Best SAST tools for 2026: 15 solutions compared

TL;DR: SAST tools scan your source code for vulnerabilities before it runs. Maze fits teams that want to know which findings are actually exploitable, Checkmarx fits enterprises that need broad language coverage, Semgrep fits teams that want fast scanning with their own rules, and Snyk Code fits developers who want scanning in the IDE.

What are static application security testing (SAST) tools?

A SAST tool reads your source code, or sometimes your binaries, and looks for security flaws like SQL injection and authentication bypasses without running the application. Most teams run it in the IDE, on pull requests, or in the CI/CD pipeline, so developers hear about a problem while the code is still fresh in their heads.

Dynamic testing (DAST) attacks a running application from the outside. SAST works from the inside, looking at how the code is built and how data moves through it, which means it can catch issues before the code is deployed or even compiled.

The earlier you find a flaw, the cheaper it is to fix. A SQL injection caught in a pull request costs a developer a few minutes, while the same bug found in production can turn into an incident, a disclosure, and a weekend of patching.

SAST tools at a glance

Here’s how the 15 tools compare. We cover each one in more detail below.

Category	Solution	Best for	Key strengths	Things to consider
AI-native SAST platforms	1. Maze	Teams that want business-logic and traditional SAST findings, cut down to the ones that are actually exploitable	AI that finds and investigates every vulnerability, exploitability verdicts with evidence, fixes where developers work	SaaS only, and you can’t bring your own LLM key
AI-native SAST platforms	2. depthfirst	Teams looking for business-logic and authorization flaws	Logic-aware detection, exploit validation, fixes in the pull request	SaaS only, and it runs on its own models rather than frontier models
AI-native SAST platforms	3. ZeroPath	Teams that want fast AI scans on every pull request	Source-to-sink taint analysis, policies written in plain English, fix pull requests	Covers 15+ languages and is a smaller vendor
AI-native SAST platforms	4. Corgea	Teams that want logic-flaw detection with fixes they can review	Reachability analysis, about 900 CWEs, fix pull requests that explain the change	Newer platform, and detection depth varies by stack
Second-generation SAST platforms	5. Semgrep	Teams that want fast scanning with their own rules	Rules plus AI detection, a large community rule registry, fixes in the pull request	Rules need tuning, and large monorepos can be hard to scan
Second-generation SAST platforms	6. Endor Labs	Teams that want AI to triage SAST noise	AI triage with evidence, business-logic detection, custom rules	Setup and interface are still maturing
Second-generation SAST platforms	7. Aikido Security	Small teams that want SAST and more in one tool	Around 90% noise reduction, one-click AI fixes, custom rules	Tiered pricing rises with user count
First-generation SAST platforms	8. Snyk Code*	Developers who want scanning and fixes in the IDE	DeepCode AI engine, fast scans without a build, AI-generated fixes	Custom rules are limited to Enterprise, and per-seat costs add up
First-generation SAST platforms	9. Checkmarx	Enterprises that need broad language coverage	Hybrid engine, 60% fewer false positives, custom queries	Takes tuning, costs more, and large scans are slow
First-generation SAST platforms	10. Veracode	Enterprises that run SAST by policy	Crosscheck path analysis, 100+ languages and frameworks, source or binary scanning	Binary scans need packaging, scans can be slow, and licensing is complex
First-generation SAST platforms	11. OpenText Fortify	Enterprises that need flexible deployment	44+ languages, 350+ frameworks, on-premises or SaaS	Pricing is opaque, setup is complex, and scans are slow
First-generation SAST platforms	12. Black Duck Coverity	Safety-critical and embedded software teams	Deep C and C++ analysis, functional-safety certifications	Enterprise pricing, a long setup, and slow scans
First-generation SAST platforms	13. SonarQube	Teams that want code quality and security in one tool	Taint analysis into libraries, quality gates, 40+ languages and frameworks	Needs tuning and gets heavy on large codebases
Platform-integrated SAST	14. GitHub Code Security	Teams that build in GitHub	CodeQL, Copilot Autofix, dependency review	Central reporting is limited, and CodeQL doesn’t cover every language
Platform-integrated SAST	15. GitLab SAST	Teams that build in GitLab	Built into CI/CD, cross-file Advanced SAST, AI-generated fixes	Merge request findings and AI features need the Ultimate tier

*Snyk now positions itself as an AI security platform, led by Evo, its agentic security product line.

