing sector in APAC. Unfortunately for the customer in this instance, Darktrace's Autonomous Response was not active on their environment and no autonomous actions were taken to contain the compromise.

Over the course of two days, Darktrace identified a wide range of malicious activity ranging from extensive initial scanning and lateral movement attempts to the writing of ransom notes that followed the aforementioned naming convention (i.e., “README-RECOVER-<extension/company_id>.txt”).

Darktrace observed two affected devices attempting to move laterally through the SMB, DCE-RPC and RDP network protocols. Default credentials (e.g., UserName, admin, administrator) were also observed in the large volumes of SMB sessions initiated by these devices. One of the target devices of these SMB connections was a domain controller, which was subsequently seen making suspicious WMI requests to multiple devices over DCE-RPC and enumerating SMB shares by binding to the ‘server service’ (srvsvc) named pipe to a high number of internal devices within a short time frame. The domain controller was further detected establishing an anomalously high number of connections to several internal devices, notably using the RDP administrative protocol via a default admin cookie.  

Repeated connections over the HTTP and SSL protocol to multiple newly observed IPs located in the 184.168.123.0/24 range were observed, indicating C2 connectivity.  WebDAV user agent and a JA3 fingerprint potentially associated with Cobalt Strike were notably observed in these connections. A few hours later, Darktrace detected additional suspicious external connections, this time to IPs associated with the MEGA cloud storage solution. Storage solutions such as MEGA are often abused by attackers to host stolen data post exfiltration. In this case, the endpoints were all rare for the network, suggesting this solution was not commonly used by legitimate users. Around 30 GB of data was exfiltrated over the SSL protocol.

Darktrace did not observe any encryption-related activity on this customer’s network, suggesting that encryption may have taken place locally or within network segments not monitored by Darktrace.

May 2024: US enterprise compromise

Key findings

  • Initial access & movement: Abuse of administrative and default credentials; lateral movement via DCE-RPC and RDP
  • Malware/Indicators: Suspicious executables (‘a157496.exe’, ‘83b87b2.exe’); abuse of RPC service LSM_API_service
  • Data exfiltration: Large amount of data exfiltrated via FTP and other channels to rare external endpoint (194.165.16[.]13)
  • C2 communications: HTTP/SSL traffic linked to Cobalt Strike, including PowerShell request for sihost64.dll
  • Darktrace analysis: Flagged unusual SMB writes, malicious file transfers, and large-scale exfiltration as highly anomalous. Confirmed widespread encryption activity targeting numerous devices and shares.

Full story

The most recent instance of Qilin observed by Darktrace took place in May 2024 and involved a customer in the US.

In this case, Darktrace initially detected affected devices using unusual administrative and default credentials. Then Darktrace observed additional Internal systems conducting abnormal activity such as:

  • Making extensive suspicious DCE-RPC requests to a range of internal locations
  • Performing network scanning
  • Making unusual internal RDP connections
  • And transferring suspicious executable files like 'a157496.exe' and '83b87b2.exe'.  

SMB writes of the file "LSM_API_service" were also observed, activity which was considered 100% unusual by Darktrace; this is an RPC service that can be abused to enumerate logged-in users and steal their tokens. Various repeated connections likely representative of C2 communications were detected via both HTTP and SSL to rare external endpoints linked in OSINT to Cobalt Strike use. During these connections, HTTP GET requests for the following URIs were observed:

/asdffHTTPS

/asdfgdf

/asdfgHTTP

/download/sihost64.dll

Notably, this included a GET request a DLL file named "sihost64.dll" from a domain controller using PowerShell.  

Over 102 GB of data may have been transferred to another previously unseen endpoint, 194.165.16[.]13, via the unencrypted File Transfer Protocol (FTP). Additionally, many non-FTP connections to the endpoint could be observed, over which more than 783 GB of data was exfiltrated. Regarding file encryption activity, a wide range of destination devices and shares were targeted.

Figure 2: Advanced Search graph displaying the total volume of data transferred over FTP to a malicious IP.

