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20242026
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8 papers · 1 filter

cs.SE2026

Vul4Py: Benchmarking Automated Vulnerability Repair in Python with Paired Exploit and Functional Oracles

Tan Bui, Ting Zhang, Ferdian Thung +4

Automated Vulnerability Repair (AVR) has advanced rapidly across program analysis, machine learning, and Large Language Models (LLMs), but a verifiable, head-to-head comparison of…

cs.SE2026

Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework

Chengran Yang, Ting Zhang, Jinfeng Jiang +9

Current learning-based Automated Vulnerability Repair (AVR) approaches, while promising, often fail to generalize effectively in real-world scenarios. Our diagnostic analysis revea…

cs.SE2025

CleanVul: Automatic Function-Level Vulnerability Detection in Code Commits Using LLM Heuristics

Yikun Li, Ting Zhang, Ratnadira Widyasari +13

Accurate identification of software vulnerabilities is crucial for system integrity. Vulnerability datasets, often derived from the National Vulnerability Database (NVD) or directl…

cs.SE2025

PatchSeeker: Mapping NVD Records to their Vulnerability-fixing Commits with LLM Generated Commits and Embeddings

Huu Hung Nguyen, Anh Tuan Nguyen, Thanh Le-Cong +8

Software vulnerabilities pose serious risks to modern software ecosystems. While the National Vulnerability Database (NVD) is the authoritative source for cataloging these vulnerab…

cs.SE2025

VERCATION: Precise Vulnerable Open-source Software Version Identification based on Static Analysis and LLM

Yiran Cheng, Ting Zhang, Lwin Khin Shar +6

Open-source software (OSS) has experienced a surge in popularity, attributed to its collaborative development model and cost-effective nature. However, the adoption of specific sof…

cs.SE2025

Fixseeker: An Empirical Driven Graph-based Approach for Detecting Silent Vulnerability Fixes in Open Source Software

Yiran Cheng, Ting Zhang, Lwin Khin Shar +6

Open source software vulnerabilities pose significant security risks to downstream applications. While vulnerability databases provide valuable information for mitigation, many sec…