Why SAST tools matter

Catch vulnerabilities early in the SDLC

A flaw gets more expensive every stage it survives. Fixing it in a pull request means changing a few lines while the developer still remembers why they wrote them. Fixing it after release means reproducing the bug, finding whoever owns the code now, and shipping a patch under pressure.

SAST tools move that check to the start of the software development lifecycle (SDLC). Developers get feedback on insecure code as they write or commit it, and security and engineering spend less time passing tickets back and forth.

Support secure coding at scale

Once you have dozens of teams and hundreds of repositories, you can’t rely on every reviewer to spot every insecure pattern. Automated scanning gives every project the same checks, whatever its size and whoever owns it.

Because SAST runs on every commit or pull request, security reviews keep pace with how fast teams ship rather than becoming a gate at the end of a release.

Reduce risk from AI-generated code

AI coding assistants write code that works, and some of it is insecure. A model can produce a query that runs fine and is still open to injection, or an endpoint that returns data without checking who is asking.

The risk grows with adoption for three reasons. AI writes code faster than anyone can review it line by line. Models repeat insecure patterns they learned from public code. And the developer who accepts a suggestion didn’t write it, so they’re less likely to notice what it leaves out, like an authorization check. Running SAST on every change holds AI-generated code to the same standard as code a person wrote, at the speed it’s being produced.

Key features of SAST tools

1. Programming language and framework coverage

Most applications mix several languages, so a SAST tool needs to cover the ones you actually use, such as:

• Java
• Python
• JavaScript
• C#

Framework support matters just as much, because many vulnerabilities come from how a framework or library is used. A tool that understands your frameworks can catch insecure configurations and misuse of framework features that a generic scan would miss.

First- and second-generation tools have historically struggled to cover many languages well, because every new language needed its own parser, its own rules, and its own tuning. AI-native platforms reason about the code itself, so supporting a new language takes far less work than it used to.

2. Vulnerability detection

Traditional SAST tools find flaws by matching code against rules for known vulnerability classes, such as:

• The OWASP Top 10
• Language-specific vulnerabilities
• Framework-specific issues

The best rules-based tools continually update their rules as new threats appear and let you write your own, so you can enforce your coding standards and cut noise from findings that don’t apply to you. The limit is that rules only find what someone has already written a rule for.

AI-native tools work differently. They use AI to find vulnerabilities in the first place, which is how they catch business-logic flaws no rule describes, and then they triage every finding instead of handing your team the whole pile. The strongest tools also pull context from outside the code, like how the application is deployed and what it connects to, so the AI can gather the evidence it needs to decide whether a finding is real.

3. Data flow and taint analysis

Taint analysis follows untrusted input through your code to see whether it reaches something sensitive, like a database query or a system command, without being validated on the way. That lets a tool find issues that simple pattern matching misses, including:

• SQL injection from user input that reaches a query unsanitized
• Cross-site scripting (XSS) from untrusted input rendered in a page
• Command injection from user input passed to a system command
• Path traversal that gives access to files outside the intended directory

Traditional tools trace these paths with call graphs, which often break at dynamic calls, framework routing, and calls into open-source packages. When the trace breaks, the tool can’t tell you whether the vulnerable code is reachable. AI-native tools read the code and follow the path through those gaps, and a tool that shows you the full path makes the root cause much easier to understand and fix.

4. Remediation guidance developers can use

Finding a vulnerability is only half the job. A useful SAST tool tells the developer what’s wrong and how to fix it, with:

• A clear explanation of the issue
• Code examples of the secure version
• Links to relevant standards or documentation

The newest tools go further. They write the fix to match your code, open a pull request for the developer who owns it, and suggest a mitigation when no fix exists yet. That matters most on teams where not every developer is a security expert, because a ready-made fix gets merged in minutes, while a description of the problem can sit in a backlog for months.

