arXiv AI
Aug 13

The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark

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 AI
Aug 19

Probing the Prefill: Detecting Code Vulnerabilities via Latent Activations

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