During investigations, Darktrace’s Threat Research team identified an additional customer, also based in the United States, where similar data exfiltration activity was observed in April 2024. Although no indications of ransomware encryption were detected on the network, multiple similarities were observed with the case discussed just prior. Notably, the same exfiltration IP and protocol (194.165.16[.]13 and FTP, respectively) were identified in both cases. Additional HTTP connectivity was further observed to another IP using a self-signed certificate (i.e., CN=ne[.]com,OU=key operations,O=1000,L=,ST=,C=KM) located within the same ASN (i.e., AS48721 Flyservers S.A.). Some of the URIs seen in the GET requests made to this endpoint were the same as identified in that same previous case.

Information regarding another device also making repeated connections to the same IP was described in the second event of the same Cyber AI Analyst incident. Following this C2 connectivity, network scanning was observed from a compromised domain controller, followed by additional reconnaissance and lateral movement over the DCE-RPC and SMB protocols. Darktrace again observed SMB writes of the file "LSM_API_service", as in the previous case, activity which was also considered 100% unusual for the network. These similarities suggest the same actor or affiliate may have been responsible for activity observed, even though no encryption was observed in the latter case.

Figure 3: First event of the Cyber AI Analyst investigation following the compromise activity.

According to researchers at Microsoft, some of the IoCs observed on both affected accounts are associated with Pistachio Tempest, a threat actor reportedly associated with ransomware distribution. The Microsoft threat actor naming convention uses the term "tempest" to reference criminal organizations with motivations of financial gain that are not associated with high confidence to a known non-nation state or commercial entity. While Pistachio Tempest’s TTPs have changed over time, their key elements still involve ransomware, exfiltration, and extortion. Once they've gained access to an environment, Pistachio Tempest typically utilizes additional tools to complement their use of Cobalt Strike; this includes the use of the SystemBC RAT and the SliverC2 framework, respectively. It has also been reported that Pistacho Tempest has experimented with various RaaS offerings, which recently included Qilin ransomware[4].

Conclusion

Qilin is a RaaS group that has gained notoriety recently due to high-profile attacks perpetrated by its affiliates. Despite this, the group likely includes affiliates and actors who were previously associated with other ransomware groups. These individuals bring their own modus operandi and utilize both known and novel TTPs and IoCs that differ from one attack to another.

Darktrace’s anomaly-based technology is inherently threat-agnostic, treating all RaaS variants equally regardless of the attackers’ tools and infrastructure. Deviations from a device’s ‘learned’ pattern of behavior during an attack enable Darktrace to detect and contain potentially disruptive ransomware attacks.

[related-resource]

Credit to: Alexandra Sentenac, Emma Foulger, Justin Torres, Min Kim, Signe Zaharka for their contributions.

References

[1] https://www.sentinelone.com/anthology/agenda-qilin/  

[2] https://www.group-ib.com/blog/qilin-ransomware/

[3] https://www.trendmicro.com/en_us/research/22/h/new-golang-ransomware-agenda-customizes-attacks.html

[4] https://www.microsoft.com/en-us/security/security-insider/pistachio-tempest

[5] https://www.trendmicro.com/en_us/research/22/h/new-golang-ransomware-agenda-customizes-attacks.html

[6] https://www.bleepingcomputer.com/forums/t/790240/agenda-qilin-ransomware-id-random-10-char;-recover-readmetxt-support/

[7] https://github.com/threatlabz/ransomware_notes/tree/main/qilin

Darktrace Model Detections

Internal Reconnaissance

Device / Suspicious SMB Scanning Activity

Device / Network Scan

Device / RDP Scan

Device / ICMP Address Scan

Device / Suspicious Network Scan Activity

Anomalous Connection / SMB Enumeration

Device / New or Uncommon WMI Activity

Device / Attack and Recon Tools

Lateral Movement

Device / SMB Session Brute Force (Admin)

Device / Large Number of Model Breaches from Critical Network Device

Device / Multiple Lateral Movement Model Breaches

Anomalous Connection / Unusual Admin RDP Session

Device / SMB Lateral Movement

Compliance / SMB Drive Write

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Anomalous DRSGetNCChanges Operation