5. Risk-based prioritization

A scanner can flag thousands of issues, and most of them won’t matter. Prioritization helps you work on the ones that do, based on factors like:

• Code context
• Data sensitivity
• Application exposure
• How and where the code runs in your cloud

A tool that only sees code has to guess at most of these. The best tools pull context from your cloud and your running application, like whether the vulnerable code is deployed, whether it’s reachable from the internet, what data it touches, and what controls sit in front of it. A critical finding in a service that never ships is noise, while a medium finding in your payment flow might be the most urgent thing on the list.

6. CI/CD pipeline integration

Running SAST in your CI/CD pipeline means every change gets checked automatically before it reaches production. It also makes security a normal part of shipping code rather than a separate step someone has to remember. With SAST in the pipeline, you can:

• Meet compliance requirements without manual checks
• Spend less time on manual security review
• Hold every release to the same security standard

7. IDE and pull request integrations

IDE plugins flag security issues while the developer is still typing, which:

• Keeps developers in the tool they’re already using
• Builds secure coding habits as people write
• Catches vulnerabilities before they’re committed

Pull request integrations scan the proposed change and flag issues before it merges, so new vulnerabilities don’t make it into the main branch.

Notable SAST tools

How we selected these tools: We looked at language and framework coverage, how deep detection goes (including data flow and business-logic analysis), how well each tool cuts false positives, the quality of its remediation guidance, and how it fits into IDEs and CI/CD pipelines. We grouped the tools by generation, from AI-native platforms to the tools that built the category, plus SAST that comes built into source code platforms. This list is a snapshot of the market, not every SAST tool that exists.

AI-native SAST platforms

AI-native platforms were built from the beginning to let AI reason about code and find vulnerabilities. Most don’t add AI on top of a rules engine. They use AI to find the vulnerability itself, and to understand what your code is supposed to do, which is how they find business-logic flaws as well as traditional vulnerabilities like SQL injection. They also decide whether a finding matters before anyone on your team has to look at it.

1. Maze

Best for: Teams that want business-logic and traditional SAST findings, cut down to the ones that are actually exploitable

Strengths: AI that finds and investigates every vulnerability, exploitability verdicts with evidence, fixes where developers work

Things to consider: SaaS only, and you can’t bring your own LLM key

Maze uses AI to find the vulnerabilities in your code, investigate every finding, and tell you which ones an attacker could actually exploit in your environment, with the evidence behind every answer. Maze Code also gathers context from your running application, building on what Maze Cloud already understands about your environment. AI-SAST runs on every pull request and across your entire codebase, and AI-SCA covers your open-source dependencies. Because Maze looks at your code, build, and cloud together, it can tell you whether a vulnerability in your code is exploitable given where and how that code runs.

Key features include:

• Exploitability, not just reachability: Maze uses AI to trace the path from an entry point to the vulnerable function instead of relying on a call graph. Call graphs tend to break at dynamic calls, framework routing, and open-source packages, which is exactly where traditional reachability gets stuck. Maze follows the path through them in whatever language you write, then checks whether an attacker can control what reaches the vulnerable code and whether that code is even running. Findings that are reachable but not exploitable get closed, and you can open any verdict to see every step behind it.
• Business-logic and traditional vulnerabilities: Maze reasons about what your application is supposed to do, so it finds business-logic bugs like authentication bypasses and account takeovers that no rule was written for, along with the SQL injection and cross-site scripting that traditional scanners catch.
• Every pull request and your entire codebase: AI-SAST checks every pull request before it merges and reviews your entire codebase to catch flaws that span services and repositories.
• AI that finds the vulnerabilities: Maze uses AI to find the vulnerabilities in your code and dependencies, then investigates every one. If you already run something like Dependabot and want to keep it, Maze will investigate, triage, and deduplicate those findings too.
• Severity based on your environment: Maze scores each exploitable finding on impact and likelihood using your cloud context, so a critical finding in code that never sees real traffic doesn’t outrank a medium finding in your payment flow.
• Fixes where your developers work: Maze writes a fix that matches your code, finds the developer who owns it, and opens a pull request for them. When no fix exists yet, it recommends a mitigation, and you can hand any fix to the coding agent your team already uses.
• CI/CD coverage: Maze runs in CI/CD pipelines including GitHub Actions, GitLab CI, and CircleCI, and shows its findings in the pull request.

