Best Code Security Review Tools and Solutions in 2027

Coding agents and copilots now open more pull requests than security teams can read line by line. The tools on this list handle code security review of those changes: they check first-party and AI-generated code at the pull request and in CI, flag security flaws before merge, and help developers fix them while the change is still in front of them.

Endor Labs is our top pick. Its AI Security Review checks every pull request for logic flaws such as IDOR, records the reasoning behind each verdict, and posts a fix in the PR.

Best code security review tools in 2027 at a glance

1. Endor Labs (AI-powered PR security review): AI Security Review runs Endor Labs' AI SAST engine on every PR, traces data across files to catch logic flaws like IDOR, labels each finding with auditable reasoning, and proposes a fix. AURI by Endor Labs adds security context while coding agents write.
2. GitHub Advanced Security (SAST, GitHub-native): CodeQL code scanning in pull requests, with Copilot Autofix suggestions.
3. Semgrep (rule-based scanning): Customizable rules that run as PR checks, free for small teams.
4. Snyk Code (SAST): AI-assisted static analysis in the IDE and at the PR, with fix suggestions.
5. Checkmarx One (full AppSec platform): Enterprise SAST with custom queries and PR decoration.
6. Veracode (full AppSec platform): SAST, DAST, and SCA with AI-suggested fixes in the IDE.
7. SonarQube (SAST): Quality gates and security analysis, with a free Community Build.
8. Aikido Security (full AppSec platform): An agentic PR reviewer on top of bundled scanners.
9. Apiiro (full AppSec platform): Detects material code changes that shift risk.
10. Cycode (full AppSec platform): Native code and pipeline scanners plus third-party integrations.

Why code security review moved to the pull request

Code security review moved to the pull request because that's where every change, human or AI-written, passes before it ships. Teams shipping 1,000+ PRs across hundreds of repositories can't route each one to a security engineer, so the first pass has to be automated.

AI-generated code makes that first pass more important. In Endor Labs' Agent Security League benchmark, over 80% of the functionally correct solutions that AI coding agents produced still contained security vulnerabilities. Code that passes its tests can still ship a flaw.

Auditors are asking for proof, too. AppSec leaders need to show that AI-generated code gets the same review rigor as human-written code. A review recorded on every PR gives them that evidence.

What counts as a code security review solution

A code security review solution is any tool or process that checks a code change for security flaws before it merges. The options fall into four types:

• AI-powered PR security review: Reasoning-based analysis of each change, posted as PR comments. It fits teams with high PR volume and concern about logic flaws.
• Rule-based and SAST scanning: Pattern and dataflow rules that run in the IDE, at the PR, or in CI. It fits teams that want predictable checks for known bug classes and are ready to tune rules.
• Manual or expert assessment: A security engineer reads the change. It fits high-risk work like new authentication flows, payment logic, or cryptography.
• Full AppSec platforms: Suites that bundle code scanning with other modules and a central policy layer. They fit organizations consolidating tools under one dashboard.

Manual review still has a place. The OWASP Code Review Guide treats human review as a complement to automated tools, especially for business logic and design flaws. Automated review decides which PRs need a human, so security engineers spend their time on the changes that warrant it.

If open source dependency risk is your main concern, see our code security platforms compared on dependency risk and reachability. If you're comparing static analysis engines on their own, read our best SAST tools roundup.

How we evaluated these tools

We ranked each tool on the work it does at the pull request:

These criteria come from what AppSec and platform teams report. Legacy SAST buries security teams in low-quality findings while missing IDORs, authentication bypasses, and business logic errors, and developers don't want another tool cluttering PR threads.

One criterion also maps to a framework auditors recognize. NIST's Secure Software Development Framework (SP 800-218) includes practice PW.7: review and/or analyze human-readable code to identify vulnerabilities and verify compliance with security requirements. A tool that records a verdict and its reasoning on every PR produces evidence for that practice, whoever wrote the code.

1. Endor Labs

Type: AI-powered PR security review, backed by a full application security platform.

AI Security Review runs Endor Labs' AI SAST engine on every pull request. AI SAST orchestrates several specialized agents and combines rule-based pattern matching, dataflow tracing, and multi-pass LLM reasoning.

Three kinds of agents do the work:

• Detection agents read a change the way a security engineer would. They trace how data moves across files and functions to catch flaws a pattern can't describe, including broken access control, IDOR, injection, and insecure design.
• A triage agent labels each finding as true positive, false positive, or unknown, with reasoning your team can audit.
• Remediation agents propose a fix matched to your codebase for every confirmed vulnerability.

One PR scan covers the code, the dependencies that PR changed (SCA), and any secrets it introduced. Results post as PR comments and connect to your CI/CD pipeline and policy engine on GitHub, GitLab, and Bitbucket. For the background on the launch, read how AI Security Review picks out security-relevant changes.

