CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities
arXiv:2606.04460v1 Announce Type: cross Abstract: 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 CyberGym-E2E, a large-scale and realistic end-to-end cybersecurity benchmark that comprehensively evaluates AI agents' abilities across the full lifecycle of vulnerability d
The rapid advancement of AI capabilities necessitates robust and scalable evaluation benchmarks to validate their efficacy in complex, real-world applications like cybersecurity.
This development is crucial for establishing trust and practical utility for AI in critical cybersecurity infrastructure, bridging the gap between theoretical AI potential and deployable solutions.
The introduction of CyberGym-E2E provides a standardized, large-scale benchmark for evaluating AI agents' end-to-end cybersecurity performance, enabling more accurate assessment and faster development cycles.
- · Cybersecurity AI developers
- · Organizations adopting AI for security
- · Cybersecurity research institutions
- · Cyber adversaries relying on human-centric defense gaps
- · Legacy cybersecurity solutions
AI agents will be more effectively evaluated, leading to faster progress in autonomous cybersecurity.
Improved AI-driven cybersecurity will reduce the attack surface for organizations, decreasing the frequency and impact of breaches.
The enhanced security capabilities could free up human cybersecurity experts to focus on more strategic and complex threat intelligence and proactive defense.
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Read at arXiv cs.LG