arXiv:2609.13728v1 Announce Type: cross
Abstract: Historical Linux kernel patches capture defect knowledge that applies beyond their original repair sites. Recent work has shown that large language m...
By Ruoyu Wang, Tuo Li, Jia Li
The paper introduces DUALLM, a dual-method pipeline that uses a Large Language Model and a fine‑tuned small language model to classify Linux kernel security patches with high precision. By analyzing commit titles, messages, diffs, and code context, DUALLM achieves 87.4% accuracy and an F1‑score of 0.875, outperforming existing methods. It successfully identified 111 recent patches addressing out‑of‑bounds or use‑after‑free vulnerabilities, with 90 confirmed true positives and proof‑of‑concept exploits demonstrating the validity of the classifications.
By Xingyu Li (UC Riverside), Juefei Pu (UC Riverside), Yifan Wu (UC Riverside), Xiaochen Zou (UC Riverside), Shitong Zhu (UC Riverside), Qiushi Wu (UC Riverside), Zheng Zhang (UC Riverside), Joshua Hsu (UC Riverside), Yue Dong (UC Riverside), Zhiyun Qian (UC Riverside), Kangjie Lu (UC Riverside), Trent Jaeger (UC Riverside), Michael De Lucia (UC Riverside), Srikanth V. Krishnamurthy (UC Riverside)
PatchBench introduces a benchmark to evaluate AI agents on realistic vulnerability patching tasks, addressing two key threats to validity: patch memorization and surface-level fixes that merely suppress crashes. The study finds that 25% of agent patches resemble historical developer patches, and that PoC-only validation inflates success rates by 1.83× on average. PatchBench mitigates these issues by selecting vulnerabilities whose true fixes lie outside the crash stack, migrating historical vulnerabilities into new contexts, and employing rigorous validation for security and semantic correctness.
By Chihao Shen, Jiacheng Li, Aastha Mahajan, Jeffery Siyuan Tian, Yonghwi Kwon, Yizheng Chen
arXiv:2604. 11950v2 Announce Type: replace-cross Abstract: While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation, limiting the practicality of automated bug detection.
By Zijie Zhao, Chenyuan Yang, Weidong Wang, Yihan Yang, Ziqi Zhang, Lingming Zhang
arXiv:2606. 17283v1 Announce Type: cross Abstract: Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at the cost of the others.
By Xiang Mei, Jordi Del Castillo, Pulkit Singh Singaria, Haoran Xi, Abdelouahab Benchikh, Tiffany Bao, Ruoyu Wang, Yan Shoshitaishvili, Adam Doup\'e, Hammond Pearce, Brendan Dolan-Gavitt
arXiv:2605. 17450v2 Announce Type: replace-cross Abstract: As software systems grow increasingly complex, automated vulnerability repair (AVR) remains difficult because the materials available to a repair system are usually failure artifacts rather than repair guidance.
By Simiao Liu, Fang Liu, Peiding Wang, Taichuan Li, Yinghao Zhu, Xiaoli Lian, Li Zhang