Researchers at cybersecurity firm Sophos have uncovered a malware development framework that uses artificial intelligence tools to speed up the creation and testing of ransomware-related software designed to avoid detection by security products.

The investigation began after Sophos analysts discovered suspicious files on a customer system. What initially appeared to be a collection of penetration-testing tools soon revealed signs of criminal activity, including references to ransom notes and organizations listed on ransomware leak sites.

According to Sophos, the framework combines traditional attack tools with AI-assisted development workflows. Researchers found evidence that the operators used coding assistants such as Cursor and Claude Opus during different stages of development, including writing code, reviewing results, refining payloads, and researching techniques that could help malware evade security controls.

One of the framework's primary goals was to bypass Endpoint Detection and Response (EDR) platforms. These security products are designed to identify malicious activity on computers and servers, often detecting attacks that traditional antivirus software might miss.

The toolkit contained several components intended to reduce the chances of detection. Among them were customized Cobalt Strike profiles that made malicious network traffic resemble ordinary web browsing activity, communication channels that routed commands through Telegram, and malware development scripts capable of injecting malicious code into legitimate Windows applications while allowing those programs to continue functioning normally.

Researchers also identified the use of a Cloudflare Worker that acted as an intermediary between infected systems and attacker-controlled infrastructure. This setup can make it more difficult for defenders to identify the true location of command-and-control servers.

A particularly notable feature of the framework was an automated Active Directory discovery system. Active Directory is widely used in enterprise networks to manage users, computers, permissions, and other resources. Because it contains valuable information about an organization's internal structure, attackers frequently attempt to map Active Directory environments after gaining access to a network.

Sophos found that the discovery process relied on a series of AI-assisted agents that gathered information, assessed results, selected follow-up actions, and continued the investigation of the network. Rather than requiring a human operator to manually perform every step, parts of the reconnaissance process could be carried out through predefined automated workflows.

The framework itself appeared to operate through multiple specialized AI agents assigned to different tasks. Sophos reported that one agent coordinated the overall development process while others focused on testing, documentation, operational security improvements, virtual machine deployment, proxy testing, and malware evaluation.

Researchers also discovered that some agents had been tasked with examining publicly available security research. The system collected information from technical reports and research publications, extracted details about detection-evasion methods, mapped those techniques to the MITRE ATT&CK framework, recreated testing environments, and documented the results.

At the center of the operation was a Python-based payload generation tool. This component produced malware written primarily in Rust and Go while combining encryption, execution techniques, and anti-analysis measures intended to make detection more difficult. Sophos observed nearly 80 generated modules being tested against more than 70 separate evasion methods.

The malware was evaluated in laboratory environments against security products from Sophos, CrowdStrike, and Microsoft. Researchers noted that repeated testing and revision cycles appeared to improve the success rate of many payloads. However, they also observed inconsistencies between some reported results and actual testing outcomes, leaving questions about the accuracy of certain internal performance claims.

Despite the extensive use of artificial intelligence during development, Sophos found no indication that AI was embedded within deployed malware or operating independently on victim systems. The technology was primarily used to accelerate the research, testing, and refinement process while human operators remained responsible for directing the activity.

The findings provide another example of how threat actors are incorporating AI into existing workflows. Rather than introducing entirely new attack methods, these tools appear to be helping attackers shorten the time needed to transform publicly available security research into functioning malware capable of challenging modern security defenses.