AI cyberattacks: LLMs can plan and execute cyber operations autonomously
A study led by Brian Singer, a PhD candidate in electrical and computer engineering at Carnegie Mellon University, has revealed that LLMs can simulate network breaches remarkably similar to real-world attacks when equipped with high-level planning capabilities and specialised agent frameworks.
In the study, the LLMs managed to infiltrate enterprise networks, identify vulnerabilities, and carry out multistep attacks without human intervention. This research demonstrates that advanced AI models are capable of not only performing basic tasks but also making decisions autonomously and adapting to dynamic network environments.
This presents both significant risks and potential opportunities for cybersecurity. On the one hand, malicious actors may exploit such technologies to automate and scale their attacks. On the other hand, companies and security researchers could harness LLMs to develop and test cybersecurity measures, for instance by simulating attacks to proactively identify vulnerabilities.
The study’s findings are detailed on Anthropic’s research website; a preprint of the paper is also available on arXiv. These publications offer valuable insights into the methodology and implications of this groundbreaking and challenging research in AI cyberattacks.
