Retrieval-augmented generation (RAG) systems use external documents for facts and rules that guide answers. We study rule-scope misapplication, where a rule valid within its documented scope is applied to a question outside that scope. Correct facts can then support an incorrect answer even when the rule's boundary is visible. This exposes the limits of document-validity checks. We introduce LENS (Local Evidence, Non-local Steering), an LLM-agent-based attack framework for constructing auditable instances of this failure. Its Planner identifies a grounded rule, its legitimate use, intended misuse, and document roles. Proposer and Verifier agents construct and repair attack documents using local retrieval and reader feedback while preserving factual support and explicit scope boundaries. Across four QA benchmarks and three target generators, LENS achieves 47.1% target-error rate over 4,000 fixed attempts, including construction failures. A separate fixed-attempt audit yields 36.1% joint target-error rate under independent validity checks. Fact-preserving interventions link errors to rule scope. For 76.2% of target-matching default responses, the same model correctly judges the rule inapplicable when explicitly probed. With matched information and budgets, an applicability audit reduces target error from 70.8% to 16.4%, compared with 31.2% for task-alignment checking. These findings motivate checking and enforcing rule applicability during answer generation.

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