A worked IDOR example

Say a developer, or a coding agent, opens a PR that adds an endpoint to fetch an invoice by ID:

// routes/invoices.ts
router.get("/invoices/:id", requireAuth, async (req, res) => {
  const invoice = await invoiceService.getById(req.params.id);
  res.json(invoice);
});

// services/invoiceService.ts
export function getById(id: string) {
  return db.invoice.findUnique({ where: { id } });
}

The route checks that the user is logged in but never checks that the invoice belongs to them. Any authenticated user can change the ID in the URL and read another customer's invoice. A pattern scanner sees an authenticated route and a parameterized query, and both look safe on their own.

AI Security Review handles the same PR in three steps:

1. Detection agents trace req.params.id from the route handler into invoiceService.getById in a second file, then into the database call. No ownership or tenant filter appears anywhere along that path.
2. The triage agent labels the finding a true positive. Its reasoning: the endpoint returns a record selected only by a user-controlled ID, requireAuth confirms identity without confirming ownership, and other invoice routes in the repo filter by customerId.
3. The remediation agent proposes a fix that follows the repo's existing pattern by scoping the query to the requesting customer:

export function getByIdForCustomer(id: string, customerId: string) {
  return db.invoice.findFirst({ where: { id, customerId } });
}

The developer gets one comment with the verdict, the reasoning, and a fix they can accept before merge.

Coverage from generation to merge

AI Security Review is one layer of a broader approach to securing code at generation with AURI by Endor Labs. AURI gives coding agents security context while they write, so fewer insecure patterns reach the PR. It plugs into Cursor, Claude Code, GitHub, and GitLab without rip-and-replace, and coding agent governance works through Hooks, Skills, MCP, CLI, and API.

Secrets Detection scans full Git history, validates leaked credentials as live or revoked, and stops leaks at pre-commit. Full-stack reachability, built on Endor Labs' patented code context graph, ties PR findings to how your application behaves across first-party code, open source dependencies, and container images.

Evidence

• Across 8 real-world projects in Java, Python, JavaScript, TypeScript, C#, and Go, with ground truth set by Endor Labs' security research team, AI SAST found 192 real vulnerabilities. That's 2.6x more than Claude Code and 3.5x more than OpenAI Codex, and it surfaced 63 vulnerabilities no other tool in the test caught.
• Fewer than 10% of PRs need human security review after rollout.
• AI SAST reduces false positives by up to 95%.
• Teams see 83% fewer blocked PRs and 6x faster remediation.

Endor Labs fits AppSec, platform engineering, and DevSecOps teams with high PR volume from coding agents, a need to catch logic flaws, and auditors who want a record of every review.

2. GitHub Advanced Security

Type: SAST, GitHub-native.

GitHub Advanced Security is now sold as two products, GitHub Code Security and GitHub Secret Protection. Code Security runs CodeQL code scanning and annotates pull requests with its alerts. Copilot Autofix suggests fixes for those alerts, and dependency review flags vulnerable packages a PR adds. Secret Protection adds secret scanning and push protection. Code scanning is free on public repositories, and both products are available on GitHub Team and Enterprise plans. It fits teams whose workflow lives entirely in GitHub.

3. Semgrep

Type: Rule-based scanning.

Semgrep runs static analysis rules against code changes and posts findings as PR and merge request comments. Its rule syntax looks like the code it matches, so security teams can write custom rules for their own frameworks and internal APIs. The platform adds cross-file analysis with Pro rules, Semgrep Supply Chain for dependencies, Semgrep Secrets, and Semgrep Assistant for AI-powered triage. The open source engine is free, and Semgrep Code and Supply Chain are free for teams of up to 10 contributors. It fits teams with security engineers who want fine-grained control over the rules that run on every PR.

4. Snyk Code

Type: SAST.

Snyk Code is the static analysis module of the Snyk platform. Its DeepCode AI engine combines symbolic AI and machine learning to flag vulnerabilities in first-party code, and DeepCode AI Fix suggests fixes developers can apply. Plugins for VS Code and JetBrains IDEs show findings while developers write, and PR checks surface them before merge. Snyk Code connects to Snyk's modules for open source dependencies, containers, and infrastructure as code. A free plan includes a monthly allowance of Snyk Code tests. It fits developer-led teams that want security feedback in the editor and at the PR.

5. Checkmarx One

Type: Full AppSec platform.

Checkmarx One is an enterprise application security platform built around SAST, with SCA, IaC, API, DAST, container, and supply chain scanning in the same platform. Security teams can write custom queries to tune detection for their own code. When a pull request opens, Checkmarx One scans it and decorates the PR with newly introduced vulnerabilities, fixed vulnerabilities, and policy violations. Developers can reply to the decoration to ask about a finding or trigger a rescan. Checkmarx One Assist adds agentic AI for remediation. It fits large enterprises with dedicated AppSec teams and heavy audit requirements.

