The paper investigates whether the hidden activations of large language models (LLMs) contain signals about the vulnerability of C/C++ code when the code is provided as context. By extracting prefill token activations from four LLMs and training small MLP probes, the authors achieve an average F1 score of 41.7% across four benchmarks, with the best probe matching state‑of‑the‑art fine‑tuned classifiers on the Devign dataset. The results suggest that a coding LLM’s internal representation can inform vulnerability detection, opening the door to lightweight, model‑native screening methods.
By Alizishaan Khatri
arXiv:2607. 02825v1 Announce Type: cross Abstract: We release \textsc{JavaVulBench}, a benchmark dataset and evaluation harness for Java vulnerability detection.
By Norbert Sandor Szolnoki, Gabor Antal
arXiv:2609.06229v1 Announce Type: cross
Abstract: Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical...
By Yuanxiang Shi, Jiayi Lin, Xuanyong Lin, Liangcai Su, Yeheng Duan, Wei Wang, Qi Han, Bing Zhao, Wei Hu, Xander Xu, Chenxiong Qian
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:2606. 27091v1 Announce Type: cross Abstract: LLMs fine-tuned for security classification are usually evaluated on held-out examples from the same distribution as their training data.
By Ryan Fetterman
arXiv:2602. 14161v2 Announce Type: replace Abstract: Detecting prompt injection, jailbreak attacks, and harmful requests is critical for deploying LLM-based agents safely, yet current evaluation practices in this literature overestimate generalization.
By Max Fomin