32 Best SAST Tools (2026)

What is SAST?

Static Application Security Testing (SAST) is a white-box security testing method that analyzes application source code, bytecode, or binaries for vulnerabilities without executing the program. SAST tools parse code into abstract syntax trees (ASTs), then apply rule engines, data flow analysis, and semantic checks to detect flaws like SQL injection, cross-site scripting (XSS), and buffer overflows — pinpointing the exact file and line number where each vulnerability exists.

Developers plug SAST tools into their IDEs or CI/CD pipelines to catch these code-level issues before anything ships. Because the analysis happens on the source code itself, SAST does not need a running application, a test environment, or network access — which makes it fast to run and easy to automate.

The roots of SAST go back further than most people realize. Compiler warnings and lint tools have flagged coding errors since the 1970s, but the first generation of commercial SAST products built specifically for security appeared around 2002–2003. Ounce Labs (2002, later acquired by IBM) and Fortify (2003, now under OpenText) were among the earliest vendors. Open-source alternatives followed over the next decade — tools like FindBugs for Java (2006, now SpotBugs), Bandit for Python, and Brakeman for Ruby on Rails. Today the market splits between free open-source SAST tools like Semgrep, Bandit, and SonarQube Community Edition and commercial SAST platforms like Checkmarx, Fortify, and Veracode that add enterprise reporting, compliance dashboards, and dedicated support.

SAST is particularly effective at catching vulnerability categories from the OWASP Top 10 that originate in source code. Injection flaws (SQL injection, OS command injection, LDAP injection), cross-site scripting (XSS), and server-side request forgery (SSRF) are all patterns that data flow analysis can trace from untrusted input to dangerous output. Hardcoded credentials, weak cryptographic algorithms, and insecure deserialization are also well within SAST’s detection range because they leave clear signatures in source code.

Beyond catching bugs, SAST plays a direct role in meeting compliance requirements. PCI DSS 4.0 (Requirement 6.2.4) mandates that organizations review custom software for vulnerabilities using manual or automated methods — SAST satisfies this. SOC 2 Type II audits regularly ask for evidence of code-level security testing as part of the security development lifecycle. ISO 27001 Annex A expects organizations to establish secure coding practices and verify them. Running SAST in CI/CD and keeping scan reports gives auditors the paper trail they need.

With the average cost of a data breach reaching $4.88 million in 2024 (IBM Cost of a Data Breach Report 2024), catching vulnerabilities in source code before they reach production is a financial necessity, not just a best practice. Organizations that deploy SAST early in the SDLC typically reduce remediation costs by 6x compared to fixing vulnerabilities discovered in production, according to IBM’s Systems Sciences Institute cost model.

According to the 2025 Gartner Magic Quadrant for Application Security Testing, SAST remains the most widely adopted AST category, with six vendors positioned as Leaders: Checkmarx (7 consecutive years), Veracode (11 consecutive years), OpenText Fortify (11 consecutive years), Black Duck/Coverity (8 years), HCL AppScan, and Snyk.

Unlike DAST tools that test running applications from the outside, SAST works at the code level and does not need a deployed environment. The trade-off is that SAST cannot detect runtime or configuration issues — a misconfigured web server, an exposed admin panel, or a broken authentication flow will slip past it. That is why many teams run it alongside DAST or IAST for fuller coverage. AppSec Santa compares every SAST tool on the market so you can find the best fit for your language stack and budget.

How SAST Works

SAST works by parsing source code into an abstract syntax tree (AST) — a structured representation that normalizes code regardless of programming language — and then applying multiple layers of analysis to detect security flaws. The process starts with a rule engine that matches known vulnerability patterns, then goes deeper with semantic analysis, data flow tracking, and control flow validation.

Understanding these analysis techniques helps you tell apart a lightweight linter from a deep-analysis engine, and explains the differences in scan time, accuracy, and price across SAST tools.

1

Abstract Syntax Tree (AST) Parsing

The tool parses your source code into an AST — a common format regardless of language — enabling faster and language-agnostic vulnerability detection.

