The paper introduces Porting Benchmark, a dataset of 1,234 security patch backporting cases covering cross-version, cross-branch, and cross-repository scenarios, along with a unified evaluation framework. Five tools—spanning program analysis, LLM prompting, and LLM agents—are evaluated under aligned settings, revealing that performance varies significantly across tools and patch complexity, with success rates dropping sharply for structurally complex patches. The study identifies four root-cause categories for failures and demonstrates that reference-based benchmark scores may not fully reflect real-world remediation, as executable validation uncovers additional integration issues.
arXiv:2605. 05000v2 Announce Type: replace-cross Abstract: LLM agents have been increasingly adopted for solving security tasks.
By Hwiwon Lee, Jongseong Kim, Lingming Zhang
arXiv:2608. 11469v1 Announce Type: cross Abstract: AI agents are rapidly improving in cybersecurity capabilities when the source code is available for analysis, yet much of the software most consequential to cybersecurity, including malware, firmware, and proprietary applications, is available only as binaries.
By Jeremy Spence, Nicholas Assaderaghi, Jinhao Zhu, Nikil Ravi, Raluca Ada Popa, Guannan Wei, Yangruibo Ding, Zhuo Zhang
arXiv:2606. 18619v1 Announce Type: cross Abstract: The advent of agentic vulnerability detection is already becoming a watershed moment for software security.
By Zhengxiong Luo, Mehtab Zafar, Dylan Wolff, Abhik Roychoudhury
arXiv:2605. 26548v2 Announce Type: replace-cross Abstract: Finding a real vulnerability in complicated systems is a challenging, long-horizon task that demands reasoning across an entire codebase to produce a working proof-of-concept (PoC).
By Hwiwon Lee, Jiawei Liu, Dongjun Kim, Wubing Xia, Ziqi Zhang, Chunqiu Steven Xia, Lingming Zhang
The paper introduces the Static‑Pass Dynamic‑Fail (SPDF) phenomenon, showing that static analysis can miss vulnerabilities that are exploitable at runtime. Using a three‑stage pipeline—static scanning, LLM‑driven CWE reasoning, and autonomous exploit verification—it evaluated 1,355 Python samples and found that 14.53% of samples that passed static checks were actually exploitable. The study highlights that static‑analysis success and runtime security are distinct assurance layers, especially for AI‑generated and security‑sensitive code.
By Jessica Pourleyli, Maitreyee Das Urmi, Glaucia Melo
arXiv:2606. 19149v1 Announce Type: cross Abstract: Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs.
By Nahum Korda, Gadi Evron
The paper introduces Porting Benchmark, a curated dataset of 1,234 security patch backporting cases that span cross-version, cross-branch, and cross-repository scenarios, along with a common evaluation framework. Five tools—spanning program analysis, LLM prompting, and LLM agents—are evaluated under aligned settings, revealing that performance varies significantly across tools and that complex patches (Type-IV) see a sharp drop in success rate. The study identifies four root-cause categories for failures and demonstrates that reference-based benchmark scores may not fully capture real-world remediation, as executable validation uncovers additional integration issues.
By Jincheng Yang, Yulong Fu, Chengwei Liu, Lyuye Zhang, Fangyuan Zhang, Bingyang Ren, Yang Liu, Hui Li
arXiv:2608. 06471v1 Announce Type: cross Abstract: Despite recent advances, frontier large language model (LLM) agents remain limited in discovering and patching complex vulnerabilities in real-world software.
By Amine Lbath, Manan Suri, Aurelien Delaitre, Vadim Okun, Massih-Reza Amini, Ram D. Sriram, Dinesh Manocha
arXiv:2606. 13757v1 Announce Type: cross Abstract: Large language model (LLM) reviewers are increasingly used in pull-request (PR) workflows, where their approvals help decide which code is merged into a repository.
By Rui Melo, Riccardo Fogliato, Sean Zhou, Pratiksha Thaker, Zhiwei Steven Wu
arXiv:2602. 04894v4 Announce Type: replace-cross Abstract: LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities.
By Tomer Kordonsky, Amit LeVi, Maayan Yamin, Noam Benzimra, Avi Mendelson
arXiv:2509. 22097v5 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern.
By Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo