arXiv subjects

Lixun Ma

Publications and source records attributed to Lixun Ma.

3 recordsLinked to original sources

arXiv2026

Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios

Large Language Models (LLMs) are widely used for code generation, yet their security behavior in realistic development workflows remains underexplored. Existing benchmarks often rely on explicitly specified security requirements, failing to capture real-world scenarios where prompts are frequently ambiguous or incomplete. In this paper, we adopt a developer-centric perspective and identify three representative risk scenarios that commonly lead to security vulnerabilities in LLM-generated code: Ambiguous Requirements, Under-Specified Operational Context, and Security--Functionality Conflict. Based on these scenarios, we construct a large-scale benchmark comprising 2,700 test cases, enabling fine-grained evaluation of LLM security under realistic conditions. Extensive evaluation of eight state-of-the-art LLMs reveals that all models exhibit average vulnerability rates exceeding 56\% across risk scenarios. We further demonstrate that security-aware prompting can substantially mitigate these risks, achieving up to 45\% improvement.

cs.CR↗

arXiv2025

I Know Who Clones Your Code: Interpretable Smart Contract Similarity Detection

Widespread reuse of open-source code in smart contract development boosts programming efficiency but significantly amplifies bug propagation across contracts, while dedicated methods for detecting similar smart contract functions remain very limited. Conventional abstract-syntax-tree (AST) based methods for smart contract similarity detection face challenges in handling intricate tree structures, which impedes detailed semantic comparison of code. Recent deep-learning based approaches tend to overlook code syntax and detection interpretability, resulting in suboptimal performance. To fill this research gap, we introduce SmartDetector, a novel approach for computing similarity between smart contract functions, explainable at the fine-grained statement level. Technically, SmartDetector decomposes the AST of a smart contract function into a series of smaller statement trees, each reflecting a structural element of the source code. Then, SmartDetector uses a classifier to compute the similarity score of two functions by comparing each pair of their statement trees. To address the infinite hyperparameter space of the classifier, we mathematically derive a cosine-wise diffusion process to efficiently search optimal hyperparameters. Extensive experiments conducted on three large real-world datasets demonstrate that SmartDetector outperforms current state-of-the-art methods by an average improvement of 14.01% in F1-score, achieving an overall average F1-score of 95.88%.

cs.SE↗

arXiv2024

ETGuard: Malicious Encrypted Traffic Detection in Blockchain-based Power Grid Systems

The escalating prevalence of encryption protocols has led to a concomitant surge in the number of malicious attacks that hide in encrypted traffic. Power grid systems, as fundamental infrastructure, are becoming prime targets for such attacks. Conventional methods for detecting malicious encrypted packets typically use a static pre-trained model. We observe that these methods are not well-suited for blockchain-based power grid systems. More critically, they fall short in dynamic environments where new types of encrypted attacks continuously emerge. Motivated by this, in this paper we try to tackle these challenges from two aspects: (1) We present a novel framework that is able to automatically detect malicious encrypted traffic in blockchain-based power grid systems and incrementally learn from new malicious traffic. (2) We mathematically derive incremental learning losses to resist the forgetting of old attack patterns while ensuring the model is capable of handling new encrypted attack patterns. Empirically, our method achieves state-of-the-art performance on three different benchmark datasets. We also constructed the first malicious encrypted traffic dataset for blockchain-based power grid scenario. Our code and dataset are available at https://github.com/PPPmzt/ETGuard, hoping to inspire future research.

cs.CR↗