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April 17, 2024

Cerber Ransomware: Dissecting the three heads

Cerber ransomware's Linux variant is actively exploiting CVE-2023-22518 in Confluence servers. It uses three UPX-packed C++ payloads: a primary stager, a log checker for environment assessment, and an encryptor that renames files with a .L0CK3D extension.
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
Nate Bill
Threat Researcher
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Apr 2024

Introduction: Cerber ransomware

Researchers at Cado Security Labs (now part of Darktrace) received reports of the Cerber ransomware being deployed onto servers running the Confluence application via the CVE-2023-22518 exploit. [1] There is a large amount of coverage on the Windows variant, however there is very little about the Linux variant. This blog will discuss an analysis of the Linux variant. 

Cerber emerged and was at the peak of its activity around 2016, and has since only occasional campaigns, most recently targeting the aforementioned Confluence vulnerability. It consists of three highly obfuscated C++ payloads, compiled as a 64-bit Executable and Linkable Format (ELF, the format for executable binary files on Linux) and packed with UPX. UPX is a very common packer used by many threat actors. It allows the actual program code to be stored encoded in the binary, and at runtime extracted into memory and executed (“unpacked”). This is done to prevent software from scanning the payload and detecting the malware.

Pure C++ payloads are becoming less common on Linux, with many threat actors now employing newer programming languages such as Rust or Go. [2] This is likely due to the Cerber payload first being released almost 8 years ago. While it will have certainly received updates, the language and tooling choices are likely to have stuck around for the lifetime of the payload.

Initial access

Cado researchers observed instances of the Cerber ransomware being deployed after a threat actor leveraged CVE-2023-22518 in order to gain access to vulnerable instances of Confluence [3]. It is an improper authorization vulnerability that allows an attacker to reset the Confluence application and create a new administrator account using an unprotected configuration restore endpoint used by the setup wizard.

[19/Mar/2024:15:57:24 +0000] - http-nio-8090-exec-10 13.40.171.234 POST /json/setup-restore.action?synchronous=true HTTP/1.1 302 81796ms - - python-requests/2.31.0 
[19/Mar/2024:15:57:24 +0000] - http-nio-8090-exec-3 13.40.171.234 GET /json/setup-restore-progress.action?taskId= HTTP/1.1 200 108ms 283 - python-requests/2.31.0 

Once an administrator account is created, it can be used to gain code execution by uploading & installing a malicious module via the admin panel. In this case, the Effluence web shell plugin is directly uploaded and installed, which provides a web UI for executing arbitrary commands on the host.

Web Shell recreation
Figure 1: Recreation of installing a web shell on a Confluence instance

The threat actor uses this web shell to download and run the primary Cerber payload. In a default install, the Confluence application is executed as the “confluence” user, a low privilege user. As such, the data the ransomware is able to encrypt is limited to files owned by the confluence user. It will of course succeed in encrypting the datastore for the Confluence application, which can store important information. If it was running as a higher privilege user, it would be able to encrypt more files, as it will attempt to encrypt all files on the system.

Primary payload

Summary of payload:

  • Written in C++, highly obfuscated, and packed with UPX
  • Serves as a stager for further payloads
  • Uses a C2 server at 45[.]145[.]6[.]112 to download and unpack further payloads
  • Deletes itself off disk upon execution

The primary payload is packed with UPX, just like the other payloads. Its main purpose is to set up the environment and grab further payloads in order to run.

Upon execution it unpacks itself and tries to create a file at /var/lock/0init-ld.lo. It is speculated that this was meant to serve as a lock file and prevent duplicate execution of the ransomware, however if the lock file already exists the result is discarded, and execution continues as normal anyway. 

It then connects to the (now defunct) C2 server at 45[.]145[.]6[.]112 and pulls down the secondary payload, a log checker, known internally as agttydck. It does this by doing a simple GET /agttydcki64 request to the server using HTTP and writing the payload body out to /tmp/agttydck.bat. It then executes it with /tmp and ck.log passed as arguments. The execution of the payload is detailed in the next section.

Once the secondary payload has finished executing, the primary payload checks if the log file at /tmp/ck.log it wrote exists. If it does, it then proceeds to delete itself and agttydcki64 from the disk. As it is still running in memory, it then downloads the encryptor payload, known internally as agttydcb, and drops it at /tmp/agttydcb.bat. The packing on this payload is more complex. The file command reports it as a DOS executable and the bat extension would imply this as well. However, it does not have the correct magic bytes, and the high entropy of the file suggests that it is potentially encoded or encrypted. Indeed, the primary payload reads it in and then writes out a decoded ELF file back using the same stream, overwriting the content. It is unclear the exact mechanism used to decode agttydcb. The primary payload then executes the decoded agttydcb, the behavior of which is documented in a later section.

