Beware EviLLM: Enabling Vulnerability Injection via Large Language Models
Advances in large language models (LLMs) have enabled AI-driven code generation from natural language specifications, introducing new attack surfaces for injecting vulnerabilities into software. Prior work has studied this problem only in benign settings where vulnerabilities are introduced inadvertently, or under unconventional threat models where the LLM itself is malicious (backdooring) or the user is the attacker (jailbreaking). In this paper, we study a more realistic threat model: a third-party adversary, with capabilities comparable to existing cybercriminals, compromises the AI code generation pipeline to deliberately introduce vulnerabilities. We call this the EviLLM attack. We h...
| Authors | Zeezoo Ryu, Simon Chung, Muhammad Faraz Karim et al. |
|---|---|
| Primary category | cs.CR |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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