arXiv:2610.33182v1 Announce Type: new Abstract: Large language model (LLM) agents are widely used in many applications and often rely on external tools. Tool information, such as function descriptions, helps agents determine which tools best suit a given context. However, attackers can manipulate this information to mislead LLM agents into using malicious tools.
Spoofing the Toolbox: Optimizing Tool Descriptions to Deceive LLM Agents
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