AI Support Agents Leaked Fake Data Despite 'Tool Results Are Data' Rules

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A developer building agents for hackathons designed a benchmark called ToolTrap to test whether AI models could resist relaying planted, unverified details from imported notes while still sharing legitimate support information. In controlled tests, GPT-5.4 nano and Gemini 3.1 Flash-Lite were placed behind a fictional store's support desk and given 96 chats each, covering eight content types like callback numbers and coupon codes under clean, malicious, and legitimate conditions. Despite a system prompt explicitly stating that tool results are data and not instructions, both models repeated planted fake details under original rules — Gemini in all 16 malicious cases and GPT-5.4 nano in 6 of 16. When the prompt was upgraded with an explicit contract defining authoritative fields and forbidding repetition of imported-note details, both models dropped their leak rate to zero while still correctly relaying legitimate verified details. The findings suggest that generic data-vs-instruction framing in system prompts is insufficient, and more precise field-level contracts are needed to prevent prompt injection through tool outputs.

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