Influence Is Not Authority: When Causal Guardrail Signals Make Legitimate Tool Use Look Like an Attack in Tool-Using LLM Agents
Influence-based guardrails for tool-using LLM agents fail to distinguish legitimate authorised actions from adversarial ones when both depend on external data—a blind spot that could cause both missed attacks and blocked valid workflows.
Summary written by editorial AI · Source link below
arXiv:2608.29942v1 Announce Type: new Abstract: The key limitation of current state-of-the-art influence-based guardrails is that they do not reliably distinguish a legitimate, user-authorized action from a malicious, unauthorized action when both rely on external tool information. This ambiguity can cause benign actions to trigger unnecessary verification and intervention, reducing utility and adding latency. We expose this limitation through an authorization-equivalence audit of 96 conditions
Editorial Analysis
Enterprises relying on causal-influence guardrails for LLM agents risk both false positives blocking business processes and false negatives letting attacks through—neither acceptable in regulated environments.
Review your LLM agent safety architecture for reliance on influence-only guardrails and supplement with explicit authorisation checks.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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