Open Source Code Scanning
Static analysis has to read your entire source tree to be useful, which makes a hosted scanner an awkward bargain: the most sensitive thing you own, including the very secrets the tool is meant to catch, gets uploaded to a third party just to be told what is wrong with it. The open source scanners here run inside your own pipeline, so source, findings, and any keys they surface never leave your infrastructure. Just as important, you can read and tune the rules that decide what counts as a vulnerability instead of trusting a vendor's hidden defaults, which is what separates a scanner developers respect from one they learn to route around.
Cypress
JavaScript testing framework for end-to-end and component testing in the browser
Trivy
All-in-one security scanner for containers, code, and Kubernetes
Selenium
Browser automation framework and ecosystem built around the W3C WebDriver specification
Gitleaks
Detect secrets in git repos, files, and piped input
TruffleHog
Find, verify, and analyze leaked credentials
Semgrep
Fast static analysis with rules that look like the code itself
Grype
Vulnerability scanner for container images, filesystems, and SBOMs
SonarQube
Self-hosted server for continuous code quality and security inspection
OSV-Scanner
Scans dependencies against the OSV vulnerability database
Our picks
Most teams run several together: one for code, one for secrets, one for dependencies and infrastructure.
Best all-around static analysis: Semgrep Semgrep's defining idea is that rules look like the source code you already write, so banning a risky pattern does not mean wrestling with syntax trees. It supports more than 30 languages, runs in the IDE, pre-commit, or CI, and analyzes code locally by default. The open Community Edition is enough to enforce standards and catch many bugs.
Broadest single scanner: Trivy Trivy folds several jobs into one tool: it scans container images, filesystems, Git repositories, and Kubernetes for known CVEs, misconfigurations, exposed secrets, and risky licenses, and can generate an SBOM. Individual scanners toggle for focused runs, and it plugs into GitHub Actions and a Kubernetes operator. A strong default when you want coverage without stitching tools together.
Best for verified secret detection: TruffleHog TruffleHog searches far beyond a repository - Git history, filesystems, chats, logs, and object stores - and classifies findings into more than 800 secret types. Its standout is verification: it can attempt to use a discovered credential to confirm whether it is still live, separating real exposure from noise.
Best for gating new code: SonarQube SonarQube runs as a self-hosted server whose dashboard separates problems newly introduced in recent changes from existing debt, so teams hold the line on new code without drowning in legacy issues. A quality gate sets the pass or fail bar a project must clear, across many languages.
Fitting a scanner to the risk it is meant to catch
Code scanning is really four jobs wearing one name: finding insecure patterns in code you wrote, catching secrets committed by mistake, flagging known-vulnerable dependencies, and checking infrastructure definitions for misconfigurations. Match the tool to the job and to your actual stack. Semgrep and CodeQL do static application security testing across many languages, and Bandit does it for Python alone; Gitleaks and TruffleHog hunt secrets; Trivy, Grype, and OSV-Scanner track vulnerable dependencies; Checkov and KICS scan Terraform, Kubernetes, and other infrastructure as code. Most teams end up running several, not one.
Where findings land decides whether developers act on them or route around them. A scanner that only reports after release gets ignored, and one that blocks every pull request without context gets bypassed, so look for clean CI behavior, baselines that separate new issues from old debt, and structured output. SonarQube is built around exactly this split, holding the line on newly introduced problems through a quality gate while keeping legacy findings out of the way. SARIF output, emitted by tools like Semgrep, Trivy, Checkov, and KICS, lets results flow into code review and dashboards instead of dying in a log.
Before you standardize, weigh rule control and the exit path. Programs live or die on tuning: suppressions with reasons, custom rules, severity mapping, and path exclusions matter more after week one than the first scan's raw count. Semgrep is notable here because its rules look like the code they match, which makes custom checks approachable. Confirm findings carry stable identifiers and enough context to reproduce locally, and that results export without losing status, so a scanner change becomes a clean baseline rather than lost history.
Related categories
Frequently asked questions
What is the difference between SAST, secret scanning, dependency scanning, and IaC scanning?+
They inspect different things. Static application security testing (Semgrep, CodeQL, Bandit) analyzes patterns in the code you wrote. Secret scanning (Gitleaks, TruffleHog) looks for credentials committed by mistake. Dependency scanning (Trivy, Grype, OSV-Scanner) checks third-party packages against known-vulnerability data. Infrastructure-as-code scanning (Checkov, KICS) finds misconfigurations in Terraform, Kubernetes, and similar files. They overlap in a security program but answer different questions, and most teams need more than one.
Does my source code leave my network when I scan it?+
With the tools here, it does not have to. Semgrep analyzes code locally by default and never uploads it, and scanners like Trivy, Grype, Gitleaks, and Checkov run entirely inside your CI. The nuance is which extras phone home - hosted rule updates, telemetry, or optional cloud platforms - so review what each tool sends. For regulated code, prefer scanners with an offline mode and a controlled update path.
Can these catch a secret that was committed and later deleted?+
Yes, and that is the point of scanning history rather than just the current files. Gitleaks walks every commit, so a key that was added and later removed is still found, and it reports the rule, file, commit, author, and a fingerprint. TruffleHog goes further by verifying whether a discovered secret is still live, which separates a real exposure from a rotated one and cuts the noise during triage.
How is CodeQL licensed for commercial, closed-source code?+
CodeQL's standard queries and libraries are open source under the MIT license, but the CLI and analysis engine ship separately and are free only for analyzing open source and for research. Running CodeQL against closed-source code requires a commercial license, typically through GitHub Advanced Security. If you want a fully open static analyzer for private code without that condition, Semgrep's Community Edition is the more direct fit.
How do I keep pull request scans from drowning developers in noise?+
Give pull request scans one job: flag only newly introduced issues, fast, so feedback is immediate. Baseline the existing findings so new work is not punished for old debt, and run deeper full-repository analysis on a schedule as security backlog rather than a surprise build break. SonarQube's quality gate encodes this directly, and requiring a written reason for each suppression keeps the noise down without hiding real risk.
Which output formats keep findings portable between tools?+
Structured output matters more than a pretty report. SARIF is the most useful because security and developer tools widely ingest it, and Semgrep, Trivy, Checkov, and KICS all emit it; JSON and API access work too when the schema preserves rule IDs, file paths, line ranges, severities, and fingerprints. Avoid tools whose only durable record is an HTML page, since that makes deduplication and migration painful later.
What happens if a scanner's rules or vulnerability data stop being updated?+
That is the real aging risk with scanning tools, more than the code itself breaking. A dependency scanner is only as current as its advisory feed, and a static analyzer only catches the patterns its rules describe, so both lose value quickly without maintenance. Keep configuration, suppressions, and policies in version control and store results in a system you own, so you can keep a stalled tool running while you stage a replacement. Gitleaks, for instance, is now feature complete and shipping only patches.