Groundbreaking research from Carnegie Mellon University, led by Ph.D. candidate Brian Singer, has demonstrated that large language models (LLMs) can autonomously plan and execute complex network attacks. Unlike previous studies, this research evaluated LLMs in realistic enterprise network settings, even successfully replicating the 2017 Equifax data breach attack sequence.
The key to this achievement was teaching LLMs a "mental model" and abstraction of security attack orchestration, enabling higher-level decision-making. While acknowledging the potential risks, Singer highlights the constructive applications, such as providing constant, affordable security testing for organizations. The team is now exploring autonomous defenses to counter such attacks.
This research, though a prototype, marks a significant step in understanding and responding to the evolving capabilities of LLMs in the cybersecurity landscape.
Read the original article: When LLMs Autonomously Attack
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