arXiv:2602.04894v4 Announce Type: replace-cross 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 backend or source code. Second, FSTab provides a model-centric evaluation that quantifies how co
The increasing adoption of Large Language Models (LLMs) for code generation necessitates immediate attention to security vulnerabilities inherent in their outputs.
This research provides a framework to identify and predict recurring security flaws in LLM-generated software, crucial for securing future AI-powered development pipelines.
Developers can now anticipate and potentially mitigate black-box vulnerabilities in LLM-generated code, shifting from reactive patching to proactive security design.
- · Cybersecurity firms
- · Software developers
- · Organizations adopting LLM for code generation
- · Security researchers
- · Malicious actors exploiting LLM vulnerabilities
- · Organizations with poor code generation security practices
- · LLM providers neglecting security in their models
Security becomes a more explicit and integrated component of LLM code generation frameworks.
New security standards and auditing tools specifically for LLM-generated software will emerge across the industry.
The development of 'security-aware' LLMs where vulnerability avoidance is a core training objective could become a competitive differentiator.
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Read at arXiv cs.AI