arXiv AI By Hasan Demirkiran, Jens Ernstberger

Language Is a Security Surface for Agent Guards

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The study investigates whether the language of a user’s instruction affects the safety verdicts of agent guards. By translating 1,220 labeled steps from an English benchmark into German, French, Turkish, Swahili, and Amharic while keeping all other data identical, the authors find that many open, small, and purpose‑built guards alter their verdicts when the instruction is translated, with larger effects for rarer languages. The impact varies with guard size, with smaller models showing greater loss of recall and larger models (e.g., Gemini 3.8 Flash) remaining largely unchanged.

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arXiv AI
Aug 24

ClawSentry: A Progressive Multi-Tier Security Monitor for Safeguarding Autonomous LLM Agents

ClawSentry is an open‑source, framework‑agnostic security supervision gateway designed to protect autonomous large language model (LLM) agents from progressive risks that can arise at four points in the agent control loop: skill admission, invocation‑time intent, execution‑time effect, and post‑action consequence. It introduces a multi‑tier decision engine—deterministic L1, rule‑anchored L2, and read‑only L3—alongside a First‑Use Skill Package Review (FSPR) and an Agent Harness Protocol (AHP) that applies a single policy across multiple agent runtimes without modifying their internals. Evaluation on SkillInject and the SkillsSafety benchmark shows that ClawSentry significantly reduces contextual adversarial skill risk (ASR) while maintaining high task success rates (TSR).

By Kai Wang, Zeming Wei, BiaoJie Zeng, Chang Jin, An Wang, Xiaokun Luan, Zhixiao Lin, Jingjing Qu, Xia Hu, Xingcheng Xu