Causality Laundering: Denial-Feedback Leakage in Tool-Calling LLM Agents
Tool-calling LLM agents can read private data, invoke external services, and trigger real-world actions, creating a security problem at the point of tool execution. We identify a denial-feedback leakage pattern, which we term causality laundering, in which an adversary probes a protected action, learns from the denial outcome, and exfiltrates the inferred information through a later seemingly benign tool call. This attack is not captured by flat provenance tracking alone because the leaked information arises from causal influence of the denied action, not direct data flow. We present the Agentic Reference Monitor (ARM), a runtime enforcement layer that mediates every tool invocation by co...
| Authors | Mohammad Hossein Chinaei |
|---|---|
| Primary category | cs.CR |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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