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Large language models increasingly act as agents that call external tools, yet most tool-safety research examines only what follows a tool call. The prior decision of whether to call the tool remains largely unexamined and can influence downstream safety. Across the seven open-weight models, the unweighted model-averag...
Toketive is introduced, a simple yet powerful reference-free attack that exploits the tokenization-based side channel to detect modified knowledge and reconstruct the corresponding pre-edit response and shows that localized modifications should not be treated as robust knowledge-control boundaries without adversarial e...
This work introduces AgentREVEAL, a diagnostic framework for analyzing retrieval-induced safety degradation in LLM agents, and uncovers the Safe Source Paradox, a safety-utility trade-off for retrieval-enabled agents.
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