2. depthfirst

Best for: Teams looking for business-logic and authorization flaws

Strengths: Logic-aware detection, exploit validation, fixes in the pull request

Things to consider: SaaS only, and it runs on its own models rather than frontier models

depthfirst’s code scanner looks at business logic, data flows, and how services talk to each other, rather than matching known patterns. It goes after flaws like broken authorization, insecure direct object references, privilege escalation, and subscription tier bypasses, and it connects low-severity findings into multi-step attack chains. When it can, it tests the running application to confirm a finding is exploitable, then delivers the fix as a pull request or PR comment written to match your codebase.

Key features include:

• Logic-aware detection: depthfirst says it finds authorization and business-logic flaws by reasoning about what your code is supposed to do.
• Attack chains: It links findings that look minor on their own into multi-step attacks.
• Exploit validation: Its Agentic Pentester tests the running application to confirm which findings can be exploited.
• Pull request and full-codebase scans: Pull request scans run in a few minutes, and deep scans cover the whole codebase.
• Fixes in the pull request: It writes fixes as pull requests or PR comments, and the vendor reports that four in five fix recommendations are accepted and merged.
• Security-specific models: depthfirst trains its own security models and says they run at a fraction of the cost of frontier models.
• Supply chain and secrets: The platform also covers reachability-based SCA, blocks malicious packages with its Dependency Firewall, and detects secrets.

3. ZeroPath

Best for: Teams that want fast AI scans on every pull request

Strengths: Source-to-sink taint analysis, policies written in plain English, fix pull requests

Things to consider: Covers 15+ languages and is a smaller vendor

ZeroPath combines large language models with program analysis to find vulnerabilities in code and dependencies. It focuses on business-logic and authentication flaws, such as missing authentication checks, insecure direct object references, race conditions, and ways around authorization. Because detection is driven by an LLM rather than a rule database, it can pick up new vulnerability classes as they appear, and it scans a pull request in under a minute.

Key features include:

• Source-to-sink taint analysis: It follows untrusted data through the application to find injection, deserialization, and command execution issues, and it understands authentication guards and framework-specific sanitization.
• Business-logic and authentication detection: It finds missing authentication, IDOR, race conditions in payment flows, authorization bypasses, weak crypto, and missing validation.
• Policies in plain English: You can write rules in plain English, like “flag endpoints that return full user objects including passwords.”
• AI-written fixes: Findings come with readable reports, an AI-assessed CVSS 4.0 score, and pull requests written to match the project’s style.
• SAST and SCA together: It treats vulnerable dependencies as sinks, so it checks whether a dependency CVE can actually be exploited, not only whether the library is reachable.
• New vulnerability classes: It detects new classes, such as prompt injection, without anyone writing a rule first.
• Language and platform support: It supports more than 15 languages and works with GitHub, GitLab, Bitbucket, and Azure DevOps.

4. Corgea

Best for: Teams that want logic-flaw detection with fixes they can review

Strengths: Reachability analysis, about 900 CWEs, fix pull requests that explain the change

Things to consider: Newer platform, and detection depth varies by stack

Corgea’s AI SAST scanner runs on its CodeIQ technology, which looks at how an application works rather than just its syntax. Its earlier BLAST engine is now part of Corgea AI SAST. It finds business-logic flaws that traditional scanners miss, including broken authentication and missing authorization checks buried in real application flows. It pairs detection with reachability analysis and delivers fixes in the pull request, across more than 20 languages and frameworks.

Key features include:

• Business-logic detection: It catches authentication flaws, authorization gaps, and risky code paths that syntax-only scans don’t see.
• Reachability analysis: Starting from public routes like a login endpoint, it traces runtime paths to potentially vulnerable code and shows where several routes lead to the same weak point.
• Fixes that explain themselves: Each fix comes with an explanation of why the change is safer, so developers can review it and learn from it.
• Fewer false positives: It filters out noise so teams focus on the findings most likely to be real risk.
• Language and framework coverage: It supports more than 20 languages and frameworks, including .NET, Django, Rails, Gin, Express, and Spring.
• Source control integrations: It works with GitHub, GitLab, Azure DevOps, Bitbucket, and Harness.
• IDE and coding agent integrations: It runs in Visual Studio Code, Cursor, Visual Studio 2022, and IntelliJ, and it works with coding agents.

