LLM agents vulnerable to prompt injection via tool descriptions

A new research paper highlights a critical vulnerability in tool-augmented LLM agents, demonstrating that current evaluations often overlook a significant attack surface: tool descriptions. Researchers found that the same malicious byte sequences can yield drastically different success rates depending on whether they are delivered through tool outputs or tool descriptions. This interaction between the model and the attack surface, rather than the surface alone, dictates the agent's susceptibility to prompt injection. AI

IMPACT Highlights a critical blindspot in LLM agent security evaluations, necessitating new defense strategies.

RANK_REASON Academic paper detailing a novel vulnerability in LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• GEMINI-3-FLASH
• GPT-4.1
• LLM agents

• paper
• safety

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