2

Rule Engine

Applies language-specific, framework-relevant, and custom rules to identify security issues. Tools like Semgrep make it easy to write your own rules.

3

Semantic Analysis

SAST tools will look for the usage of insecure code and can even detect indirect calls that simple pattern matching would miss.

4

Structural Analysis

Checks for language-specific secure coding violations and detects improper access modifiers, dead code, insecure multithreading, and memory leaks.

5

Control Flow Analysis

Validates the order of operations by checking sequence patterns. It can identify dangerous sequences, resource leaks, race conditions, and improper initialization.

6

Data Flow Analysis

The most powerful technique. It tracks data flow from taint sources (attacker-controlled inputs) to vulnerable sinks (exploitable code), detecting injection flaws, buffer overflows, and format-string attacks. Enterprise tools like Coverity and Fortify perform deep inter-procedural data flow analysis across entire codebases.

7

Configuration Analysis

Checks the application's configuration files (XML, Web.config, .properties, YAML) and finds known security misconfigurations that code-only scanning would miss.

Which technique matters most depends on what you are trying to find. Pattern matching and rule engines catch the low-hanging fruit quickly — hardcoded passwords, use of deprecated crypto functions, missing input validation on obvious entry points. These are fast checks that run in seconds and work well as pre-commit hooks or quick CI scans. Semantic and structural analysis go deeper by understanding how your code actually behaves — whether a variable holds user-controlled input, whether an access modifier exposes an internal method — but they take more time and need a richer model of your language.

What is data flow analysis in SAST?

Data flow analysis (technique 6 above) is widely considered the gold standard for SAST detection accuracy. It tracks data from taint sources (HTTP parameters, database reads, environment variables) through the program’s execution paths to vulnerable sinks (SQL queries, file writes, HTML output), catching injection vulnerabilities that span multiple files and function calls. This is how enterprise tools find second-order SQL injection, where malicious input enters in one request and gets executed in a completely different code path.

Most commercial SAST tools combine several of these techniques in a single scan. Checkmarx and Coverity run data flow, control flow, and semantic analysis together, cross-referencing findings to reduce false positives. Snyk Code adds machine learning on top of semantic analysis to prioritize findings based on patterns from millions of real-world fixes. The layering is what distinguishes a deep-analysis engine from a fast linter — and it is also what determines scan time and resource requirements.

Not every tool does all seven.

Free open-source SAST tools like Bandit and Brakeman mostly stick to rule engines and pattern matching. That is enough for many teams — especially when combined with a tool like Semgrep that adds custom rules and cross-file analysis in its free tier.

Enterprise tools like Checkmarx, Coverity, and Fortify layer all seven techniques together, which is a big part of why they cost what they cost.

Quick Comparison

We track 13 free open-source SAST tools, 7 freemium options, and 8 commercial platforms — 32 tools total. The SAST market in 2026 ranges from free tools like Semgrep and Bandit that cover most CI/CD use cases, to enterprise platforms like Checkmarx One and Veracode that add compliance dashboards, ASPM correlation, and support for 35-100+ programming languages. The table below groups them by license type so you can quickly narrow down your shortlist.

For full reviews, see each tool’s page on our mega comparison.

SAST vs DAST vs IAST

SAST analyzes source code without running the application (white-box), DAST tests the running application from outside (black-box), and IAST combines both by instrumenting the runtime during testing (grey-box). Each method finds vulnerabilities the others miss, which is why most security teams deploy at least two together. For a full comparison with decision frameworks, real-world scenarios, and architecture guidance, see our SAST vs DAST vs IAST guide.

SAST in Your CI/CD Pipeline

SAST integrates into CI/CD pipelines by running automated code scans on every pull request, blocking merges when critical vulnerabilities are detected, and posting findings as inline code annotations where developers can act on them immediately. The typical pipeline has four layers: pre-commit hooks for instant feedback, PR-level scanning for comprehensive analysis, quality gates for enforcement, and baseline management for handling legacy code.

