1 citations · 1 across the 8 of their papers we have counts for
10 papers
Path2Spec: Path-Aware Specification Generation via Large Language Models
Dan Huang, Zhensu Sun, Huihui Huang +4
Formal specifications are critical for program verification, comprehension, and maintenance. However, manually writing them is costly and difficult to scale. Recent studies have sh…
Towards Scalable and Cost-Efficient Vulnerability Detection: A Study on Automatic Query Generation
Ivana Clairine Irsan, Ratnadira Widyasari, Huihui Huang +6
Static analysis remains a cornerstone of software security, yet the effectiveness of tools such as CodeQL is often limited by the substantial manual effort required to develop high…
PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution
Duc Manh Tran, Ratnadira Widyasari, Ivana Clairine Irsan +6
Ideally, the detailed information about a vulnerability should be made available together with the fixing commit. In practice, however, such details often become available only lon…
Revisiting Vulnerability Patch Identification on Data in the Wild
Ivana Clairine Irsan, Ratnadira Widyasari, Ting Zhang +7
Attacks can exploit zero-day or one-day vulnerabilities that are not publicly disclosed. To detect these vulnerabilities, security researchers monitor development activities in ope…
Finding Memory Leaks in C/C++ Programs via Neuro-Symbolic Augmented Static Analysis
Huihui Huang, Jieke Shi, Bo Wang +2
Memory leaks remain prevalent in real-world C/C++ software. Static analyzers such as CodeQL provide scalable program analysis but frequently miss such bugs because they cannot reco…
Beyond Function-Level Analysis: Context-Aware Reasoning for Inter-Procedural Vulnerability Detection
Yikun Li, Ting Zhang, Jieke Shi +10
Recent progress in ML and LLMs has improved vulnerability detection, and recent datasets have reduced label noise and unrelated code changes. However, most existing approaches stil…
PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing
Huihui Huang, Jieke Shi, Junkai Chen +6
Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with…
Out of Distribution, Out of Luck: How Well Can LLMs Trained on Vulnerability Datasets Detect Top 25 CWE Weaknesses?
Yikun Li, Ngoc Tan Bui, Ting Zhang +16
Automated vulnerability detection research has made substantial progress, yet its real-world impact remains limited. Prior work found that current vulnerability datasets suffer fro…
Back to the Basics: Rethinking Issue-Commit Linking with LLM-Assisted Retrieval
Huihui Huang, Ratnadira Widyasari, Ting Zhang +8
Issue-commit linking, which connects issues with commits that fix them, is crucial for software maintenance. Existing approaches have shown promise in automatically recovering thes…
R2Vul: Learning to Reason about Software Vulnerabilities with Reinforcement Learning and Structured Reasoning Distillation
Martin Weyssow, Chengran Yang, Junkai Chen +12
Large language models (LLMs) have shown promising performance in software vulnerability detection, yet their reasoning capabilities remain unreliable. We propose R2Vul, a method th…