The paper introduces AgentSecGraph, a static analysis framework that builds a Security-Aware Agent Dependency Graph (Security-ADG) for each security-sensitive operation in large language model (LLM) agents. It enriches operation identity with agent relevance, source and dependency evidence, trust-boundary context, guard evidence, and external-effect semantics. The authors also present AgentSecBench, a corpus of 67 real-world LLM-agent repositories, and demonstrate that their analyzer identifies thousands of operation candidates, recovers substantial dependency and guard evidence, and distinguishes guarded behaviors from vulnerabilities with high accuracy.
By Hang Cui
Large Language Models (LLMs) are evolving from simple code completion tools to repository‑scale agents capable of retrieving context, editing files, executing tools, and engaging in security‑sensitive workflows. A structured survey up to May 31 2026 reviews LLM work across software engineering and security tasks, adaptation mechanisms, artifact granularity, and evaluation design, and introduces an assurance framework that separates functional correctness, security, operational reliability, evidence provenance, and agent authority. The review highlights that while execution feedback and repository access improve engineering task completion, they do not guarantee security, and static‑analysis labels rarely ensure deployable correctness; it also identifies common validity threats and proposes a minimum reporting protocol and a research agenda focused on jointly secure‑and‑functional benchmarks, repository‑scale threat models, calibrated human oversight, longitudinal maintainability evidence, and reproducible agent evaluation.
By Wei Lin, Tao Zhou, Zhaofei Xie, Changgui Hong
arXiv:2606. 17283v1 Announce Type: cross Abstract: Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at the cost of the others.
By Xiang Mei, Jordi Del Castillo, Pulkit Singh Singaria, Haoran Xi, Abdelouahab Benchikh, Tiffany Bao, Ruoyu Wang, Yan Shoshitaishvili, Adam Doup\'e, Hammond Pearce, Brendan Dolan-Gavitt
The paper introduces CodePoisonRAG, a framework that poisons retrieval-augmented code generation systems by transforming benign code artifacts into malicious ones. It injects CWE-specific vulnerabilities and false safety claims into a single task-matched artifact, achieving high success rates across multiple generators and even against a defense system. The study demonstrates that attackers can target and propagate specific weaknesses without altering the underlying language model.
By Varun Gadey, Ziad Marey, Alexandra Dmitrienko
arXiv:2603.18740v3 Announce Type: replace-cross
Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, rang...
By Dimitris Mitropoulos, Nikolaos Alexopoulos, Georgios Alexopoulos, Diomidis Spinellis
arXiv:2607. 23088v1 Announce Type: cross Abstract: Large Language Models (LLMs) are widely used for code generation, yet their security behavior in realistic development workflows remains underexplored.
By Lixun Ma, Ruolong Ma, Bei Wang, Feng Wei, Zhenguang Liu, Lorenzo Cavallaro, Wentao Chen