Language Is a Security Surface for Agent Guards
Crucial for ML engineers building secure multilingual agents, exposing critical vulnerabilities in safety evaluation pipelines.
AI Summary
Research shows agent security guards exhibit language bias, with smaller models blocking 5% more benign steps in non-English requests and losing 7.4% recall in rare languages.
Excerpt
Step-level guards decide, before each tool call, whether an agent's next action is safe given the user's request and the trajectory so far. Users write those requests in many languages, while guard prompts and the benchmarks on which purpose-built guards are trained and evaluated are English. We ask whether a guard's verdict depends on the language of the request. We build LinguaGuardBench from the 1,220 labelled steps of TS-Bench AgentDojo-Traj: we translate only the user instruction into Germa
