SAST scanners are static application security testing tools that analyze source code, bytecode, or binaries without executing them to find vulnerabilities such as injection flaws, hardcoded secrets, and unsafe API usage. They differ from dynamic tools in one fundamental way: because they read the code directly, a SAST scanner can point you at the exact file and line where a problem lives, before the application ever runs.
Choosing among them is less about which finds the most issues and more about which produces findings your developers will actually act on. This guide covers how the engines work, how the popular options differ, and how to pick.
How SAST Scanners Work
Every SAST scanner shares a common pipeline even when the marketing makes them sound unique. It parses source into an abstract syntax tree, builds a control-flow graph describing how execution can move through the program, and then runs data-flow and taint analysis to track untrusted values.
Taint analysis is the heart of injection detection. The scanner marks data from untrusted sources, HTTP parameters, files, environment variables, as tainted, then follows it through assignments and calls. If a tainted value reaches a dangerous sink like a SQL executor or a shell command without passing a recognized sanitizer, the scanner reports it. The sophistication of a scanner is largely how accurately it models sources, sinks, sanitizers, and the paths between them.
Where scanners genuinely diverge is depth versus speed. A pattern-matching scanner evaluates rules against the syntax tree quickly and cheaply. A whole-program analysis engine builds a semantic model of the entire codebase and can trace data across files and function boundaries, which finds deeper flaws at the cost of time and memory.
The Major SAST Scanners
Semgrep sits at the fast, pattern-oriented end. Its rules read almost like the code they match, which makes writing and auditing custom rules unusually approachable. It runs in seconds to minutes, integrates cleanly into CI on the diff, and has a large community rule registry. Its tradeoff is depth: while newer versions added cross-function data-flow, it is not built to trace a taint path across a dozen files the way a whole-program engine is. For most teams that want fast, tunable pull-request feedback, it is the sensible default.
CodeQL, from GitHub, treats your code as a database you query. You write queries in a dedicated language to find patterns, and the engine performs deep interprocedural analysis. It is powerful for finding complex, multi-step vulnerabilities and it powers a lot of serious security research. The costs are a steeper learning curve and slower scans, and its licensing is free for open-source and for use within GitHub's ecosystem but restricted for some commercial automated use, so read the terms for your situation.
SonarQube is as much a code-quality platform as a security scanner. Many teams already run it for maintainability metrics, and its security rules come along for the ride. It is a reasonable choice when you want one tool spanning quality and security, though dedicated security scanners generally go deeper on vulnerability detection.
Commercial platforms such as Checkmarx and the SAST module inside broader developer-security suites add managed rule maintenance, triage workflows, and, increasingly, reachability context and AI-assisted fix suggestions. What you pay for is less the raw detection and more the operational layer that keeps findings actionable at scale.
The Metric That Actually Matters
The trap in evaluating SAST scanners is comparing raw finding counts. A scanner that reports 400 issues is not better than one that reports 40; it may simply be noisier. The number that predicts whether a program survives is the true-positive rate, the fraction of findings that represent a real, reachable problem worth fixing.
When developers hit a wall of false positives, they learn to dismiss findings reflexively, and the scanner becomes theater. This is why reachability analysis has become the differentiating feature at the high end. Knowing that a vulnerable code path is actually invoked in your application, rather than merely present, collapses a long list into a short, credible one. When you trial scanners, measure them on your own repositories and count how many findings your engineers agree are real, not how many the tool emits.
SAST Is Not the Whole Picture
A SAST scanner is blind to entire vulnerability classes. It cannot reason about runtime configuration, and it has no concept of business logic, so it will pass an endpoint with a broken authorization check that a human or a dynamic test would catch instantly. It also does not see your dependency graph the way software composition analysis does.
That is why a working program layers tools. SAST covers code you wrote; software composition analysis covers the libraries you pulled in, which is where most modern vulnerability volume actually lives; and a DAST scanner covers the runtime behavior neither static tool can see. Teams that treat SAST as a complete AppSec program discover the gaps the hard way. If you are assembling this layered setup, the security academy walks through how the pieces fit.
How to Choose
Start with your stack and your team. If you want fast, tunable feedback on pull requests and the ability to write custom rules for your own framework conventions, Semgrep is the low-friction entry point. If you need to hunt deep, multi-step vulnerabilities and have the appetite for a query language, CodeQL rewards the investment. If you are already on SonarQube for quality, turn on its security rules before buying anything new. And if you are operating at a scale where triage workflow and reachability are the bottleneck, that is when a commercial platform's operational layer starts to justify its cost.
Whatever you pick, run it in warn-only mode first, baseline the existing findings so gates block only new issues, and promote rules to blocking one high-signal set at a time. The rollout discipline matters more than the logo on the scanner.
FAQ
What is the best free SAST scanner?
For most teams, Semgrep is the best free starting point because it is fast, its rules are easy to read and customize, and it integrates cleanly into CI. CodeQL is free for open-source projects and offers deeper analysis, but it has a steeper learning curve and more restrictive terms for some commercial use.
How is a SAST scanner different from SCA?
A SAST scanner analyzes the code your team wrote, looking for flaws like injection and unsafe API usage. Software composition analysis analyzes the third-party dependencies you pulled in, matching them against known CVEs. Both are necessary because most modern vulnerability volume comes from dependencies, not first-party code.
Why do SAST scanners produce so many false positives?
Taint analysis is conservative and reports any path it cannot prove is safe, so unrecognized sanitizers and framework guarantees generate noise. The best way to reduce it is to prefer scanners with reachability context, baseline existing findings, and promote only low-noise rule sets to blocking status.
Can one SAST scanner cover every language?
Rarely well. Coverage and analysis depth vary by language even within a single tool, because each language needs its own parser and taint model. Teams with polyglot codebases often run one scanner broadly and supplement it with a language-specific tool where depth matters most.