Youpeng Li

The widespread reliance on open-source software dramatically increases the risk of vulnerability exploitation, underscoring the need for effective and scalable vulnerability detection (VD). Existing VD techniques, whether traditional machine learning-based or LLM-based approaches like prompt engineering, supervised fine-tuning, or off-policy preference optimization, remain fundamentally limited in their ability to perform context-aware analysis: They depend on fixed inputs or static preference datasets, cannot adaptively explore repository-level dependencies, and are constrained by function-level benchmarks that overlook critical vulnerability context. This paper introduces Vulnerability-Adaptive Policy Optimization (VULPO), an on-policy LLM reinforcement learning framework for context-aware VD. To support training and evaluation, we first construct ContextVul, a new dataset that augments high-quality function-level samples with lightweight method to extract repository-level context information. We then design multi-dimensional reward structuring that jointly captures prediction correctness, vulnerability localization accuracy, and the semantic relevance of vulnerability analysis, thereby guiding the model toward comprehensive contextual reasoning. To address the asymmetric difficulty of different vulnerability cases and mitigate reward hacking, VULPO incorporates label-level and sample-level difficulty-adaptive reward scaling, encouraging the model to explore challenging cases while maintaining balanced reward distribution. Extensive experiments demonstrate the superiority of our VULPO framework in context-aware VD: Our VULPO-4B substantially outperforms existing VD baselines based on prompt engineering and off-policy optimization, improving F1 by 85% over Qwen3-4B and achieving performance comparable to a 150x larger-scale model, DeepSeek-R1-0528.

The integration of LLMs into vulnerability detection (VD) has shifted the field toward more interpretable and context-aware analysis. While post-training techniques have shown promise in general coding tasks, their systematic application to VD remains underexplored. In this paper, we present the first comprehensive investigation into the post-training pipeline for LLM-based VD, demonstrating that on-policy RL with GRPO consistently outperforms SFT, off-policy preference optimization methods, and specialized VD LLMs. Our study further reveals VD-specific post-training guidelines and insights beyond common practices: (1) For data curation, contrary to the widespread use of rationalization-based supervision in prior VD work, SFT based on rejection sampling proves more effective, as rationalization can introduce hallucinations; in RL training, the inherently skewed difficulty distribution of vulnerabilities leads difficulty-aware data filtering to drastically reduce data coverage, causing non-negligible performance loss, and undermines curriculum learning, while pair-based data scheduling can partially mitigate this. (2) For stage interactions, unlike preference optimization typically applied to lightly trained SFT models, increasing SFT epochs consistently benefits off-policy preference optimization in VD tasks; however, excessive SFT suppresses self-exploration in on-policy RL, limiting its gains. (3) For reward mechanisms, naively treating vulnerability classification correctness as reward signals leads to reward hacking, whereas fine-grained root-cause judgments provide more reliable credit assignment; specification-based rewards further improve efficiency at the cost of additional design and generation effort. (4) For evaluation protocols, LLM-as-a-Judge based on root-cause analysis offers a more robust alternative, albeit with variability across judge models.

The rapid advancement of pre-trained language models (PLMs) has demonstrated promising results for various code-related tasks. However, their effectiveness in detecting real-world vulnerabilities remains a critical challenge. While existing empirical studies evaluate PLMs for vulnerability detection (VD), they suffer from data leakage, limited scope, and superficial analysis, hindering theaccuracy and comprehensiveness of evaluations. This paper begins by revisiting the common issues in existing research on PLMs for VD through the evaluation pipeline. It then proceeds with an accurate and extensive evaluation of 18 PLMs, spanning model parameters from millions to billions, on high-quality datasets that feature accurate labeling, diverse vulnerability types, and various projects. Specifically, we compare the performance of PLMs under both fine-tuning and prompt engineering, assess their effectiveness and generalizability across various training and testing settings, and analyze their robustness to perturbations such as code normalization, abstraction, and semantic-preserving transformations. Our findings reveal that, for function-level VD, PLMs incorporating pre-training tasks designed to capture the syntactic and semantic patterns of code outperform both general-purpose PLMs and those solely pre-trained or fine-tuned on large code corpora. However, these models face notable challenges in real-world scenarios, such as difficulties in detecting vulnerabilities with complex dependencies, handling perturbations introduced by code normalization and abstraction, and identifying semantic-preserving vulnerable code transformations. Also, the truncation caused by the limited context windows of PLMs can lead to a non-negligible number of labeling errors, which is overlooked by previous work. This study underscores the importance of thorough evaluations of model performance in practical scenarios and outlines future directions to help enhance the effectiveness of PLMs for realistic VD applications.