Second-generation SAST platforms

Second-generation platforms arrived once SAST was established and went after what developers disliked about it, like slow scans, rigid rules, and noise. They brought fast rule engines you can customize, AI triage, and all-in-one platforms built for smaller teams. Most are now adding AI on top of those engines. That builds on a mature base, and it also means fitting AI-native workflows into architectures that were designed for rules-based detection, which is hard.

5. Semgrep

Best for: Teams that want fast scanning with their own rules

Strengths: Rules plus AI detection, a large community rule registry, fixes in the pull request

Things to consider: Rules need tuning, and large monorepos can be hard to scan

Semgrep Code combines rule-based detection with AI through what it calls Multimodal. The rules catch classic issues like cross-site scripting and SQL injection, and the AI looks for harder ones, like insecure direct object references and business-logic flaws. It uses the context around a finding to cut down what your team has to triage, and it puts step-by-step fix instructions in the pull request.

Key features include:

• Multimodal detection: Rules handle classic vulnerabilities and AI handles complex logic flaws, with your organization’s context applied to both.
• Noise reduction: It uses the context around a finding to spot false positives that static analysis alone would miss, and it learns from your triage decisions.
• Fix instructions in the pull request: Developers get step-by-step remediation in their pull requests, so they can fix real findings before security reviews them.
• Memory of past triage: Once an issue is triaged, Semgrep keeps that context and uses it to judge similar findings on future scans.
• Rule registry: You can use rules written by Semgrep and its community.
• Rule playground: You can write, test, and share your own rules.
• Pipeline integration: It runs in pull requests and CI.

6. Endor Labs

Best for: Teams that want AI to triage SAST noise

Strengths: AI triage with evidence, business-logic detection, custom rules

Things to consider: Setup and interface are still maturing

Endor Labs combines static analysis with AI to triage findings and find complex flaws. It uses data flow analysis across files and functions to flag likely false positives, so your team reviews the issues that carry real risk. It also detects business-logic and authentication flaws, and it works inside AI code editors so developers can fix issues before they commit. Its AI SAST covers 13 languages plus IaC YAML, and the wider Endor platform also covers open source dependencies with reachability analysis.

Key features include:

• AI triage: It reads the code, traces data flow, and reasons about context to validate each finding, and it shows the evidence for each decision.
• Business-logic and access-control detection: It finds logic flaws and broken access control, the kind of issues that usually show up in pentest reports, without anyone writing rules for them.
• Policy engine: Policies decide what happens with a finding, and it flags when a pull request changes your security posture in a way that could add exploitable flaws.
• Custom rules and context: You can add Semgrep-compatible rules and OPA policies, and add AI context rules that the AI uses as reference material.
• Fixes in context: Findings include the exact snippet, the rules triggered, the CWE, the AI’s reasoning, and a recommended fix you can apply in your AI code editor.
• Language coverage: Its AI SAST covers 13 languages, including Java, JavaScript, Python, C#, C/C++, Go, Kotlin, TypeScript, Ruby, Rust, Scala, and Swift, plus IaC YAML.
• Dashboards: Dashboards track severity and risk over time and show which projects carry the most risk.

7. Aikido Security

Best for: Small teams that want SAST and more in one tool

Strengths: Around 90% noise reduction, one-click AI fixes, custom rules

Things to consider: Tiered pricing rises with user count

Aikido Security includes SAST in a platform that also covers dependencies, secrets, containers, and cloud. Its SAST rules are tuned to cut false positives by around 90 percent by filtering out unreachable vulnerabilities, and it adjusts severity based on context you give it. It offers one-click AI fixes, runs in the IDE and CI/CD pipeline, and connects to AI coding tools through an MCP plugin that scans generated code.

Key features include:

• Noise reduction: Its rules are tuned to cut false positives by around 90 percent, and you can adjust them further.
• Severity based on context: You tell Aikido whether a repo is internet-facing or handles sensitive data, and it adjusts severities to match.
• AI AutoFix: It suggests AI-generated fixes you can apply in one click.
• Custom rules: You can write rules for risks specific to your codebase.
• IDE and pull request checks: It scans in the IDE as you write and can block merges by severity, type, or context.
• MCP plugin for AI code: The Aikido MCP plugin scans code from AI coding tools for vulnerabilities and hardcoded secrets as it’s generated.
• Language and platform coverage: It supports major languages including JavaScript, TypeScript, Python, .NET/C#, Java, Rust, PHP, Ruby, and Go, and it works with GitHub, GitLab, Bitbucket, and Azure DevOps.

