cs.CR · 2026-06-13 · No. 22

Cryptography and Security, 2026-06-13.

2 new papers in cs.CR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents

    Zihao Wang, Yiming Li, Yutong Wu, Zheyu Liu, Kangjie Chen, Fok Kar Wai, Pin-Yu Chen, Vrizlynn L. L. Thing, Bo Li,...

    cs.CR · cs.AI · cs.CY · cs.HC · cs.MM

    Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments, where they operate over untrusted web content and execute actions with direct consequences. This makes them vulnerable to prompt-injection attacks, in which seemingly benign content embeds adversarial instructions that manipulate agent behaviour. Existing security benchmarks adopt an \textit{attack-centric} perspective, focusing on the...

    arxiv.org/abs/2606.13385 · PDF

  2. 02

    The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems

    Jiaqi Luo, Jiarun Dai, Zhile Chen, Jia Xu, Weibing Wang, Yawen Duan, Brian Tse, Geng Hong, Xudong Pan, Yuan Zhang, Min Yang

    cs.CR · cs.AI

    Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross. Within this broader red-line scenario, autonomous penetration represents a core enabling capability and subtask: the ability of LLM-powered AI systems to independently conduct adversarial operations against a target server without human intervention,...

    arxiv.org/abs/2606.13079 · PDF

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