Understanding SAST Technology and Capabilities

Understanding SAST Technology and Capabilities

SAST tools employ various techniques to identify security vulnerabilities in code. Data flow analysis tracks how user input moves through an application, identifying paths where untrusted data could reach sensitive operations. Control flow analysis examines program execution paths to detect issues like unreachable code or missing security checks. Pattern matching identifies known vulnerable code constructs based on extensive vulnerability databases.

Modern SAST tools go beyond simple pattern matching to understand application context and behavior. Semantic analysis comprehends code intent, reducing false positives by understanding when seemingly vulnerable patterns are actually safe. Inter-procedural analysis tracks vulnerabilities across function and module boundaries, catching issues that simpler tools miss. Some advanced tools even employ machine learning to identify novel vulnerability patterns.

The accuracy of SAST tools varies significantly based on language support and analysis sophistication. Strongly-typed languages like Java and C# typically yield better SAST results than dynamically-typed languages like Python or JavaScript. However, modern tools increasingly support dynamic languages through improved type inference and runtime behavior modeling. Understanding your tool's strengths and limitations helps set appropriate expectations and configure effective scanning rules.