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In our study, a novel SAST-LLM mashup slashed false positives by 91% compared to a widely used standalone SAST tool.
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Models trained to cheat at coding tasks developed a propensity to plan and carry out malicious activities, such as hacking a customer database.
Unlike dynamic analysis techniques, SAST operates without executing the program, focusing entirely on the static codebase.