Running a scan manually is fine for a one-off audit, but the real payoff comes when every pull request gets scanned automatically before it merges. The goal is to make security feedback as routine as unit tests — developers see findings before code gets approved, not weeks later in a security review.

Pre-commit hooks are the fastest feedback loop. Tools like Semgrep and Bandit run in seconds and can catch obvious issues before code even leaves the developer’s machine. Semgrep’s CLI scans an average-sized project in under 10 seconds, making it practical as a git pre-commit hook without slowing developers down. This layer is not meant to be comprehensive — it catches the low-hanging fruit so the heavier scans downstream have less noise to deal with.

Pull request scanning is where most teams get the biggest value. Running a full SAST analysis on every PR using GitHub Actions, GitLab CI, or Jenkins means every code change gets reviewed for security before merge. Most tools can post findings directly as PR comments or inline code annotations, so developers see the issue in context. GitHub CodeQL does this natively for GitHub repositories, uploading results as code scanning alerts that appear on the pull request’s “Security” tab. Snyk Code and Semgrep both offer GitHub Actions that work the same way.

Quality gates add enforcement. Instead of just reporting findings, you block the merge when critical or high-severity vulnerabilities show up. SonarQube has built-in quality gate conditions that can check for new security hotspots, and Checkmarx lets you define policies that prevent merging when specific CWE categories are detected. The key is to start strict only on critical findings and loosen gradually — blocking on every medium-severity issue will make developers resent the tool.

Baseline management keeps the noise manageable. When you first introduce SAST to an existing codebase, the initial scan will likely produce hundreds or thousands of findings. Rather than dumping all of them on the team, baseline the existing findings and configure the pipeline to only flag new issues introduced by the current PR. SonarQube calls this the “new code period.” Bandit supports baseline files that exclude known findings. Over time, you chip away at the backlog through separate remediation sprints.

How long does a SAST scan take?

Scan time is one of the biggest practical barriers to SAST adoption in CI/CD. Lightweight scanners like Semgrep and Bandit finish in seconds to minutes even on large codebases, while full deep-analysis scans with tools like Checkmarx or Fortify can take 15 minutes to several hours depending on codebase complexity. A scan that takes 45 minutes on every PR will get disabled within a week. Most tools support incremental scanning — analyzing only the files that changed rather than the entire codebase — which cuts scan times by 80-90%. Veracode Pipeline Scan returns results with a median scan time of 90 seconds by focusing on the diff. Semgrep can be configured to scan only changed files using --diff-depth. Mend SAST offers three scan profiles (Fast, Balanced, Deep) that trade thoroughness for speed.

For monorepos, the challenge is avoiding full-codebase scans when only one service changed. Most CI systems support path-based triggers — you can configure GitHub Actions to run a SAST job only when files in a specific directory change. Pair this with incremental scanning and a large monorepo can get SAST feedback in minutes instead of hours. Tools like SonarQube and Checkmarx also support project-level configuration that maps subdirectories to separate scan targets.

A typical GitHub Actions setup runs Semgrep on every pull request, uploads SARIF results to GitHub’s code scanning dashboard, and blocks the merge if new critical findings appear. The whole workflow adds about 30–60 seconds to the CI pipeline for most repositories — negligible compared to build and test times.

AI-Powered SAST in 2026

AI-powered SAST refers to static analysis tools that use machine learning, large language models, or AI agents to improve vulnerability detection, reduce false positives, or generate automated fix suggestions. As of 2026, AI capabilities in SAST tools fall into three distinct categories: AI-assisted triage and remediation, semantic query engines, and agentic SAST that scans code inside AI editors before it reaches the repository.

AI-assisted SAST tools still use traditional rule-based engines or semantic analysis for detection. They layer AI on top for triage, prioritization, and auto-fix suggestions. Snyk Code uses its DeepCode AI engine — trained on millions of real-world commits — to suggest one-click fixes alongside each finding. Checkmarx One deploys two AI agents: Checkmarx One Assist for automated remediation and Developer Assist for catching issues in the IDE before code is committed. SonarQube added AI CodeFix that generates LLM-powered remediation suggestions. The detection engine in these tools is still deterministic rules and data flow analysis — AI handles the “what do I do about it?” part.

