Using Claude to reduce the attack surface of code

Chris Rohlf

Over the past year, we’ve published lots of data that shows that frontier AI models are more than capable of discovering and exploiting security vulnerabilities. Showing how previously unknown vulnerabilities might be exploited is, however, only the start of what frontier AI models can do for cyber defense. In this post, we summarize some of our initial research into automated attack surface analysis and reduction: the process of using AI models to map the code that is reachable, then removing or restricting access to anything than a given deployment doesn’t need to expose.

Automated attack surface analysis and reduction can meaningfully improve the security of deployed software in the near term. Securing software by finding and fixing individual vulnerabilities alone scales poorly: fixing even a critical vulnerability tells you nothing about the vulnerabilities that remain. By contrast, reducing code reachability can reduce the severity of unknown vulnerabilities, including the ones that might be under active exploitation. These techniques don’t remove the unknown vulnerabilities in the code, but they do make the vulnerabilities harder to reach, and thereby reduce their severity. Minimizing codebases’ attack surface results in better security without ever having to find or fix an individual vulnerability.