Autonomous Cyber Assaults Executed by Artificial Legally-Learned Machines, as Revealed in a Carnegie Mellon Investigation
In a groundbreaking development, researchers from Carnegie Mellon University, in collaboration with AI company Anthropic, have demonstrated that large language models (LLMs) can autonomously plan and execute sophisticated cyberattacks on enterprise-grade network environments without human intervention.
The study, led by Ph.D. candidate Brian Singer from Carnegie Mellon’s Department of Electrical and Computer Engineering, involved a team of students and faculty affiliated with the university's security and privacy institute, CyLab. The collaboration also saw technical consultation and model credits provided by Anthropic.
The team developed a hierarchical architecture where the LLM acts as a strategist, planning the attack and issuing high-level instructions, while a mix of LLM and non-LLM agents carry out low-level tasks like scanning networks or deploying exploits. This approach allows LLMs to perform high-level planning and coordinate multi-step attacks, including exploiting vulnerabilities, deploying malware, and exfiltrating data, without requiring detailed human instructions.
The research team recreated a realistic enterprise network modeled on the 2017 Equifax breach scenario. By abstracting and guiding the LLMs with representations of red team strategies rather than low-level commands, the models reliably coordinated and executed complex cyberattacks autonomously. This use of structured abstractions and hierarchical control enabled sub-agent coordination, allowing LLMs to manage the complexity of multi-host network attacks effectively.
Although the research is currently a prototype operating under controlled and constrained conditions, it highlights both serious safety concerns about misuse and promising defensive applications. For example, similar LLM architectures could continuously test network defenses at scale, making proactive cybersecurity more accessible beyond large organizations that traditionally afford red team exercises.
The resulting paper, "On the Feasibility of Using LLMs to Autonomously Execute Multi-host Network Attacks," has already been cited in multiple industry reports. The study reveals that large language models, when structured with high-level planning capabilities and supported by specialized agent frameworks, can simulate network intrusions that closely mirror real-world breaches.
Brian Singer emphasized that the research is not something that's going to take down the internet tomorrow, and the scenarios are constrained and controlled. However, it's a powerful step forward. Singer stated, "We're entering an era of AI versus AI in cybersecurity," emphasizing the need to understand both sides to stay ahead.
The team is now studying how similar architectures might enable autonomous AI defenses. The research builds on Singer's prior work into making autonomous attacker and defender tools more accessible and programmable for human developers. The same abstractions that simplified development for humans made it easier for LLMs to autonomously perform similar tasks.
This approach proved far more effective than earlier methods, which relied solely on LLMs executing shell commands. The findings have already influenced AI safety documentation and have been discussed in expert workshops, signaling an evolving era of automated AI-driven offense and defense in cybersecurity.
- In light of the findings, AI models could potentially be used in the medical field to simulate and plan strategies for addressing medical conditions, leveraging the same hierarchical architecture as demonstrated in the cybersecurity research.
- As artificial intelligence continues to advance, it may not be long before technology is adopted to automate the planning and execution of sophisticated strategies in various fields, such as science, cybersecurity, and beyond.
