AgentShield: Deception-based Compromise Detection for Tool-using LLM Agents
Defenses against indirect prompt injection (IPI) in tool-using LLM agents share two structural weaknesses. First, they all attempt to prevent attacks rather than detect the compromises that slip through. Second, they have only been evaluated in English, leaving users of low-resource languages such as Kurdish and Arabic without tested protection. This paper addresses both gaps with AgentShield, a deception-based detection framework that places three layers of traps inside the agent's tool interface: fake tools, fake credentials, and allowlisted parameters. The same trap triggers serve as high-precision labels for a self-supervised classifier. An LLM agent that follows an attacker's hidden ...
| Authors | Yassin H. Rassul, Tarik A. Rashid |
|---|---|
| Primary category | cs.CR |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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