Semantic query engines represent a different approach. GitHub CodeQL treats your entire codebase as a relational database — compiling source code into a queryable representation of variables, functions, types, and data flows. Instead of matching patterns, you write declarative queries that describe the vulnerability you are looking for. This means CodeQL can find complex multi-step vulnerabilities (like a tainted value passing through 5 functions across 3 files before reaching a SQL query) that pattern-matching tools miss. The trade-off is that writing custom CodeQL queries requires learning a dedicated query language, which is steeper than Semgrep’s code-mirroring syntax.

Agentic SAST is the 2026 frontier. Tools like Mend SAST plug directly into AI code editors via MCP (Model Context Protocol) servers, integrating with Cursor, Claude Code, GitHub Copilot, Windsurf, and Amazon Q to scan AI-generated code before it even reaches your repo. The idea is simple: if AI is writing your code, AI should also be checking it. Checkmarx also entered this space with its Developer Assist agent that runs inside VS Code, IntelliJ, Cursor, and Windsurf.

This matters because AI-generated code introduces vulnerabilities at a comparable or higher rate than human-written code. A 2021 study by NYU researchers found that roughly 40% of GitHub Copilot suggestions contained security vulnerabilities when generating security-sensitive code (Pearce et al., “Asleep at the Keyboard,” NYU 2021). A follow-up study by Stanford researchers confirmed the pattern: developers using AI coding assistants produced less secure code than those writing it manually (Perry et al., Stanford 2023). As AI coding assistants become standard development tools in 2025-2026, scanning their output with SAST has become a critical security requirement rather than a nice-to-have.

Newer entrants are pushing AI further into the detection engine itself. DeepSource uses its Autofix AI to generate one-click remediation for detected issues, and its Narada model achieves 97% precision for secrets detection. Qodana (by JetBrains) brings 3,000+ IDE inspections to CI/CD pipelines with taint analysis that processes 7 million lines in under 30 minutes. Both tools combine traditional static analysis with ML-based prioritization to surface the findings most likely to be real vulnerabilities.

When evaluating tools in 2026, three questions are worth asking: does the tool use AI in its detection engine, or only in its remediation UI? Does it scan AI-generated code before it hits your repo? And does its AI produce actionable fix suggestions that developers can apply in one click, or just generic descriptions of the problem?

How to Choose a SAST Tool

Choosing the right SAST tool comes down to five factors: language and framework support, CI/CD integration, false positive rate, budget, and developer experience. The best tool for your team depends on your specific language stack, pipeline setup, and whether you need free open-source coverage or enterprise features like compliance dashboards and centralized policy management.

Here is what I would look at:

1. Language and framework support. This is the single most important filter. A tool that does not understand your framework will miss vulnerabilities specific to its patterns or drown you in false positives from patterns it misunderstands. Brakeman is the gold standard for Ruby on Rails — it understands Rails routing, ActiveRecord queries, and ERB templates deeply — but it is Rails-only. Bandit covers Python with 47 built-in checks. If you use multiple languages, look for multi-language tools: Semgrep covers 30+ languages, Checkmarx One covers 35+, and Veracode supports 100+ including legacy stacks like COBOL and RPG.

2. CI/CD integration. How easily does it plug into your pipeline? Look for native support for GitHub Actions, GitLab CI, Jenkins, or Azure DevOps. GitHub CodeQL is the easiest to set up if you are already on GitHub — it runs as a built-in Actions workflow with zero external configuration. Snyk Code and Semgrep both offer well-documented GitHub Actions that upload SARIF results to the code scanning dashboard. Enterprise tools like Checkmarx and Fortify offer plugins for every major CI system, but setup tends to involve more configuration.

3. False positive rate. This is what kills SAST adoption in practice. Developers stop looking at findings when half of them are noise. Commercial tools tend to be quieter out of the box because they invest in data flow analysis and ML-based prioritization. Cycode reports a 2.1% false positive rate on OWASP benchmarks. But open-source tools like Semgrep let you write precise custom rules that cut down false positives just as well — you just need to invest the time to tune them for your codebase.