First-generation SAST platforms

First-generation platforms built the SAST category. Checkmarx, Veracode, Fortify, and Coverity made static analysis an enterprise practice, with deep language coverage, compliance reporting, and on-premises options that keep them the standard in regulated and safety-critical industries. Snyk did more than anyone to bring security to developers, putting scanning in the IDE and the pull request. That depth and reach usually come with more setup, more tuning, and longer scans.

8. Snyk Code*

Best for: Developers who want scanning and fixes in the IDE

Strengths: DeepCode AI engine, fast scans without a build, AI-generated fixes

Things to consider: Custom rules are limited to Enterprise, and per-seat costs add up

Snyk Code scans source code in the IDE and in pull requests without a build step and returns results in seconds to minutes. It runs on the DeepCode AI engine and pairs detection with Snyk Agent Fix, which the vendor reports generates fixes that are 80 percent accurate or better. It’s part of the wider Snyk platform, which also covers open source dependencies, containers, and infrastructure as code.

*Snyk now positions itself as an AI security platform, led by Evo, its agentic security product line.

Key features include:

• DeepCode AI engine: Its machine learning engine draws on data flow cases modeled from millions of open source libraries.
• Fast scanning without a build: It scans in the IDE, in pull requests, and in CI/CD without waiting for a build.
• AI-generated fixes: Snyk Agent Fix combines AI with Snyk’s security research to generate fixes, which the vendor reports are 80 percent accurate or better.
• Prioritization: It uses application context to push new, deployed, or publicly exposed issues to the top.
• Vulnerability knowledge base: Snyk’s security team maintains a database of more than 35,000 real-world vulnerabilities and fixes.
• Broad coverage: It supports popular languages, IDEs, and CI/CD tools, including common LLM libraries.
• Platform integration: It connects to the rest of the Snyk platform for open source, container, and infrastructure as code security.

9. Checkmarx

Best for: Enterprises that need broad language coverage

Strengths: Hybrid engine, 60% fewer false positives, custom queries

Things to consider: Takes tuning, costs more, and large scans are slow

Checkmarx SAST uses a hybrid engine, with rules for core languages and AI for newer languages and AI-generated code. It scans uncompiled code straight from your repositories, sorts findings to cut noise, and offers AI fixes where developers work. CxSAST, the on-premises version, runs the traditional rules-based engine. The AI engine and Findings Analysis are only available in the cloud-native Checkmarx One platform, where Checkmarx is also introducing Fusion.

Key features include:

• Hybrid engine: Rules cover core languages and AI covers additional languages and AI-generated code in the same scan.
• Findings Analysis: It marks each result as a likely true or false positive before your team sees it, which the vendor says cuts false positives by 60 percent, and it ranks findings rather than deleting them.
• No build required: It scans uncompiled code directly from GitHub, GitLab, Azure, and Bitbucket.
• Full and incremental scans: Full scans go deep, and incremental scans keep pull requests fast.
• AI fixes in the IDE: Developers can apply AI-generated fixes in the IDE, CLI, or AI coding environment.
• Custom queries: Security teams can write their own queries.
• Platform breadth: Checkmarx One also includes SCA, DAST, API security, IaC security, and container security.

10. Veracode

Best for: Enterprises that run SAST by policy

Strengths: Crosscheck path analysis, 100+ languages and frameworks, source or binary scanning

Things to consider: Binary scans need packaging, scans can be slow, and licensing is complex

Veracode has offered SAST for more than 20 years. It can scan raw source code without compiling it, and it can scan binaries, or a mix of source and binary, when you don’t have the source. It maps the paths untrusted data takes to reach critical functions, aligns findings to CWE, and works in the IDE, the CI/CD pipeline, and the command line.