6. Veracode

Type: Full AppSec platform.

Veracode combines SAST, DAST, and SCA in one platform, with policy tracking across an application portfolio. Veracode Scan plugins for VS Code, JetBrains IDEs, Visual Studio, and Eclipse run static and SCA scans from the editor. Veracode Fix generates AI-suggested patches for static findings that developers can review and apply. Pipeline scans run in CI. Veracode fits regulated enterprises that need policy enforcement and audit reporting across many applications.

7. SonarQube

Type: SAST.

SonarQube combines code quality and security analysis and enforces quality gates a change must pass before merge. It ships as SonarQube Server, SonarQube Cloud, and SonarQube for IDE, formerly SonarLint. The free Community Build is one of the most common starting points. Developer Edition adds branch analysis, pull request decoration, and taint analysis, which tracks untrusted input across methods and files. SonarQube fits teams that want security checks inside an existing code quality workflow.

8. Aikido Security

Type: Full AppSec platform.

Aikido Security bundles SAST, SCA, secrets detection, and other scanners in one platform. Deep Review, its agentic PR reviewer, reasons across the codebase, posts suggested fixes directly on pull requests, and remembers accepted risks and false positives. Aikido offers a free tier for small teams. It fits startups and mid-size engineering teams that want several scanners and PR review under one account.

9. Apiiro

Type: Full AppSec platform.

Apiiro uses Deep Code Analysis to model how code behaves and flags material code changes, such as a change to authorization logic or newly exposed sensitive data. Policies can then trigger the right response, for example assigning a security reviewer, starting a threat model, or requesting a pen test, only when a material change appears. Apiiro connects to GitHub, GitLab, Bitbucket, and Azure DevOps. It fits enterprises that want security architects focused on the changes that alter their risk profile.

10. Cycode

Type: Full AppSec platform.

Cycode combines native scanners for SAST, SCA, IaC, secrets, and CI/CD pipeline security. ConnectorX, its integration marketplace, pulls in findings from more than 100 third-party security and DevOps tools so teams can correlate results in one place. A change impact analysis agent reviews code changes for shifts in risk posture. Cycode fits organizations that want to consolidate code and pipeline security findings from several tools.

How to trial a code security review tool on your own pull requests

The fastest way to compare tools is to run them on PRs you already know the answers to. This plan fits in a week:

1. Build a test set. Pick 20 to 50 merged PRs from the last six months, including several that introduced bugs you later fixed. Include at least two logic flaws, such as a missing ownership check or an authentication bypass, plus a hardcoded secret and a vulnerable dependency bump.
2. Replay the PRs. Recreate them on a branch or fork and run each shortlisted tool against the same set.
3. Score detection. Count true positives, false positives, and missed bugs for each tool. Track logic flaws separately, since that's where tools differ most.
4. Check the triage. For each finding, ask whether a reviewer could confirm or reject it from the tool's reasoning alone.
5. Measure noise. Record the number of PR comments per PR and how many a developer would act on.
6. Measure routing. Note the share of PRs each tool would send to human security review.
7. Time the fix. For confirmed findings, measure time from finding to merged fix, and note whether the tool proposed the fix.

If your teams use coding agents, run the same plan on AI-generated PRs. That gives you the comparison auditors will ask for.

Frequently asked questions

What is the best code security review tool?

Endor Labs is our top pick for code security review in 2027. AI Security Review traces data across files to catch logic flaws like IDOR, labels each finding with auditable reasoning, and proposes a fix in the PR. One scan covers the code, the dependencies it changed, and any new secrets.

Are there free code security review tools?

Yes, several tools have free options. SonarQube offers a free Community Build, Semgrep Code and Supply Chain are free for up to 10 contributors, Snyk and Aikido Security have free plans with limits, and GitHub code scanning is free on public repositories.

Which code security review tools work with GitHub?

Every tool on this list integrates with GitHub. GitHub Advanced Security runs natively inside it, and Endor Labs posts AI Security Review comments on GitHub, GitLab, and Bitbucket pull requests.

How is code security review different from SAST?

Code security review covers every way a change gets checked for security flaws before merge, and SAST is one technique within it. Pattern-based SAST matches known code shapes, while reasoning-based PR review follows data across files and functions to find logic flaws, then adds triage and fixes.

Can AI replace manual security code review?

AI can't replace manual security review, but it can decide which changes need a human. With Endor Labs' AI Security Review, fewer than 10% of PRs need human security review after rollout, so security engineers can focus on high-risk changes like new authentication flows.