BypassGym: A Benchmark for Evaluating AI Agents' Endpoint Defense Evasion Capabilities

Reported by arXiv cs.CR (Weifeng Yuan, Zifan Chen, Yucheng Zhang, Feng Dong, Peng Gao, Haoyu Wang)

Summary by Cyber Security News11 Nov 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2611.13684

arXiv:2611.13684v1 Announce Type: new Abstract: Artificial intelligence agents increasingly automate cybersecurity tasks, including offensive operations. Frequent attacks across the web make endpoint detection and response (EDR) a critical defense, but whether AI agents can evade it remains unclear. Benchmarks evaluate the offensive capabilities of large language model (LLM) agents in areas such as penetration testing and vulnerability discovery.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.