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May 18, 2021

The Dangers of Double Extortion Ransomware Attacks

Learn about the latest trend in ransomware attacks known as double extortion. Discover how Darktrace can help protect your organization from this threat.
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager
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18
May 2021

A year and a half ago, ‘double extortion’ ransomware was being used by only one known threat actor. Now, over 16 ransomware groups actively utilize this tactic. So, what is it, and why has it become so popular?

What is double extortion ransomware?

The traditional story of ransomware was one of malicious code rapidly encrypting files with public-key RSA encryption, and then deleting those files if the victim did not pay the ransom.

However, after the infamous WannaCry and NotPetya ransomware campaigns over 2017, companies ramped up their cyber defense. More emphasis was placed on backups and restoration processes, so that even if files were destroyed, organizations had copies in place and could easily restore their data.

Yet in turn, cyber-criminals have also adapted their techniques. Now, rather than just encrypting files, double extortion ransomware exfiltrates the data first. This means that if the company refuses to pay up, information can be leaked online or sold to the highest bidder. Suddenly, all those backups and data recovery plans became worthless.

Maze ransomware and friends

In late 2019, Maze ransomware emerged as the first high-profile case of double extortion. Other strains soon followed, with the Sodinokibi attack — which crippled foreign exchange company Travelex — occurring on the final day of that year.

By mid-2020, hundreds of organizations were falling victim to double extortion attacks, various websites on the dark net were leaking company data, and the Ransomware-as-a-Service business was booming as developers sold and rented new types of malware.

Furthermore, cyber security regulations started being weaponized by cyber-criminals who could leverage the threat of having to pay a hefty compliance fine (CCPA, GDPR, NYSDFS regulations) to encourage their victims to keep quiet by offering them a ransom smaller than the penalty fee.

There were 1,200 double extortion ransomware incidents in 2020, across 63 countries, with over 60% of these aimed at the US and the UK.

Despite new legislation being written regularly to try and mitigate these attacks, they aren’t slowing down. According to a recent study by RUSI, there were 1,200 double extortion ransomware incidents in 2020 alone, across 63 different countries. 60% of these were aimed at organizations headquartered in the US, and the UK suffered the second highest number of breaches.

Last month, the cyber-criminal gang known as REvil released details about Apple’s new Macbook Pro on their site ‘Happy Blog’, threatening to release more blueprints and demanding a ransom of $50 million. And last week, Colonial Pipeline purportedly paid $5 million in bitcoin to recover from a devastating OT ransomware attack.

Anatomy of a double extortion ransomware attack

Darktrace has detected a huge upsurge in double extortion ransomware threats in the last year, most recently at an energy company based in Canada. The hackers had clearly done their homework, tailoring the attack to the company and moving quickly and stealthily once inside. Below is a timeline of this real-world incident, which was mostly carried out in the space of 24 hours.

Figure 1: A timeline of the attack

Darktrace detected every stage of the intrusion and notified the security team with high-priority alerts. If Darktrace Antigena had been active in the environment, the compromised server would have been isolated as soon as it began to behave anomalously, preventing the infection from spreading.

Encryption and exfiltration

The initial infection vector is not known, but the admin account was compromised most likely from a phishing link or a vulnerability exploit. This is indicative of a trend away from the widespread ‘spray and pray’ ransomware campaigns of the last decade, towards a more targeted approach.

Cyber AI identified an internal server engaging in unusual network scanning and attempted lateral movement using the Remote Desktop Protocol (RDP). Compromised admin credentials were used to spread rapidly from the server to another internal device, ‘serverps’.

The device ‘serverps’ initiated an outbound connection to TeamViewer, a legitimate file storage service, which was active for nearly 21 hours. This connection was used for remote control of the device and to facilitate the further stages of attack. Although TeamViewer was not in wide operation in the company’s digital environment, it was not blocked by any of the legacy defenses.

The device then connected to an internal file server and downloaded 1.95 TB of data, and uploaded the same volume of data to pcloud[.]com. This exfiltration took place during work hours to blend in with regular admin activity.

The device was also seen downloading Rclone software – an open source tool, which was likely applied to sync data automatically to the legitimate file storage service pCloud.

The compromised admin credential allowed the threat actor to move laterally during this time. Following the completion of the data exfiltration, the device ‘serverps’ finally began encrypting files on 12 devices with the extension *.06d79000.

As with the majority of ransomware incidents, the encryption happened outside of office hours – overnight in local time – to minimize the chance of the security team responding quickly.

AI-powered investigation

Cyber AI Analyst reported on four incidents related to the attack, highlighting the suspicious behavior to the security team and providing a report on the affected devices for immediate remediation. Such concise reporting allowed the security team to quickly identify the scope of the infection and respond accordingly.

Figure 2: Cyber AI Analyst incident tray for a week

Cyber AI Analyst investigates on demand

Following further analysis on March 13, the security team employed Cyber AI Analyst to conduct on-demand investigations into the compromised admin credential in Microsoft 365, as well as another device which was identified as a potential threat.

Cyber AI Analyst created an incident for this other device, which resulted in the identification of unusual port scanning during the time period of infection. The device was promptly removed from the network.

Figure 3: Cyber AI Analyst incident for a compromised device, detailing an unusual internal download

Double trouble

The use of legitimate tools and ‘Living off the Land’ techniques (using RDP and a compromised admin credential) allowed the threat actors to carry out the bulk of the attack in less than 24 hours. By exploiting TeamViewer as a legitimate file storage solution for the data exfiltration, as opposed to relying on a known ‘bad’ or recently registered domain, the hackers easily circumvented all the existing signature-based defenses.

If Darktrace had not detected this intrusion and immediately alerted the security team, the attack could have resulted not only in a ‘denial of business’ with employees locked out of their files, but also in sensitive data loss. The AI went a step further in saving the team vital time with automatic investigation and on-demand reporting.

There is so much more to lose from double extortion ransomware. Exfiltration provides another layer of risk, leading to compromised intellectual property, reputational damage, and compliance fines. Once a threat group has your data, they might easily ask for more payments down the line. It is important therefore to defend against these attacks before they happen, proactively implementing cyber security measures that can detect and autonomously respond to threats as soon as they emerge.

Learn more about double extortion ransomware.

Darktrace model detections:

  • Device / Suspicious Network Scan Activity
  • Device / RDP Scan
  • Device / Network Scan
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous Connection / Unusual Admin RDP Session
  • Device / Multiple Lateral Movement Model Breaches
  • User / New Admin Credentials on Client
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Low and Slow Exfiltration
  • Device / Anomalous SMB Followed By Multiple Model
  • Anomalous Connection / Download and Upload
  • Anomalous Connection / Suspicious Activity On High Risk Device
  • Anomalous File / Internal::Additional Extension Appended to SMB File
  • Compromise / Ransomware::Suspicious SMB Activity
  • Anomalous Connection / Sustained MIME Type Conversion
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Suspicious Read Write Ratio
  • Device / Large Number of Model Breaches
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager

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April 29, 2026

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

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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/

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Nathaniel Bill
Malware Research Engineer

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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

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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.

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