Key features include:

• Source scanning without a build: It scans raw source code directly, so developers get feedback without compiling.
• Source, binary, and hybrid scanning: It can scan third-party or proprietary code when the source isn’t available.
• Crosscheck path analysis: Veracode says its patented Crosscheck process finds and reports execution paths an attacker could use to reach vulnerable code.
• Language support: It covers more than 100 languages and frameworks, including legacy, mobile, and cloud-native stacks.
• CWE alignment: Every finding maps to the Common Weakness Enumeration.
• IDE and pipeline integration: It gives feedback in the IDE and runs policy scans in CI/CD and from the command line.
• Policy and reporting: It includes reporting, policy management, and portfolio-level risk views.

11. OpenText Fortify

Best for: Enterprises that need flexible deployment

Strengths: 44+ languages, 350+ frameworks, on-premises or SaaS

Things to consider: Pricing is opaque, setup is complex, and scans are slow

OpenText Fortify SAST covers a long list of languages and frameworks and can be deployed almost any way you need. It scans code as it’s written so issues are caught before merge, and it works with common IDEs and CI/CD tools. You can run it on-premises, in a private hosted environment, or as SaaS through OpenText Core Application Security (formerly Fortify on Demand), and it adds AI to help teams audit and fix findings.

Key features include:

• Language and framework coverage: It supports more than 44 languages, 350 frameworks, and one million APIs, and it checks for more than 1,524 vulnerability categories.
• Secrets detection: It finds more than 200 types of secrets in source code.
• AI auditing and fixes: Fortify Remediation Aviator speeds up auditing and suggests code fixes for SAST findings.
• Deployment options: You can use SaaS through OpenText Core Application Security, a private hosted model, or run it fully off-cloud.
• Infrastructure-as-code scanning: It scans Docker, Kubernetes, and serverless configurations on the same engine.
• IDE and CI/CD integration: It works with GitHub, GitLab, Jenkins, Azure DevOps, VS Code, Eclipse, and more.
• Compliance reporting: Policy-based scans and dashboards support OWASP Top 10, NIST, PCI-DSS, and ISO 27001.

12. Black Duck Coverity

Best for: Safety-critical and embedded software teams

Strengths: Deep C and C++ analysis, functional-safety certifications

Things to consider: Enterprise pricing, a long setup, and slow scans

Black Duck Coverity builds a structural model of your application across files and libraries and analyzes it without running the code. Its path-sensitive data flow analysis is especially strong in C and C++, which is why safety-critical and embedded teams use it. It supports 22 languages, more than 200 frameworks, and infrastructure as code, and it holds certifications for the functional-safety standards regulated industries require.

Key features include:

• Structural model analysis: It combines dependency, compiler, and language knowledge to find defects that span multiple components.
• C and C++ depth: It tracks pointer arithmetic, memory aliasing, and thread synchronization across the codebase.
• Functional-safety certification: TÜV SÜD certifies it under IEC 61508-3, and it’s qualified up to ASIL D under ISO 26262 and Level A under DO-178C, with a Qualification Kit for configuring it correctly.
• Standards coverage: It supports MISRA, AUTOSAR C++, CERT, DISA STIG, OWASP Top 10, and CWE Top 25, with downloadable compliance and trend reports.
• Code Sight IDE plugin: Developers get results, issue summaries, hardcoded secret detection, and fixes in the IDE.
• Workflow automation: IDE, SCM, and CI integrations trigger scans on commits and pull requests.
• Deployment options: It runs on-premises or in the cloud through Black Duck Polaris.

13. SonarQube

Best for: Teams that want code quality and security in one tool

Strengths: Taint analysis into libraries, quality gates, 40+ languages and frameworks

Things to consider: Needs tuning and gets heavy on large codebases

SonarQube checks code quality and security in one tool. Its SAST traces data from sources to sinks to find issues like SQL injection, cross-site scripting, buffer overflows, and authentication problems, and Advanced SAST, part of the paid SonarQube Advanced Security add-on, follows that analysis into third-party open source libraries for Java, C#, JavaScript, and TypeScript. It comes as SonarQube Server, SonarQube Cloud, and a free IDE extension, and it supports more than 40 languages and frameworks.