Anomalous Server Activity / Domain Controller Initiated to Client

User / New Admin Credentials on Client

C2 Communication

Anomalous Server Activity / Outgoing from Server

Anomalous Connection / Multiple Connections to New External TCP Port

Anomalous Connection / Anomalous SSL without SNI to New External

Anomalous Connection / Rare External SSL Self-Signed

Device / Increased External Connectivity

Unusual Activity / Unusual External Activity

Compromise / New or Repeated to Unusual SSL Port

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Device / Suspicious Domain

Device / Increased External Connectivity

Compromise / Sustained SSL or HTTP Increase

Compromise / Botnet C2 Behaviour

Anomalous Connection / POST to PHP on New External Host

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

Anomalous File / EXE from Rare External Location

Exfiltration

Unusual Activity / Enhanced Unusual External Data Transfer

Anomalous Connection / Data Sent to Rare Domain

Unusual Activity / Unusual External Data Transfer

Anomalous Connection / Uncommon 1 GiB Outbound

Unusual Activity / Unusual External Data to New Endpoint

Compliance / FTP / Unusual Outbound FTP

File Encryption

Compromise / Ransomware / Suspicious SMB Activity

Anomalous Connection / Sustained MIME Type Conversion

Anomalous File / Internal / Additional Extension Appended to SMB File

Compromise / Ransomware / Possible Ransom Note Write

Compromise / Ransomware / Possible Ransom Note Read

Anomalous Connection / Suspicious Read Write Ratio

IoC List

IoC – Type – Description + Confidence

93.115.25[.]139 IP C2 Server, likely associated with SystemBC

194.165.16[.]13 IP Probable Exfiltration Server

91.238.181[.]230 IP C2 Server, likely associated with Cobalt Strike

ikea0[.]com Hostname C2 Server, likely associated with Cobalt Strike

lebondogicoin[.]com Hostname C2 Server, likely associated with Cobalt Strike

184.168.123[.]220 IP Possible C2 Infrastructure

184.168.123[.]219 IP Possible C2 Infrastructure

184.168.123[.]236 IP Possible C2 Infrastructure

184.168.123[.]241 IP Possible C2 Infrastructure

184.168.123[.]247 IP Possible C2 Infrastructure

184.168.123[.]251 IP Possible C2 Infrastructure

184.168.123[.]252 IP Possible C2 Infrastructure

184.168.123[.]229 IP Possible C2 Infrastructure

184.168.123[.]246 IP Possible C2 Infrastructure

184.168.123[.]230 IP Possible C2 Infrastructure

gfs440n010.userstorage.me ga.co[.]nz Hostname Possible Exfiltration Server. Not inherently malicious; associated with MEGA file storage.

gfs440n010.userstorage.me ga.co[.]nz Hostname Possible Exfiltration Server. Not inherently malicious; associated with MEGA file storage.

Get the latest insights on emerging cyber threats

This report explores the latest trends shaping the cybersecurity landscape and what defenders need to know in 2025

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
Alexandra Sentenac
Cyber Analyst

More in this series

No items found.

Blog

/

Network

/

April 29, 2026

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

botnetDefault blog imageDefault blog image

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/

Continue reading
About the author
Nathaniel Bill
Malware Research Engineer

Blog

/

AI

/

April 28, 2026

State of AI Cybersecurity 2026: 87% of security professionals are seeing more AI-driven threats, but few feel ready to stop them

Default blog imageDefault blog image

The findings in this blog are taken from Darktrace’s annual State of AI Cybersecurity Report 2026.

In part 1 of this blog series, we explored how AI is remaking the attack surface, with new tools, models, agents — and vulnerabilities — popping up just about everywhere. Now embedded in workflows across the enterprise, and often with far-reaching access to sensitive data, AI systems are quickly becoming a favorite target of cyber threat actors.

Among bad actors, though, AI is more often used as a tool than a target. Nearly 62% of organizations  experienced a social engineering attack involving a deepfake, or an incident in which bad actors used AI-generated video or audio to try to trick a biometric authentication system, compared to 32% that reported an AI prompt injection attack.

In the hands of attackers, AI can do many things. It’s being used across the entire kill chain: to supercharge reconnaissance, personalize phishing, accelerate lateral movement, and automate data exfiltration. Evidence from Anthropic demonstrates that threat actors have harnessed AI to orchestrate an entire cyber espionage campaign from end to end, allegedly running it with minimal human involvement.

CISOs inhabit a world where these increasingly sophisticated attacks are ubiquitous. Naturally, combatting AI-powered threats is top of mind among security professionals, but many worry about whether their capabilities are up to the challenge.

