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Chaowei Xiao

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#machine learning Preprint Oct 2026

Self-Reflection Fine-Tuning: Enhancing Agent Security against Prompt Injection Attacks from Failure Experience

Large language model (LLM) agents are increasingly deployed in tool-augmented environments, but their reliance on external inputs makes them highly vulnerable to prompt injection attacks that can hijack task objectives. Existing safety alignment methods rely on static expert trajectories or preference optimization, lim...

Zi-Xu Wang, Hao Li, Feng Gao et al. · 0 citations
#artificial intelligence Preprint Jun 2026

AutoDojo: A Generative Benchmark for Evaluating Prompt Injection Defenses in LLM Agents

AutoDojo is introduced, a generative benchmark built on AgentDojo and AgentDyn that generates IPI adaptively for a given agent and defense, and it is demonstrated that standard static benchmarks often significantly overestimate defense efficacy.

Xinhang Ma, Taoran Li, Chao-Wei Xiao et al. · 5 citations
Preprint Aug 2026

ROPE: Routed Origin Policy Enforcement against Indirect Prompt Injection

ROPE is presented, which is anchored in a structural notion of trust, and holds attack success rate to 1.6--2.6 while retaining 82--100% of undefended clean utility, significantly exceeding state-of-the-art system-level defenses in utility while attaining comparable or better security.

Xinhang Ma, Chao-Wei Xiao, William Yeoh et al. · 3 citations · ⚡2

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