2283  openat(AT_FDCWD, "/tmp/agttydcb.bat", O_RDWR) = 4 
2283  read(4, "\353[\254R\333\372\22,\1\251\f\235 'A>\234\33\25E3g\335\0252\344vBg\177\356\321"..., 450560) = 450560 
2283  lseek(4, 0, SEEK_SET)             = 0 
2283  write(4, "\177ELF\2\1\1\0\0\0\0\0\0\0\0\0\2\0>\0\1\0\0\0X\334F\0\0\0\0\0"..., 450560) = 450560 
2283  close(4)                          = 0 

Truncated strace output for the decoding process

Log check payload - agttydck

Summary of payload:

  • Written in C++, highly obfuscated, and packed with UPX
  • Tries to write the phrase “success” to a given file passed in arguments
  • Likely a check for sandboxing, or to check the permission level of the malware on the system

The log checker payload, agttydck, likely serves as a permission checker. It is a very simple payload and was easy to analyze statically despite the obfuscation. Like the other payloads, it is UPX packed.

When run, it concatenates each argument passed to it and delimits with forward slashes in order to obtain a full path. In this case, it is passed /tmp and ck.log, which becomes /tmp/ck.log. It then tries to open this file in write mode, and if it succeeds writes the word “success” and returns 0. If it does not succeed, it returns 1.

cleaned-up routine
Figure 2: Cleaned-up routine that writes out the success phrase

The purpose of this check isn’t exactly clear. It could be to check if the tmp directory is writable and that it can write, which may be a check for if the system is too locked down for the encryptor to work. Given the check is run in a process separate to the primary payload, it could also be an attempt to detect sandboxes that may not handle files correctly, resulting in the primary payload not being told about the file created by the child.

Encryptor - agttydck

Summary of payload:

  • Written in C++, highly obfuscated, and packed with UPX
  • Writes log file /tmp/log.0 on start and /tmp/log.1 on completion, likely for debugging
  • Walks the root directory looking for directories it can encrypt
  • Writes a ransom note to each directory
  • Overwrites all files in directory with their encrypted content and adds a .L0CK3D extension

The encryptor, agttydcb, achieves the goal of the ransomware, which is to encrypt files on the filesystem. Like the other payloads, it is UPX packed and written with heavily obfuscated C++. Upon launch, it deletes itself off disk so as to not leave any artefacts. It then creates a file at /tmp/log.0, but with no content. As it creates a second file at /tmp/log.1 (also with no content) after encryption finishes, it is possible these were debug markers that the attacker mistakenly left in.

The encryptor then spawns a new thread to do the actual encryption. The payload attempts to write a ransom note at /<directory>/read-me3.txt. If it succeeds, it will walk all files in the directory and attempt to encrypt them. If it fails, it moves on to the next directory. The encryptor chooses to pick which directories to encrypt by walking the root file system. For example, it will try to encrypt /usr, and then /var, etc.

Cerber ransom note
Figure 3: Ransom note left by Cerber

When it has identified a file to encrypt, it opens a read-write file stream to the file and reads in the entire file. It is then encrypted in memory before it seeks to the start of the stream and writes the encrypted data, overwriting the file content, and rendering the file fully encrypted. It then renames the file to have the .L0CK3D extension. Rewriting the same file instead of making a new file and deleting the old one is useful on Linux as directories may be set to append only, preventing the outright deletion of files. Rewriting the file may also rewrite the data on the underlying storage, making recovery with advanced forensics also impossible.

2290  openat(AT_FDCWD, "/home/ubuntu/example", O_RDWR) = 6 
2290  read(6, "file content"..., 3691) = 3691 
2290  write(6, "\241\253\270'\10\365?\2\300\304\275=\30B\34\230\254\357\317\242\337UD\266\362\\\210\215\245!\255f"
..., 3691) = 3691 
2290  close(6)                          = 0 
2290  rename("/home/ubuntu/example", "/home/ubuntu/example.L0CK3D") = 0 

Truncated strace of the encryption process

Once this finishes, it tries to delete itself again (which fails as it already deleted itself) and creates /tmp/log.1. It then gracefully exits. Despite the ransom note claiming the files were exfiltrated, Cado researchers did not observe any behavior that showed this.

Conclusion

Cerber is a relatively sophisticated, albeit aging, ransomware payload. While the use of the Confluence vulnerability allows it to compromise a large amount of likely high value systems, often the data it is able to encrypt will be limited to just the confluence data and in well configured systems this will be backed up. This greatly limits the efficacy of the ransomware in extracting money from victims, as there is much less incentive to pay up.

IoCs

The payloads are packed with UPX so will match against existing UPX Yara rules.

Hashes (sha256)

cerber_primary 4ed46b98d047f5ed26553c6f4fded7209933ca9632b998d265870e3557a5cdfe

agttydcb 1849bc76e4f9f09fc6c88d5de1a7cb304f9bc9d338f5a823b7431694457345bd

agttydck ce51278578b1a24c0fc5f8a739265e88f6f8b32632cf31bf7c142571eb22e243

IPs

C2 (Defunct) 45[.]145[.]6[.]112

References

  1. https://confluence.atlassian.com/security/cve-2023-22518-improper-authorization-vulnerability-in-confluence-data-center-and-server-1311473907.html
  1. https://www.proofpoint.com/uk/threat-reference/cerber-ransomware  
  1. https://nvd.nist.gov/vuln/detail/CVE-2023-22518

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
Nate Bill
Threat Researcher

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May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

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How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

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Oakley Cox
Director of Product

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May 28, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

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Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

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About the author
Jamie Bali
Technical Author (AI) Developer
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