arXiv AI By Jie Zhu, Ronik Bhaskar, Heather Zheng, Ben Y. Zhao, Kexin Pei

Natural Code Transformation Confuses Agents in Vulnerability Analysis

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arXiv AI
Sep 4

PatchBench: Evaluating AI Agents for Vulnerability Patching

PatchBench introduces a benchmark to evaluate AI agents on realistic vulnerability patching tasks, addressing two key threats to validity: patch memorization and surface-level fixes that merely suppress crashes. The study finds that 25% of agent patches resemble historical developer patches, and that PoC-only validation inflates success rates by 1.83× on average. PatchBench mitigates these issues by selecting vulnerabilities whose true fixes lie outside the crash stack, migrating historical vulnerabilities into new contexts, and employing rigorous validation for security and semantic correctness.

By Chihao Shen, Jiacheng Li, Aastha Mahajan, Jeffery Siyuan Tian, Yonghwi Kwon, Yizheng Chen
arXiv AI
2d ago

NameTrap: Steering AI Coding Agents Toward Security Vulnerabilities via Variable Names

The paper introduces NameTrap, a source‑code poisoning attack that renames identifiers in third‑party Python files to steer AI coding agents toward specific Common Weakness Enumeration (CWE) vulnerabilities. By iteratively aligning variable names with CWE‑specific conventions, the attack achieves success rates ranging from 22.5% to 75.0% across six agents and four vulnerability categories, outperforming random and neutral renaming strategies. The poisoned files contain no explicit instructions or vulnerable statements, and existing AI‑based and static security analyzers fail to detect the induced weaknesses.

By Chieh-Yuan Chang, Guan-Wei Chen, Chia-Yi Hsu, Chih Yang, Chia-Mu Yu, Chun-Ying Huang
arXiv AI
Aug 5

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection

arXiv:2601. 19138v2 Announce Type: replace-cross Abstract: Secure code review is critical during pre-integration, where Atlassian developers rely on lightweight analysis tools, while deep security assessment is deferred to later stages, delaying feedback and increasing remediation costs.

By Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam, Hong Yi Lin, Minwoo Jeong, Ming Wu
arXiv AI
Sep 15

Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale

arXiv:2609.15939v1 Announce Type: cross Abstract: Language-model agents increasingly operate over complete software repositories, yet cybersecurity evaluations primarily measure whether they can dete...

By Aman Priyanshu, Supriti Vijay, Kimia Majd, Xuhong He, Fraser Burch, Takahiro Matsumoto, Jianliang He, Baturay Saglam, Arthur Goldblatt, Zhuoran Yang, Amin Karbasi