A Layer-wise Study of How Style Rewriting Breaks the Calibration of Zero-Shot LLM Fake-News Detectors

Summary

A layer-wise study tests how style rewriting affects the confidence of zero-shot LLM fake-news detectors. Across LLaMA-3-8B, Qwen2.5-7B and Mistral-7B on three benchmarks, rewriting articles into sensational or emotionally charged styles, without changing facts, makes the models markedly less accurate, worse than random guessing under the strongest attack, while their confidence stays the same. The authors conclude that an LLM's confidence about veracity cannot be trusted as a safety signal when an article's style may have been manipulated.

Related items

• InfoSuppression Is Not Erasure: Mechanistic Analysis and Reactivation Boundaries of Subspace Projection for LoRA Backdoor DefenseSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoAnyBreak: Towards Jailbreaking Any T2I Model for Any ConceptSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoHarness Matters: Measuring and Dissecting Security Resilience in Web AgentsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoBeyond AUROC: Deployment-Aware Evaluation of Model-Internal Jailbreak DetectorsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoEvaluating the robustness of explainable AI methods to adversarial attacks in intrusion and malware detectionSimilar attack · OpenAlex (peer-reviewed AI security)