Same Payload, Different Channel: Measuring Trust Asymmetry in Tool-Using Language Models
As language models take on agentic roles that call APIs, read tool outputs, and act on third-party content, their attack surface expands beyond what users type. Whether they treat a malicious instruction the same way regardless of where it arrives has not been studied systematically. We introduce the Safety Asymmetry Score (SAS), measuring how a model's susceptibility to adversarial content shifts depending on whether it arrives in the user message, tool metadata, or tool output, using matched payload pairs that hold the malicious text identical and vary only the channel. Across 10 production LLMs and three attack families, general-purpose models sharply discount instructions arriving as ...
| Authors | Mohammed Sameer Syed, Rozhin Yasaei |
|---|---|
| Primary category | cs.LG |
| Published | 30 May 2026 |
| Last updated at source | 21 August 2026 |
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