CyberCycle: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities
University of California, Berkeley · Johns Hopkins University · UC Santa Cruz · UC Berkeley · University of California, Santa Cruz · University of California, Santa Barbara · University of California Berkeley
PDF 由论文原始站点提供，PaperCompass 不保存论文文件。
摘要
AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities. However, existing cybersecurity evaluations of AI systems are limited in scale or scope, and fail to capture the end-to-end lifecycle of real-world software vulnerability discovery and remediation. To address this gap, we propose CyberCycle, a large-scale and realistic end-to-end cybersecurity benchmark that comprehensively evaluates AI agents' abilities across the full lifecycle of vulnerability discovery, PoC generation, and patch generation. CyberCycle is comprehensive and scalable, as we build an automated, agent-enhanced pipeline for transforming open-source vulnerability data into realistic evaluation environments. Currently, the benchmark consists of 615 real-world vulnerabilities across 120 different open-source projects.