Recognition Without Enforcement: Configuration-Dependent Failures in LLM Agent Instruction Arbitration and External Control
New research formalises how LLM agents can recognise instruction sources without actually enforcing trust boundaries — a gap that turns configuration choices into silent privilege-escalation vectors across multi-source agent architectures.
Summary written by editorial AI · Source link below
arXiv:2608.28502v1 Announce Type: new Abstract: LLM agents arbitrate among instructions from system prompts, users, memory, and tools, but this arbitration cannot be assumed to enforce trust boundaries. We identify a recognition-enforcement gap: source-format features (role-template position, channel metadata, formatting cues) are linearly decodable from model activations, and models can explicitly identify forged authority when prompted, yet some configurations still produce the conflicting to
Editorial Analysis
Enterprises deploying LLM agents for automation risk silent trust-boundary failures where tool or user instructions override system-level controls, creating an underappreciated attack surface in agentic AI.
Audit all deployed LLM agents for instruction-arbitration behaviour and enforce explicit trust-hierarchy validation in agent configurations.
LLM agents used in enterprise workflows may silently allow lower-trust instructions to override higher-trust ones, posing a governance risk that current configurations do not reliably prevent.
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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