Capability-Routed Guard: Defending Large Reasoning Models Against Reasoning-Centric Jailbreaks

• URL: http://arxiv.org/abs/2608.07892v1
• Date: Sat, 08 Aug 2026 03:40:06 GMT
• Title: Capability-Routed Guard: Defending Large Reasoning Models Against Reasoning-Centric Jailbreaks
• Abstract summary: We introduce Capability-Routed Guard (CRG), a model-agnostic inference-time guardrail for closed-source large reasoning models.<n>CRG reframes prompt defense as a capability-routing problem, separating executable intent from untrusted reasoning context.<n>It effectively mitigates diverse reasoning-centric jailbreaks while preserving benign utility and avoiding common over-refusal issues.
• Score: 6.260656015665084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large reasoning models (LRMs) expose a new safety failure mode: adversarial prompts can manipulate reasoning context, task decomposition, or capability interpretation so that harmful objectives are processed as legitimate reasoning steps. Existing safeguards, including safety reminders, external classifiers, and self-checking wrappers, are often brittle because they either inspect the adversarial prompt directly or ask the target model to perform additional safety reasoning on the same surface that attacks exploit. We introduce Capability-Routed Guard (CRG), a model-agnostic inference-time guardrail for closed-source LRMs, where defenders cannot inspect hidden reasoning traces or modify model weights. CRG reframes prompt defense as a capability-routing problem: a side-channel controller first constructs a trusted representation of the user's authorized task, active context, safety evidence, and capability-transfer risk, separating executable intent from untrusted reasoning context. This representation supports route-specific execution, allowing CRG to block high-risk requests, constrain ambiguous ones, and forward low-risk requests through trusted active context. Finally, CRG applies TraceCheck to verify consistency with the authorized task and invokes a restricted fallback to preserve utility for low-risk benign prompts. Extensive experiments demonstrate that CRG effectively mitigates diverse reasoning-centric jailbreaks while preserving benign utility and avoiding common over-refusal issues. Further analysis shows that its components contribute complementary benefits, highlighting the importance of coordinated defense mechanisms for securing large reasoning models.

Related papers

• Operational Hallucination and Safety Drift in AI Agents [0.3823356975862005]
  Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution.<n>This paper empirically characterizes two observed failure modes across multiple state-of-the-art LLMs: Safety Drift and Operational Hallucination.<n>We propose an Action-Aware Supervision Layer - a lightweight, plug-and-play architectural blueprint incorporating intent-action consistency checks, state tracking, and forced termination primitives.
  arXiv  Detail & Related papers  (2026-07-20T17:01:50Z)
• Internalizing Safety Understanding in Large Reasoning Models via Verification [33.2377930782685]
  We propose a framework that internalizes safety specifications by training LRMs exclusively on safety verification tasks.<n>We demonstrate that learning to verify induces a strong generalization for response safety, significantly enhancing robustness against out-of-domain jailbreaks.
  arXiv  Detail & Related papers  (2026-05-09T13:05:00Z)
• CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation [68.53387633351484]
  We propose CORA (COnformal Risk-controlled GUI Agent), a post-policy, pre-action safeguarding framework that provides statistical guarantees on harmful executed actions.<n> CORA reformulates safety as selective action execution: we train a Guardian model to estimate action-conditional risk for each proposed step.<n>To rigorously evaluate this paradigm, we introduce Phone-Harm, a new benchmark of mobile safety violations with step-level harm labels.
  arXiv  Detail & Related papers  (2026-04-10T09:41:21Z)
• TraceGuard: Process-Guided Firewall against Reasoning Backdoors in Large Language Models [19.148124494194317]
  We propose TraceGuard, a process-guided security framework that transforms small-scale models into robust reasoning firewalls.<n>Our approach treats the reasoning trace as an untrusted payload and establishes a defense-in-depth strategy.<n>We demonstrate robustness against adaptive adversaries in a grey-box setting, establishing TraceGuard as a viable, low-latency security primitive.
  arXiv  Detail & Related papers  (2026-03-02T22:19:13Z)
• CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution [49.689452243966315]
  AI agents equipped with tool-calling capabilities are susceptible to Indirect Prompt Injection (IPI) attacks.<n>We propose CausalArmor, a selective defense framework that computes lightweight, leave-one-out attributions at privileged decision points.<n> Experiments on AgentDojo and DoomArena demonstrate that CausalArmor matches the security of aggressive defenses.
  arXiv  Detail & Related papers  (2026-02-08T11:34:08Z)
• RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic [56.38397499463889]
  Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks.<n>However, they remain vulnerable to hazardous instructions that may trigger unsafe behaviors.<n>We propose RoboSafe, a runtime safeguard for embodied agents through executable predicate-based safety logic.
  arXiv  Detail & Related papers  (2025-12-24T15:01:26Z)
• When Models Outthink Their Safety: Mitigating Self-Jailbreak in Large Reasoning Models with Chain-of-Guardrails [74.63933201261595]
  Large Reasoning Models (LRMs) demonstrate remarkable capabilities on complex reasoning tasks.<n>LRMs remain vulnerable to severe safety risks, including harmful content generation and jailbreak attacks.<n>We propose the Chain-of-Guardrail (CoG), a training framework that recomposes or backtracks unsafe reasoning steps.
  arXiv  Detail & Related papers  (2025-10-24T09:32:25Z)
• Bag of Tricks for Subverting Reasoning-based Safety Guardrails [62.139297207938036]
  We present a bag of jailbreak methods that subvert the reasoning-based guardrails.<n>Our attacks span white-, gray-, and black-box settings and range from effortless template manipulations to fully automated optimization.
  arXiv  Detail & Related papers  (2025-10-13T16:16:44Z)
• LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation [4.29885665563186]
  LATENTGUARD is a framework that combines behavioral alignment with supervised latent space control for interpretable and precise safety steering.<n>Our results show significant improvements in both safety controllability and response interpretability without compromising utility.
  arXiv  Detail & Related papers  (2025-09-24T07:31:54Z)
• ARMOR: Aligning Secure and Safe Large Language Models via Meticulous Reasoning [64.32925552574115]
  ARMOR is a large language model that analyzes jailbreak strategies and extracts the core intent.<n> ARMOR achieves state-of-the-art safety performance, with an average harmful rate of 0.002 and an attack success rate of 0.06 against advanced optimization-based jailbreaks.
  arXiv  Detail & Related papers  (2025-07-14T09:05:54Z)
• To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models [56.19026073319406]
  Large Reasoning Models (LRMs) are designed to solve complex tasks by generating explicit reasoning traces before producing final answers.<n>We reveal a critical vulnerability in LRMs -- termed Unthinking -- wherein the thinking process can be bypassed by manipulating special tokens.<n>In this paper, we investigate this vulnerability from both malicious and beneficial perspectives.
  arXiv  Detail & Related papers  (2025-02-16T10:45:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.