arXiv:2610.33450v1 Announce Type: new Abstract: Large Language Models (LLMs) can generate functionally correct code that remains insecure. Security-focused fine-tuning can be expensive and is not always available, motivating inference-time approaches. Security-oriented prompting and retrieval can reduce vulnerabilities, but may also remove required behavior or apply defenses incompatible with the task.
Secure and Functional Code Generation with Self-Evaluation
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