What Guides the Agent? Adjudicating Unauthorized Behavior via Localizing Behavior-Guiding Instructions
The study identifies injection vulnerabilities in LLM agents where untrusted external data can be dynamically interpreted as behavior-guiding instructions, allowing attackers to subvert agent decisions. The work proposes methods to localize and defend against these dynamic injections that occur during inference rather than at static input/output boundaries.
Why this matters
The study identifies injection vulnerabilities in LLM agents where untrusted external data can be dynamically interpreted as behavior-guiding instructions, allowing attackers to subvert agent decisions. The work proposes methods to localize and defend against these dynamic injections that occur during inference rather than at static input/output boundaries.
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