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Minxin Du

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#artificial intelligence Preprint Sep 2026

SINGED: Correct Outputs Do Not Certify Safe Execution in LLM Agents

Tool-using language-model agents select and execute third-party artifacts. Different implementations can return the requested output while producing hidden execution effects that task-, attack-, or choice-based evaluations may miss. We study functional counterfeits: implementations that match benign alternatives on the...

XiaoYu Xu, Zi Liang, Min-Xin Du et al. · 0 citations
#artificial intelligence Preprint Aug 2026

CAITLYN: Can LLM Agents Autonomously Synthesize Defenses against Emerging Injection Attacks?

This work proposes Continuous Agents for Injection Threats via Lifelong Yielding Nexus (CAITLYN), an agent-agnostic defense middleware that matches the detection performance of state-of-the-art defenses at lower token overhead than LLM-as-a-judge baselines.

Zi Liang, XiaoYu Xu, Yanyun Wang et al. · 1 citation

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