blog.trailofbits.com - Now that DARPA’s AI Cyber Challenge (AIxCC) has officially ended, we can finally make Buttercup, our CRS (Cyber Reasoning System), open source!
We’re thrilled to announce that Trail of Bits won second place in DARPA’s AI Cyber Challenge (AIxCC)! Now that the competition has ended, we can finally make Buttercup, our cyber reasoning system (CRS), open source. We’re thrilled to make Buttercup broadly available and see how the security community uses, extends, and benefits from it.
To ensure as many people as possible can use Buttercup, we created a standalone version that runs on a typical laptop. We’ve also tuned this version to work within an AI budget appropriate for individual projects rather than a massive competition at scale. In addition to releasing the standalone version of Buttercup, we’re also open-sourcing the versions that competed in AIxCC’s semifinal and final rounds.
In the rest of this post, we’ll provide a high-level overview of how Buttercup works, how to get started using it, and what’s in store for it next. If you’d prefer to go straight to the code, check it out here on GitHub.
How Buttercup works
Buttercup is a fully automated, AI-driven system for discovering and patching vulnerabilities in open-source software. Buttercup has four main components:
Orchestration/UI coordinates the overall actions of Buttercup’s other components and displays information about vulnerabilities discovered and patches generated by the system. In addition to a typical web interface, Buttercup also reports its logs and system events to a SigNoz telemetry server to make it easy for users to see what Buttercup is doing.
Vulnerability discovery uses AI-augmented mutational fuzzing to find program inputs that demonstrate vulnerabilities in the program. Buttercup’s vulnerability discovery engine is based on OSS-Fuzz/Clusterfuzz and uses libFuzzer and Jazzer to find vulnerabilities.
Contextual analysis uses traditional static analysis tools to create queryable program models that are used to provide context to AI models used in vulnerability discovery and patching. Buttercup uses tree-sitter and CodeQuery to build the program model.
Patch generation is a multi-agentic system for creating and validating software patches for vulnerabilities discovered by Buttercup. Buttercup’s patch generation system uses seven distinct AI agents to create robust patches that fix vulnerabilities it finds and avoid breaking the program’s other functionality.
