Open Internet by MindsNet

Evasion of Textual Safety Triggers in LLM Agents

Current safety alignment methods for large language models (LLMs) rely on detecting textual cues to prevent attacks. However, this approach fails when attacks are embedded in the sequence of tool calls rather than the text itself. As a result, existing guardrails are ineffective against agentic safety triggers, allowing attacks to bypass safety measures.

Computing & Technology, Computer Science, Machine Learning