"Basically, both generations were created to alert for code weaknesses that have mostly been solved in other ways (i.e., improvements in compilers and frameworks eliminated whole classes of CWEs), and the tools haven't evolved at the same pace as modern application development. They rely on syntactic pattern matching, occasionally enhanced with intraprocedural taint analysis. But modern applications are much more complex and often use middleware, frameworks, and infrastructure to address risks."
"So while responsibility for weaknesses shifted to other parts of the stack (thanks to memory safety, frameworks, and infrastructure), SAST tools spew out false positives (FPs) found at the granular, code level. Whether you're using first or second generation SAST, 68% to 78% of findings are FPs. That's a lot of manual triaging by the security team. Worse, today's code weaknesses are more likely to come from logic flaws, abuse of legitimate features, and contextual misconfigurations."
Traditional SAST performs deep scans but causes long runtimes; rules-based SAST favors developer experience with faster, customizable rules but limited coverage. Many code weaknesses have been mitigated by compilers, frameworks, and infrastructure, shifting risk responsibility away from granular code-level issues. Syntactic pattern matching and intraprocedural taint analysis produce high false positive rates—68% to 78%—and miss logic flaws, abuse of legitimate features, and contextual misconfigurations. Security teams expend significant effort triaging findings. Combining AI agents and multi-modal analysis in SAST enables contextual understanding across code, frameworks, and infrastructure, reducing false positives and false negatives and better surfacing modern application risks.
Read at InfoWorld
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