SAST

Prompt injection attacks do not need to exploit software vulnerabilities; they exploit the model's difficulty in distinguishing instructions from data.

For AI-generated code, static application security testing (SAST) tools such as Semgrep and Bandit can be used to automatically scan for common vulnerabilities, and software composition analysis (SCA) tools can identify known CVE vulnerabilities in dependency packages.

DevSecOps mandates the embedding of security checkpoints within CI/CD pipelines, including automated tools such as Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Software Composition Analysis (SCA).

SAST (Static Application Security Testing) scans source code before compilation, while DAST (Dynamic Application Security Testing) simulates attacks in a runtime environment.

The industry widely considers the false positive rate of traditional SAST tools to be between 30% and 70%.

Semgrep, Fortify, and Checkmarx are commonly used Static Application Security Testing (SAST) tools.

CodeRabbit's use of large models for security investigation is essentially layering semantic reasoning on top of rule-based scanning to compensate for traditional SAST's shortcomings in cross-module understanding and alert noise reduction.

The industry typically measures SAST effectiveness using false positive and false negative rates, and introduces techniques such as taint analysis and symbolic execution to strengthen context tracking.

SAST is a technique that finds potential security defects by analyzing source code, bytecode, or binary files without running the program.

Traditional SAST tools have long been plagued by two problems: high false-positive rates and findings that are disconnected from business risk.

The advantages of SAST are that it can scan everything, gives stable results, and can be integrated into CI.

Static application security testing (SAST) matches dangerous patterns with predefined rules by parsing the abstract syntax tree (AST) or control flow graph.

The drawback of SAST is that rules cannot cover risks that depend on business semantics, and its ability to track context across functions and files is limited, making it prone to producing large numbers of false positives.

The industry currently mostly adopts hybrid workflows such as "LLM initial screening + human review" or "SAST alerts + LLM noise reduction" rather than letting the model issue audit conclusions independently.

Traditional SAST tools rely on predefined rules (such as taint analysis and pattern matching) to scan source code; they are good at finding known types of vulnerabilities, but have high false-positive rates on context-sensitive logic flaws.

Complete security testing should also cover dimensions such as authentication bypass, privilege escalation, CSRF, and sensitive information leakage, requiring dedicated tools such as SAST and DAST plus manual penetration testing.

Security integrations typically include dependency vulnerability scanning, secret leak detection and static application security testing (SAST).

Large model-driven code scanning is closer to 'semantic-level static analysis', not relying on predefined rules but understanding code intent based on training corpora, thereby finding logic flaws that rules struggle to cover.

SAST tools match patterned vulnerabilities via rules, DAST probes at runtime, while large model scanning is closer to semantic-level static analysis.

Traditional static analyzers (SAST) typically only perform pattern matching and report suspected vulnerabilities, but have high false-positive rates and cannot confirm whether a vulnerability is actually triggerable in the current code context.