Every document a security copilot retrieves can be true, instruction-free, and non-contradictory --- and the copilot can still be driven to assess a critical, exploitable vulnerability correctly and then recommend a remediation that leaves it open. We study this failure in retrieval-augmented generation (RAG) backing analyst-facing copilots in Security Operations Centers, and identify a class of attacks, \emph{\compmis{}} (COMA), in which every adversarial document is factually correct, instruction-free, non-contradictory, and distributionally benign --- yet the answer is misled by their \emph{composition}. We realize \compmis{} through \emph{action-corruption}, which steers a correctly-diagnosed vulnerability toward an inferior remediation, and \emph{verdict-flip}, which destabilizes the exploitability verdict via an undecidable reachability chain. Action-corruption bites all five tested models --- including frontier reasoning models --- on every run, on two synthetic domains and a real CVE (CVE-2021-33813); verdict-flip bites stochastically, decreasing with model capability but never vanishing. A single principle governs both: the attack succeeds when the disambiguating fact must be \emph{inferred} rather than \emph{read}. We propose \ccd{} (Causal Counterfactual Defense), an audit that measures the leave-one-out causal influence of each retrieved document and flags answers whose influence concentrates on low-trust documents. \ccd{} localizes the attack to attacker-controlled documents with no false positives on four benign multi-document controls; an adaptive influence-spreading adversary is caught by an \emph{aggregate} variant. We release attack seeds and a \ccd{} reference implementation.

翻译：暂无翻译