Personalized news recommenders are increasingly vulnerable to LLM-powered promotional attacks, where content providers rephrase headlines to manipulate rankings without altering underlying facts. Defending against such manipulation requires covering a wide spectrum of LLM generators and optimization strategies--ranging from stylistic prompting to reinforcement feedback--without incurring prohibitive training or inference costs. In this work, we present PACT (Pooled Attack Consistency Training), a model-agnostic defense that decouples relevance estimation from superficial rhetorical variations. PACT integrates three synergistic operations: it pools offline, fact-checked rewrites from multi-generator and multi-strategy attack sources; samples a constant number of variants at each training visit to yield an unbiased estimator of the pool-wide consistency objective under a fixed compute budget; and aligns original and rewritten ranking scores within the same user context jointly with click supervision. Requiring no architectural modifications or test-time filtering, PACT deploys the trained recommender with zero extra latency. Across nine experimental settings spanning three recommendation backbones and three unseen-generator attacks, PACT consistently achieves the lowest promotion rate and target exposure, outperforming the strongest defense baseline by up to 3.0 percentage points in promotion reduction under LANCE. Further evaluations verify that PACT's superiority holds robustly under unseen attack-strategy holdouts and adaptive white-box retraining, while keeping clean recommendation utility virtually intact.

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