arXiv:2609.15523v1 Announce Type: cross
Abstract: Logic attack graphs grounded in scanner output provide explicit and auditable attack path reasoning LLM-based agents lack. Integrating symbolic frame...
By Oliver Stevanovic, Jasmin Wachter
arXiv:2604.10800v2 Announce Type: replace-cross
Abstract: Learned classifiers deployed in agentic pipelines face a fundamental reliability problem: predictions are probabilistic inferences, not verif...
By Jugal Gajjar
The paper examines how Large Language Models (LLMs) have introduced a trustworthiness crisis in cybersecurity vulnerability assessment, producing artifacts known as "AI slop" such as hallucinated vulnerabilities and incorrect patches. It presents a taxonomy of these failures, attributes them to a mismatch between deductive reasoning and probabilistic generation, and introduces the Deductive Coverage Score as a measurable proxy. The authors review mitigation strategies, critique passive approaches, and propose active neuro‑symbolic verification, concluding with new evaluation tools—CVE‑Bench and Slop‑Score—to shift focus from linguistic fluency to mathematical verifiability.
By Junchen Ding, Jialiang Dong, Yichen Zhu, Yi Liu, Gelei Deng, Willy Susilo, Siqi Ma, Yuekang Li
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
MetaVul is a self‑improving framework that discovers and refines harnesses—code scaffolding around large language model agents—for vulnerability detection. Starting from a seed reasoning scaffold, it iteratively optimizes harnesses using feedback from paired vulnerable and patched functions, producing Pareto‑optimal designs that can be reused across different models. Experiments show that optimized harnesses can boost pairwise vulnerability detection by up to 25.8 percentage points and transfer effectively between models without further tuning.
By Ira Ceka, Arnav Garg, Ryan Lutz, Ting Zhang, Ratnadira Widyasari, Gail Kaiser, Baishakhi Ray
The paper introduces Trustworthy RAG, an evaluation agent designed to detect misinformation and knowledge poisoning in Retrieval-Augmented Generation systems. It combines natural language inference verification, a five-signal poison detector, and a weighted Trust Index to assess the reliability of retrieved content. Experiments on multiple LLMs show high accuracy and precision, with the agent effectively blocking unsafe advice in a secure-coding assistant scenario.
By Balkrishna Giri, Md Toufique Hasan, Jussi Rasku, Muhammad Waseem, Pekka Abrahamsson