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Discrete Prompt Optimization Using Genetic Algorithm for Secure Python Code Generation
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DOI：10.1016/j.jss.2025.112682.png)
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Abstract
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• A discrete prompt optimization pipeline leveraging genetic algorithm, equipped with security-focused scoring and mutation functions for secure code generation. • Introduces two new security-specific LLM-assisted prompt mutation techniques called self-guided and feedback-guided mutation to generate more security oriented variations of code generation prompts. • The security-specific mutation techniques led to prompts with richer security cues compared to the prompts mutated by just using the generic techniques such as paraphrase, back translation and cloze transformation. • Prompts optimized using a combination of security-specific and generic mutation techniques led to the most improvement in the security of the LLM-generated code. • Prompts optimized on one LLM demonstrated lack of transferability to others indicating the importance of model-specific optimizations.
Keywords:
LLMs
Secure Code Generation
Prompt Optimization
Genetic Algorithms
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