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аналитика - Secure Code Generation using Large Language Models
Securing LLM-Generated Code through Multi-Agent Static Analysis and Dynamic Fuzzing
A multi-agent framework that integrates static analysis and dynamic fuzzing to generate secure and functionally correct code by leveraging large language models.
Enhancing Secure Code Generation in Large Language Models via Oracle-Guided Synthetic Training Data
Large language models can be enhanced to generate more secure code by automatically synthesizing pairs of vulnerable and fixed code samples, and fine-tuning the models using this data.
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