The widespread adoption of open-source software (OSS) necessitates the mitigation of vulnerability risks. Most vulnerability detection (VD) methods are limited by inadequate contextual understanding, restrictive single-round interactions, and coarse-grained evaluations, resulting in undesired model performance and biased evaluation results. To address these challenges, we propose MAVUL, a novel multi-agent VD system that integrates contextual reasoning and interactive refinement. Specifically, a vulnerability analyst agent is designed to flexibly leverage tool-using capabilities and contextual reasoning to achieve cross-procedural code understanding and effectively mine vulnerability patterns. Through iterative feedback and refined decision-making within cross-role agent interactions, the system achieves reliable reasoning and vulnerability prediction. Furthermore, MAVUL introduces multi-dimensional ground truth information for fine-grained evaluation, thereby enhancing evaluation accuracy and reliability. Extensive experiments conducted on a pairwise vulnerability dataset demonstrate MAVUL’s superior performance. Our findings indicate that MAVUL significantly outperforms existing multi-agent systems with over 62% higher pairwise accuracy and single-agent systems with over 600% higher average performance. The system’s effectiveness is markedly improved with increased communication rounds between the vulnerability analyst agent and the security architect agent, underscoring the importance of contextual reasoning in tracing vulnerability flows and the crucial feedback role. Additionally, the integrated evaluation agent serves as a critical, unbiased judge, ensuring a more accurate and reliable estimation of the system’s real-world applicability by preventing misleading binary comparisons.

Federated learning (FL), an emerging distributed machine learning paradigm, has been applied to various privacy-preserving scenarios. However, due to its distributed nature, FL faces two key issues: the non-independent and identical distribution (non-IID) of user data and vulnerability to Byzantine threats. To address these challenges, in this paper, we propose FedCAP, a robust FL framework against both data heterogeneity and Byzantine attacks. The core of FedCAP is a model update calibration mechanism to help a server capture the differences in the direction and magnitude of model updates among clients. Furthermore, we design a customized model aggregation rule that facilitates collaborative training among similar clients while accelerating the model deterioration of malicious clients. With a Euclidean norm-based anomaly detection mechanism, the server can quickly identify and permanently remove malicious clients. Moreover, the impact of data heterogeneity and Byzantine attacks can be further mitigated through personalization on the client side. We conduct extensive experiments, comparing multiple state-of-the-art baselines, to demonstrate that FedCAP performs well in several non-IID settings and shows strong robustness under a series of poisoning attacks.

Currently, federated learning (FL) can enable users to collaboratively train a global model while protecting the privacy of user data, which has been applied to human activity recognition (HAR) tasks. However, in real HAR scenarios, deploying an FL system needs to consider multiple aspects, including system accuracy, fairness, robustness, and scalability. Most existing FL frameworks aim to solve specific problems while ignoring other properties. In this paper, we propose FedCHAR, a personalized FL framework with a hierarchical clustering method for robust and fair HAR, which not only improves the accuracy and the fairness of model performance by exploiting the intrinsically similar relationship between users but also enhances the robustness of the system by identifying malicious nodes through clustering in attack scenarios. In addition, to enhance the scalability of FedCHAR, we also propose FedCHAR-DC, a scalable and adaptive FL framework which is featured by dynamic clustering and adapting to the addition of new users or the evolution of datasets for realistic FL-based HAR scenarios. We conduct extensive experiments to evaluate the performance of FedCHAR on seven datasets of different sizes. The results demonstrate that FedCHAR could obtain better performance on different datasets than the other five state-of-the-art methods in terms of accuracy, robustness, and fairness. We further validate that FedCHAR-DC exhibits satisfactory scalability on three large-scale datasets regardless of the number of participants.