Benchmark RadarCyberGym-E2E
CyberGym-E2E evaluates AI agents on end-to-end cybersecurity tasks, covering vulnerability discovery, proof-of-concept generation, and patch generation, using 920 real-world vulnerabilities from 139 open-source projects.
- Released
- 2026-06-03
- Readiness
- Paper only
- Primary field
- Cybersecurity
Why it matters
Existing cybersecurity benchmarks often lack scale or end-to-end scope, limiting assessment of AI agents' practical utility in vulnerability remediation. CyberGym-E2E aims to address this by providing a large-scale, realistic environment for evaluating agents across the full vulnerability lifecycle.
Motivation
AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.