TraceGuard: Process-Guided Firewall against Reasoning Backdoors in Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908861442555904 |
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| author | Guo, Zhen Shi, Shanghao Li, Hao Yazdani, Shamim Zhang, Ning Tourani, Reza |
| author_facet | Guo, Zhen Shi, Shanghao Li, Hao Yazdani, Shamim Zhang, Ning Tourani, Reza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02436 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TraceGuard: Process-Guided Firewall against Reasoning Backdoors in Large Language Models Guo, Zhen Shi, Shanghao Li, Hao Yazdani, Shamim Zhang, Ning Tourani, Reza Cryptography and Security 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. |
| title | TraceGuard: Process-Guided Firewall against Reasoning Backdoors in Large Language Models |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2603.02436 |