Hao Li

The deployment of Large Reasoning Models (LRMs) in high-stakes decision-making pipelines has introduced a novel and opaque attack surface: reasoning backdoors. In these attacks, the model's intermediate Chain-of-Thought (CoT) is manipulated to provide a linguistically plausible but logically fallacious justification for a malicious conclusion. While frontier models exhibit an intrinsic capacity to detect these fractures, compact, deployable models suffer from a fundamental verification gap, relying on fragile lexical heuristics that are easily bypassed by motivated adversaries. To bridge this gap, we propose TraceGuard, a process-guided security framework that transforms small-scale models into robust reasoning firewalls. Our approach treats the reasoning trace as an untrusted payload and establishes a defense-in-depth strategy through three synergistic phases: (1) Automated Forensic Synthesis, which generates contrastive reasoning pairs to isolate the specific logical point of fracture; (2) Step-Aware Supervised Fine-Tuning (SSFT), to instill a structural verification grammar; and (3) Verifier-Guided Reinforcement Learning (VGRL), utilizing Group Relative Policy Optimization. We identify and mitigate a critical failure mode of baseline alignment - lexical overfitting - whereby verifiers memorize adversarial triggers rather than auditing logical integrity. Our empirical evaluation demonstrates that TraceGuard acts as a security force multiplier: a 4B-parameter verifier achieves forensic precision on unseen attacks - including latent backdoors and post-hoc rationalizations - that rivals architectures two orders of magnitude larger. We further demonstrate robustness against adaptive adversaries in a grey-box setting, establishing TraceGuard as a viable, low-latency security primitive for the Trusted Computing Base.