Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software
LLM code generation creates predictable vulnerability patterns that enterprise development teams must systematically identify and remediate.
Summary written by editorial AI · Source link below
arXiv:2602.04894v4 Announce Type: replace Abstract: LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities. We study vulnerability persistence in LLM-generated software and introduce Feature--Security Table (FSTab) with two components. First, FSTab enables a black-box attack that predicts likely backend vulnerabilities from observable frontend features and knowledge of the source LLM, without access to the b
External link — opens at arXiv Crypto & Security in a new tab.
More from the AI Security Desk
- Hugging Face warns an autonomous AI agent hacked its network20 Jul
- Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking20 Jul
- Hidden in Thought: Transferable Chain-of-Thought Artifacts Induce Harmful Behavior20 Jul
- Poison to Detect: Detection of Targeted Overfitting in Federated Learning20 Jul
- Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation20 Jul