4. Budget. Free open-source SAST tools cover most use cases for small and mid-size teams. Semgrep CE handles multi-language scanning with custom rules. Bandit and Brakeman cover Python and Rails specifically. SonarQube CE provides code quality plus security across 20+ languages. CodeQL is free for public repos. Enterprise tools add centralized reporting, compliance dashboards (PCI DSS, SOC 2, HIPAA mapping), cross-project portfolio views, and dedicated support — but the free options have gotten good enough that many teams never upgrade.

5. Developer experience. IDE integration, clear fix guidance, and fast scan times keep developers from ignoring findings. Snyk Code does well here with real-time scanning in VS Code, IntelliJ, and PyCharm plus AI-powered fix suggestions from its DeepCode engine. Qodana brings the same JetBrains IDE inspections developers already see locally into the CI/CD pipeline. Tools that show findings as inline code annotations in pull requests get higher fix rates than tools that send email reports to a separate dashboard.

Decision framework

If you are a startup or small team — Start with Semgrep plus Bandit for free SAST coverage across 30+ languages with easy CI/CD integration. Both are free, fast, and set up in GitHub Actions in under 10 minutes. You can add SonarQube CE later if you want code quality metrics alongside security findings.

If you are an enterprise with legacy code — Fortify (44+ languages including COBOL, ABAP, Fortran) or Checkmarx One (35+ languages with ASPM correlation) handle the broadest language stacks. Veracode is worth considering if you need binary analysis — it scans compiled bytecode across 100+ languages without requiring source code access, which is useful for third-party code audits.

If you are already on GitHub — CodeQL is free for public repositories and integrates natively with GitHub Actions and code scanning alerts. For private repos, it requires a GitHub Advanced Security license. It covers 12 languages with deep semantic analysis.

If developer experience is the priority — Snyk Code offers real-time IDE feedback with AI-powered fix suggestions. Its free tier works for individual developers, and the paid platform integrates SAST with SCA, container, and IaC scanning under one roof.

If you need compliance reporting — Coverity (Black Duck) maps findings to MISRA, AUTOSAR, ISO 26262, CERT, and DISA STIG standards. Fortify and Checkmarx both offer PCI DSS 4.0 and OWASP Top 10 2021 compliance reports out of the box. PCI DSS 4.0 Requirement 6.2.4 specifically mandates automated code review for custom software, making SAST with compliance mapping a direct regulatory need.

SAST Best Practices

SAST best practices focus on reducing false positives, integrating scans into developer workflows, and measuring remediation outcomes rather than just finding counts. The most common failure mode is not a bad tool — it is a good tool that nobody pays attention to because it was introduced poorly. Here is what works in practice:

1. Start with a baseline scan, then go incremental. Run a full scan once to get a snapshot of existing technical debt. Triage the results — suppress known false positives, categorize genuine findings by severity, and create a backlog for the real issues. Then switch to incremental scanning on every PR so developers only see findings they introduced. Nobody fixes 2,000 existing findings on day one, and asking them to will guarantee they resent the tool. SonarQube handles this through its “new code period” setting, and Bandit supports baseline files that exclude previously seen findings.

2. Own your rules. Default rule sets catch common vulnerability patterns, but your codebase has internal frameworks, custom authentication wrappers, and proprietary APIs that generic rules do not understand. Write custom rules for these. Semgrep makes this straightforward — its rule syntax mirrors your source code, so a developer can write a rule in minutes without learning a query language. CodeQL offers more expressive power through its declarative QL language for complex multi-step vulnerability patterns. Teams that invest in 10–20 custom rules tailored to their stack see measurably better signal-to-noise ratios.

3. Set severity thresholds that match your risk appetite. Block merges on critical and high findings. Warn on medium. Ignore informational noise entirely. These thresholds should be documented, agreed upon by engineering and security, and adjusted over time as the team gets comfortable. Starting too strict will create pushback; starting too lenient means findings pile up without action.

