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:2609.08040v1 Announce Type: cross
Abstract: The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing d...
By Jiahao Shi, Edward Tsien, Yifeng Di, Hongjiao Zhang, Yuan Tang, Ronit Dey, Ilona Shishov, Gal Netanel, Zvi Grinberg, Vladimir Belousov, Bat-Zion Rotman, Ilan Pinto, Tianyi Zhang
FuzzingBrain‑Bench V1 is a new benchmark that tests large language models (LLMs) on their ability to discover software bugs in open‑source projects. Unlike prior benchmarks that focus on a single target vulnerability, this benchmark gives models a Docker‑based harness and asks them to generate inputs that trigger as many distinct crashes as possible. The first version contains 77 challenges from 43 projects (36 C, 32 C++, 9 Java/JVM) and evaluates Claude Haiku 4.5, Claude Sonnet 4.6, and Claude Opus 4.8, with Claude Opus 4.8 achieving the highest score by triggering crashes in 60 of 77 challenges.
By Ze Sheng, Aleksandar Kezic, Zhicheng Chen, Jeff Huang
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)
arXiv:2605. 09504v2 Announce Type: replace-cross Abstract: We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared memory, parallel exploration, and evolutionary optimization.
By Michael A. Riegler, Inga Str\"umke
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