AI-powered threats at scale: no longer hypothetical

AI-driven threats share signature characteristics. They operate at speed and scale. Automated tools can probe multiple attack paths, search for multiple vulnerabilities and send out a barrage of phishing emails, all within seconds. The ability to attack everywhere at once, at a pace that no human operator could sustain, is the hallmark of an AI-powered threat. AI-powered threats are also dynamic. They can adapt their behavior to spread across a network more efficiently or rewrite their own code to evade detection.

Security teams are seeing the signs that they’re fighting AI-powered threats at every stage of the kill chain, and the sophistication of these threats is testing their resolve and their resources.

  • 73% say that AI-powered cyber threats are having a significant impact on their organization
  • 92% agree that these threats are forcing them to upgrade their defenses
  • 87% agree that AI is significantly increasing the sophistication and success rate of malware
  • 87% say AI is significantly increasing the workload of their security operations team

These teams now confront a challenge unlike anything they’ve seen before in their careers, and the risks are compounding across workflows, tools, data, and identities. It’s no surprise that 66% of security professionals say their role is more stressful today than it was five years ago, or that 47% report feeling overwhelmed at work.

Up all night: Security professionals’ worry list is long

Traditional security methods were never built to handle the complexity and subtlety of AI-driven behavior. Working in the trenches, defenders have deep firsthand experience of how difficult it can be to detect and stop AI-assisted threats.

Increasingly effective social engineering attacks are among their top concerns. 50% of security leaders mentioned hyper-personalized phishing campaigns as one of their biggest worries, while 40% voiced apprehension about deepfake voice fraud. These concerns are legitimate: AI-generated phishing emails are increasingly tailored to individual organizations, business activities, or individuals. Gone are the telltale signs – like grammar or spelling mistakes – that once distinguished malicious communications. Notably, 33% of the malicious emails Darktrace observed in 2025 contained over 1,000 characters, indicating probable LLM usage.

Security leaders also worry about how bad actors can leverage AI to make attacks even faster and more dynamic. 45% listed automated vulnerability scanning and exploit chaining among their biggest concerns, while 40% mentioned adaptive malware.

Confidence is lacking

Protecting against AI demands capabilities that many organizations have not yet built. It requires interpreting new indicators, uncovering the subtle intent within interactions, and recognizing when AI behavior – human or machine – could be suspicious. Leaders know that their current tools aren’t prepared for this. Nearly half don’t feel confident in their ability to defend against AI-powered attacks.

We’ve asked participants in our survey about their confidence for the last three years now. In 2024, 60% said their organizations were not adequately prepared to defend against AI-driven threats. Last year, that percentage shrunk to 45%, a possible indicator that security programs were making progress. Since then, however, the progress has apparently stalled. 46% of security leaders now feel inadequately prepared to protect their organizations amidst the current threat landscape.

Some of these differences are accentuated across different cultures. Respondents in Japan are far less confident (77% say they are not adequately prepared) than respondents in Brazil (where only 21% don’t feel prepared).

Where security programs are falling short

It’s no longer the case that cybersecurity is overlooked or underfunded by executive leadership. Across industries, management recognizes that AI-powered threats are a growing problem, and insufficient budget is near the bottom of most CISO’s list of reasons that they struggle to defend against AI-powered threats.  

It’s the things that money can’t buy – experience, knowledge, and confidence – that are holding programs back. Near the top of the list of inhibitors that survey participants mention is “insufficient knowledge or use of AI-driven countermeasures.” As bad actors embrace AI technologies en masse, this challenge is coming into clearer focus: attack-centric security tools, which rely on static rules, signatures, and historical attack patterns, were never designed to handle the complexity and subtlety of AI-driven attacks. These challenges feel new to security teams, but they are the core problems Darktrace was built to solve.  

Our Self-Learning AI develops a deep understanding of what “normal” looks like for your organization –including unique traffic patterns, end user habits, application and device profiles – so that it can detect and stop novel, dynamic threats at the first encounter. By focusing on learning the business, rather than the attack, our AI can keep pace with AI-powered threats as they evolve.

Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

[related-resource]

Continue reading
About the author
The Darktrace Community
Your data. Our AI.
Elevate your network security with Darktrace AI