Key features include:

• Taint analysis: It traces data across methods and files to find injection and similar issues while keeping false positives down.
• Advanced SAST across dependencies: On Enterprise plans with Advanced Security, it follows taint analysis into open source libraries to catch vulnerabilities that come from how your code uses them.
• Security hotspots: Hotspots show up in the IDE and pull requests so developers can review security-sensitive code.
• Quality gates and pull request feedback: It gives feedback in the IDE and CI/CD, decorates pull requests, and enforces quality gates aligned to OWASP Top 10 and CWE Top 25.
• Secrets detection: It finds secrets like cloud credentials alongside code vulnerabilities.
• Compliance reporting: SonarQube Server Enterprise and the SonarQube Cloud Enterprise plan include security reports for PCI DSS, OWASP Top 10, OWASP ASVS, and CWE Top 25, and Server also covers STIG and CASA.
• Integrations: It works with GitHub, Bitbucket, Azure DevOps, and GitLab.

Platform-integrated SAST

Platform-integrated SAST comes built into the source code platform your team already uses. There’s very little to set up, findings show up where code review happens, and how deep the analysis goes depends on which tier you pay for.

14. GitHub Code Security (CodeQL)

Best for: Teams that build in GitHub

Strengths: CodeQL, Copilot Autofix, dependency review

Things to consider: Central reporting is limited, and CodeQL doesn’t cover every language

GitHub Code Security is one of two GitHub Advanced Security products, alongside GitHub Secret Protection. It bundles code scanning, AI Scan, the CodeQL CLI, Copilot Autofix, security campaigns, dependency review, custom auto-triage rules for Dependabot, and security overview. CodeQL treats code as data you can query and traces data flows across your application to find vulnerabilities. Copilot Autofix suggests fixes for CodeQL alerts, and scanning runs inside GitHub Actions.

Key features include:

• CodeQL analysis: CodeQL traces data flows through your application to find security issues.
• Copilot Autofix: It reads the context around a CodeQL finding and suggests a fix, and it covers 90 percent of alert types.
• Scanning in GitHub Actions: Code scanning runs in GitHub Actions and checks code as it’s built.
• Dependency review: It flags vulnerable dependencies in pull requests before they merge and shows which packages were added, removed, or updated.
• Dependabot with EPSS: Dependabot alerts include EPSS scores to help you prioritize.
• Security campaigns: Campaigns group related vulnerabilities across repositories, assign owners, and track progress on security debt.
• Free risk assessment: A free tool checks your repositories for code vulnerabilities and leaked secrets.

15. GitLab SAST

Best for: Teams that build in GitLab

Strengths: Built into CI/CD, cross-file Advanced SAST, AI-generated fixes

Things to consider: Merge request findings and AI features need the Ultimate tier

GitLab SAST runs inside GitLab’s CI/CD pipeline. It uses analyzer containers, including a Semgrep-based analyzer and language-specific ones, and for most languages it scans source without a build. On the Free and Premium tiers, findings go into a downloadable SAST report. The Ultimate tier adds findings in merge requests, vulnerability management, GitLab Advanced SAST for cross-file analysis, and AI-generated fixes, and Duo false positive detection also needs a GitLab Duo add-on.

Key features include:

• Built into CI/CD: SAST runs in the GitLab pipeline on every tier, and on Ultimate new findings appear in the merge request.
• Open-source analyzers: Most languages use a Semgrep-based analyzer that scans source without a build, while language-specific analyzers such as SpotBugs compile the code first.
• GitLab Advanced SAST: An opt-in analyzer adds cross-file and cross-function scanning.
• Duo false positive detection: On Ultimate with a GitLab Duo add-on, Duo flags likely false positives in critical and high findings and gives each a confidence score.
• AI-generated fixes: Agentic SAST Vulnerability Resolution opens merge requests with fixes for high and critical findings.
• Custom rulesets: On Ultimate, you can turn rules off or adjust them in a project config file.
• Merge request view: On Ultimate, findings appear inline in the merge request Changes view.

Conclusion

SAST tools catch vulnerabilities while they’re still cheap to fix, in the IDE, the pull request, and the pipeline. The category has moved from rules engines that flag every risky pattern to AI that reads your code, finds what rules miss, and tells you which findings an attacker could actually exploit.

When you compare tools, look past how many findings each one produces. Ask how many of those findings are real, whether the tool covers the languages you write in, whether it understands how your code runs in production, and whether its fixes are ones your developers will actually merge. The right tool is the one your team trusts enough to act on.