4. Make findings visible where developers work. PR comments beat email reports. IDE warnings beat PR comments. The closer a finding is to the developer’s cursor, the faster it gets fixed. Snyk Code provides real-time IDE feedback in VS Code and IntelliJ. GitHub CodeQL posts findings as inline code annotations on pull requests. The tools that win adoption are the ones that integrate into the developer’s existing workflow rather than requiring them to check a separate dashboard.

5. Combine with DAST and SCA. SAST finds code-level flaws. DAST catches runtime and configuration issues. SCA covers your third-party dependencies. Used together, they give you real coverage instead of partial visibility. A SQL injection found by SAST becomes much more urgent when your SCA scan confirms the vulnerable ORM version is also affected by a known CVE. See our SAST vs SCA guide for a detailed breakdown of how these two approaches complement each other.

6. Track fix rates, not just finding counts. A tool that finds 500 issues nobody fixes is worse than one that finds 50 issues that all get resolved. The metrics that matter are: mean time to remediate (how fast do findings get fixed after detection?), fix rate (what percentage of findings actually get resolved?), and finding density per KLOC (are you improving over time?). Report these to engineering leadership monthly to keep security visible.

7. Build a security champion program. Assign one developer per team as a security champion — someone who takes ownership of SAST findings, helps triage false positives, and evangelizes secure coding practices. Champions do not need to be security experts; they just need to care enough to keep the team’s finding queue clean. This decentralizes security responsibility and prevents a single AppSec team from becoming a bottleneck.

8. Measure what matters: finding density and remediation time. Track findings per thousand lines of code (KLOC) across your repositories over time. A decreasing trend means developers are writing more secure code, not just suppressing findings. Pair this with mean time to remediate — if your MTTR is under 7 days for critical findings, your SAST program is working. If it is over 30 days, the tool is producing reports that nobody reads.

Common SAST Mistakes

The most common SAST mistakes that reduce effectiveness include running only default rules, ignoring framework-specific patterns, treating all findings equally, and scanning only on the main branch instead of on every pull request. Here are the details on each:

1. Running only default rules. Every SAST tool ships with a generic rule set designed to work across many codebases. These rules catch common CWE patterns, but they miss vulnerabilities specific to your internal frameworks, custom authentication wrappers, and proprietary APIs. If you use a custom ORM, a homegrown session management library, or framework middleware that generic rules do not model, those code paths go unscanned. Invest time in writing custom rules — even 10–15 targeted rules for your most critical code paths will significantly improve detection coverage.

2. Ignoring custom framework patterns. A SAST tool that does not understand your framework will produce both false positives (flagging safe framework-handled patterns) and false negatives (missing vulnerabilities in framework-specific code). If your team uses Spring Security, Django REST Framework, or a custom authorization decorator, make sure your SAST tool has rules that model those patterns. Semgrep and CodeQL both let you define framework-aware rules. Some commercial tools like Checkmarx let you add custom sanitizer definitions so their data flow engine correctly models your internal security functions.

3. Treating all findings equally. A hardcoded test API key in a unit test file is not the same severity as a SQL injection in a production API endpoint. Teams that treat every finding as equally urgent burn out quickly and start ignoring the tool. Prioritize based on exploitability, exposure (is the code reachable from the internet?), and data sensitivity. Tools with ASPM capabilities like Checkmarx One and Cycode correlate findings with application context to help with this ranking automatically.

4. Not suppressing known false positives. When the same false positive shows up on every scan, developers learn to ignore all findings — including the real ones. Build a process for reviewing and suppressing confirmed false positives using inline comments (// nosec, // nolint, # nosemgrep) or centralized suppression rules. Document why each suppression was added so it can be reviewed later. A clean findings list with 20 real issues gets more developer attention than a noisy list with 200 items where half are noise.

5. Scanning only on the main branch. Running SAST only after code merges to main defeats the purpose of shift-left security. By the time a finding surfaces, the code is already in production or queued for release. Run scans on every pull request so developers can fix issues before the code merges. The incremental scan cost is minimal compared to the cost of finding a vulnerability in production.

