arXiv:2606. 14295v1 Announce Type: cross Abstract: Frontier AI systems are increasingly capable of cybersecurity tasks, including codebase inspection, vulnerability detection, and exploitation.
By Fengyu Liu, Jiarun Dai, Yihe Fan, Wuyuao Mai, Ziao Li, Bofei Chen, Jie Zhang, Zheng Lou, Bocheng Xiang, Qiyi Zhang, Xudong Pan, Geng Hong, Yuan Zhang, Min Yang
arXiv:2606. 15899v1 Announce Type: cross Abstract: Open-source LLM agent ecosystems are growing rapidly, yet the security of community-contributed skills - modular tool definitions that extend agent capabilities - remains largely unvetted.
By Ismail Hossain, Sai Puppala, Md Jahangir Alam, Tanzim Ahad, Sajedul Talukder
arXiv:2512. 18542v3 Announce Type: replace-cross Abstract: AI coding assistants produce vulnerable code in 45\% of security-relevant scenarios~\cite{veracode2025}, yet no public training dataset teaches both traditional web security and AI/ML-specific defenses in a format suitable for instruction tuning.
By Scott Thornton
Large Language Models (LLMs) have shown promise for automated penetration testing, yet existing end-to-end black-box evaluations are highly susceptible to error cascading: failures in early reconnaissance can mask an agent's actual ability to exploit vulnerabilities. To more accurately characterize these capabilities, we propose a two-stage decoupled evaluation framework that separates exploit execution from reconnaissance.
arXiv:2605. 10834v2 Announce Type: replace Abstract: AI pentesting agents are increasingly credible as offensive security systems, but current benchmarks still provide limited guidance on which will perform best in real-world targets.
By Pedro Conde, Henrique Branquinho, Valerio Mazzone, Bruno Mendes, Andr\'e Baptista, Nuno Moniz
arXiv:2608. 09732v1 Announce Type: cross Abstract: Agent skills are emerging as an important attack surface in LLM-based agent systems.
By Puyu Zeng, Simeng Qin, Jingzhi Li, Ju Jia, Zheli Liu, Xiaojun Jia