R2Vul: Learning to Reason about Software Vulnerabilities with Reinforcement Learning and Structured Reasoning Distillation (2504.04699v1)

Abstract: LLMs have shown promising performance in software vulnerability detection (SVD), yet their reasoning capabilities remain unreliable. Existing approaches relying on chain-of-thought (CoT) struggle to provide relevant and actionable security assessments. Additionally, effective SVD requires not only generating coherent reasoning but also differentiating between well-founded and misleading yet plausible security assessments, an aspect overlooked in prior work. To this end, we introduce R2Vul, a novel approach that distills structured reasoning into small LLMs using reinforcement learning from AI feedback (RLAIF). Through RLAIF, R2Vul enables LLMs to produce structured, security-aware reasoning that is actionable and reliable while explicitly learning to distinguish valid assessments from misleading ones. We evaluate R2Vul across five languages against SAST tools, CoT, instruction tuning, and classification-based baselines. Our results show that R2Vul with structured reasoning distillation enables a 1.5B student LLM to rival larger models while improving generalization to out-of-distribution vulnerabilities. Beyond model improvements, we contribute a large-scale, multilingual preference dataset featuring structured reasoning to support future research in SVD.

Learning to Reason About Software Vulnerabilities with $R2Vul$

The paper presents a novel approach called $R2Vul$ which aims to improve the reasoning capabilities of LLMs in the domain of software vulnerability detection (SVD). Despite the promising performance of LLMs in various tasks, such as natural language understanding, the aspect of reliable reasoning in high-stakes environments like SVD remains a challenge. To address these limitations, $R2Vul$ introduces structured reasoning distillation combined with reinforcement learning from AI feedback (RLAIF). This methodological advance targets the twofold challenge of detecting software vulnerabilities and distinguishing valid security assessments from plausible yet misleading ones.

Core Contributions

1. Structured Reasoning Distillation: The paper introduces structured reasoning as an avenue to train LLMs more effectively for SVD. By leveraging insights and reasoning patterns from secure and insecure code constructs, $R2Vul$ offers a fine-tuned approach to LLM training, enabling improved interpretability and security awareness in LLM output.
2. RLAIF with Knowledge Distillation: Through RLAIF, $R2Vul$ contrasts high-quality reasoning against flawed ones, thereby refining the model's capability to produce logical security assessments. This contrasts with regular supervised fine-tuning (SFT) which fails to explicitly penalize misleading reasoning.
3. Large-Scale Multilingual Dataset: As a substantial resource for future research, the authors present a dataset featuring over 18,000 samples across multiple languages, enriched with structured reasoning annotations. This dataset supports the training of multilingual models capable of generalizing across diverse programming constructs and languages.
4. Model Generalization and Performance: Through empirical evaluation, the paper demonstrates $R2Vul$'s enhanced model capabilities, noting its robustness in generalizing to unseen vulnerabilities and handling class imbalance scenarios. Smaller student LLMs, finely tuned with $R2Vul$, rival larger counterparts, underscoring the approach’s cost-efficiency and computational feasibility.

Experimental Findings

The experimental evaluation spanned five programming languages, comparing $R2Vul$ against existing SAST tools and various tuning strategies—CLS, SFT, CoT—and instructive models like MSIVD and VulLLM. Across languages, $R2Vul$ consistently surpassed baseline methods. Furthermore, a noteworthy finding was the improved performance of smaller LLMs, outperforming even the teacher LLM in reasoning output. This offers insights into the practicality of deploying such models where computational resources are limited.

Practical Implications and Future Directions

The potential implications of this research are multifaceted. Practically, the approach provides a pathway to more effective AI-driven vulnerability detection systems, elevating security assessments by enhancing reasoning precision. Theoretically, structured reasoning distillation presents a viable model improvement strategy that could extend beyond vulnerability detection into other areas requiring nuanced security reasoning.

For future work, expanding the approach to cover additional reasoning types, such as R1 and o1 reasoning, could further enhance both the interpretability and effectiveness of LLMs in security contexts. Additionally, investigating zero-day vulnerability detection as a practical extension could prove lucrative in harnessing LLM abilities for early-stage security threat assessment.

In conclusion, $R2Vul$ is poised to make significant strides in bridging the reasoning gap in LLMs for software vulnerability detection. By instilling structured reasoning through reinforcement learning, the approach not only resolves current limitations but also sets the stage for more sophisticated security-aware AI systems.