Ask HN: Which CVEs should I add to my Python security benchmark for AI agents? (github.com)

A new AI benchmarking tool, called CVE-Bench, has been introduced to evaluate large language model (LLM) agents on their ability to fix real-world security vulnerabilities, specifically Common Vulnerabilities and Exposures (CVEs). This benchmark operates within sandboxed Docker containers, allowing agents to be scored against a rigorous test suite designed by the maintainer. Each task within the benchmark corresponds to a specific CVE and includes metadata about the vulnerability, scripts for setup and testing, and a method for securely injecting test scripts during execution. The significance of CVE-Bench lies in its potential to improve the security capabilities of AI agents by providing a structured environment to assess their vulnerability-fixing abilities. By including technical features such as the use of Docker for isolation, a well-defined task structure for CVEs, and a mechanism to ensure that security tests can be reliably executed, this tool aims to push the boundaries of how AI models can assist in cybersecurity. The integration of various AI models, concurrent task execution, and comprehensive result reporting underscores the ongoing evolution of AI safety and security, making it a pivotal development for both AI researchers and cybersecurity experts.