6. Not correlating SAST findings with SCA and DAST results. A SQL injection found by SAST in a function that uses a vulnerable database driver flagged by SCA is a much higher risk than either finding alone. A reflected XSS found by SAST in a controller that DAST confirms is reachable from the internet is a confirmed vulnerability, not just a theoretical one. Teams that analyze SAST, SCA, and DAST findings in isolation miss these compounding risk factors. Unified platforms and ASPM tools help, but even without them, periodic cross-referencing of findings from different scan types improves prioritization.

Bandit

Open-Source Python Scanner

Free (Open-Source) 1 langs

Bearer

NEW

Data-First SAST with Privacy Scanning

Open Source (ELv2) / Part of Cycode

Brakeman

Open-Source Ruby on Rails

Free (Non-Commercial) 1 langs

Checkmarx

Gartner Leader for Enterprise SAST

Commercial

Codacy

40+ Languages with AI Code Protection

Commercial (Free for open-source, CLI is AGPL-3.0) 18 langs

Contrast Scan

SAST with Runtime Context

Commercial 20 langs

Coverity

Deep Analysis for Complex Codebases

Commercial 21 langs

DeepSource

AI-Powered Code Analysis with Autofix

Commercial (Free tier available) 16 langs

detect-secrets

Baseline secret management

Free (Open-Source, Apache-2.0)

Fortify Static Code Analyzer

Gartner Leader 11 Years, 33+ Languages

Commercial

GitHub CodeQL

Semantic Analysis, GitHub Native

Free for open-source, Commercial for private repos 12 langs

GitLab SAST

Built-in CI scanning

Included with GitLab (Free tier: limited, Premium/Ultimate: full features)

Gitleaks

Git secret scanner

Free (Open-Source, MIT)

gosec

Go Security Linter

Free/OSS 1 langs

Graudit

Grep-Based Code Auditing

Free (Open-Source, GPL-3.0) 10 langs

HCL AppScan

Gartner Leader with Free CodeSweep

Commercial (AppScan CodeSweep is Free) 18 langs

Horusec

Multi-Language Open-Source Orchestrator

Free/OSS (Apache 2.0) 14 langs

Kiuwan Code Security

30+ Languages Including Legacy

Commercial 31 langs

Klocwork

Safety-Certified C/C++ Analysis

Commercial (with Free Trial) 7 langs

Mend SAST

Agentic SAST for AI-Generated Code

Commercial 12 langs

NodeJSScan

Node.js Security Scanner

Free/OSS 2 langs

OpenGrep

NEW

Community Fork, Taint Analysis, 30+ Languages

LGPL-2.1 36 langs

PMD

Multi-Language Code Analyzer

Free/OSS 13 langs

PT Application Inspector

SAST+DAST+IAST+SCA Combined

Commercial 16 langs

Qodana

NEW

JetBrains IDE Inspections in CI/CD

Commercial (Free tier available) 14 langs

Semgrep

Fast Open-Source with Custom Rules

LGPL-2.1 (OSS CLI) / Commercial (Platform) 35 langs

Snyk Code

Developer-First SAST with AI-Powered Fix Suggestions

Commercial (Free tier available) 14 langs

SonarLint

Real-time IDE analysis

Free (LGPL-3.0) + Commercial Features with SonarQube/SonarCloud

SonarQube

35+ Languages, Code Quality + Security

Commercial (with Free Community Edition) 23 langs

SpotBugs

Java Bug Pattern Detection

Free/OSS (LGPL-2.1) 4 langs

TruffleHog

Verify live secrets

Free (Open-Source, AGPL-3.0) + Commercial Plans

Veracode Static Analysis

Binary Analysis, No Source Needed

Commercial 16 langs

Frequently Asked Questions

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SAST covers one aspect of application security. Browse other categories in our complete tools directory.

Suphi Cankurt

Application Security @ Invicti

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10+ years in application security. Reviews and compares 170 AppSec tools across 11 categories to help teams pick the right solution. More about me →