28 Best SCA Tools (2026)

What is SCA?

Software Composition Analysis (SCA) is a category of application security tools that automatically identify all open-source libraries and third-party dependencies in your software, then check them against vulnerability databases for known security issues and license compliance violations. SCA tools work by reading manifest files (package.json, pom.xml, requirements.txt) or scanning compiled binaries, then cross-referencing each component and version against databases like the NVD, OSV, and vendor-specific advisory feeds.

Unlike SAST, which analyzes your own source code, SCA focuses exclusively on third-party components. SCA tools do not need to see your application logic — some work with just manifest files alone. They detect every open-source library in your dependency tree and flag versions with known vulnerabilities or problematic licenses.

The concept dates back further than most developers realize. Black Duck (founded in 2002, now independent after spinning out of Synopsys) was one of the earliest commercial SCA platforms, focused on license compliance for enterprises shipping proprietary software with open-source components. On the open-source side, OWASP Dependency-Check emerged as one of the first free tools, matching Java dependencies against the National Vulnerability Database using CPE identification. The category exploded after high-profile breaches through open-source components — Equifax’s 2017 breach through a known Apache Struts vulnerability being the most cited example.

The scale of open-source usage makes manual tracking impossible. According to Synopsys’ 2024 OSSRA report, 96% of commercial codebases contain open-source components, and 84% of those contain at least one known vulnerability. Dependency counts vary by ecosystem: Sonatype’s 2024 State of the Software Supply Chain report found that the average Java application pulls in 148 dependencies, while JavaScript projects routinely pull in hundreds of transitive ones. No team can track all of that by hand.

Understanding the difference between direct and transitive dependencies matters here. A direct dependency is a package you explicitly import — say, lodash in a Node.js project. But lodash itself depends on other packages, and those packages depend on more. If minimist (a transitive dependency several levels deep) has a prototype pollution vulnerability, your application inherits that risk even though you never wrote a single line of code using minimist. Most of the vulnerabilities SCA tools flag come from these transitive dependencies, which is why tools that only check your top-level manifest file miss a significant portion of the risk.

Regulatory pressure is also pushing adoption. US Executive Order 14028 (2021) requires SBOMs for software sold to federal agencies. The EU Cyber Resilience Act mandates vulnerability handling and SBOM disclosure for products sold in the EU. PCI DSS 4.0, requirement 6.3.2, requires organizations to maintain an inventory of custom and third-party software components and monitor them for vulnerabilities. These are not suggestions — they carry compliance consequences.

Some SCA tools offer auto-remediation, automatically opening pull requests to bump a vulnerable dependency to a patched version. That alone can save hours of manual work. One thing that catches people off guard: “open source” does not always mean “free to use in commercial applications.” Many open-source licenses have restrictions that can create legal problems. SCA tools check for license compliance so you do not end up in that situation.

Supply chain attacks are also on the rise. The Sonatype 2024 State of the Software Supply Chain report found a 156% increase in malicious packages over the previous year, with over 704,102 malicious packages identified since 2019 across npm, PyPI, and other ecosystems.

The trade-off is noise. SCA tools may report hundreds of issues, but not all vulnerabilities are actually reachable from your code. You might use an open-source library for a single function, and the library might have dependencies that never execute in your application at runtime. Newer tools with reachability analysis (Endor Labs, Contrast SCA, Qwiet AI) address this problem by showing you which vulnerabilities actually matter — typically cutting alert volume by 70-90% according to these vendors.

How SCA Works

SCA tools follow a five-stage pipeline: dependency discovery, vulnerability matching, reachability analysis, license compliance checking, and SBOM generation. The tool identifies every open-source component in your application — including transitive dependencies — then cross-references each one against vulnerability databases like the NVD and OSV and reports what it finds. Advanced tools go further with reachability analysis to determine which vulnerabilities are actually callable from your code, and license analysis to flag compliance risks.

How dependency discovery works varies significantly across ecosystems, and this affects the accuracy of SCA results. npm uses a flattened node_modules structure where transitive dependencies are hoisted to the top level when possible, which means your project can end up with multiple versions of the same package. Maven resolves conflicts with a “nearest wins” strategy — the version closest to your project in the dependency tree takes precedence, which can silently downgrade a dependency to a vulnerable version. Go modules use minimum version selection, always picking the lowest version that satisfies all constraints, which tends to be more predictable but can mean you stay on older versions longer.

Lock files are critical to accurate SCA scanning. A manifest file (package.json, requirements.txt) declares what you want; a lock file (package-lock.json, poetry.lock, go.sum) records what you actually got. Without a lock file, the SCA tool cannot know exactly which versions of transitive dependencies are installed. Some tools fall back to resolving the dependency tree themselves, but the result may not match what is actually running in production. Always commit your lock files and scan them rather than just the manifest.

There is also the problem of phantom dependencies — packages that work in your project but are not explicitly declared in your manifest. This happens when a package you depend on brings in a transitive dependency, and your code imports that transitive dependency directly. If your declared dependency drops that transitive dependency in a future version, your build breaks. Some SCA tools flag phantom dependencies, but many do not.

For compiled artifacts where manifest files are unavailable, tools like Black Duck and OWASP Dependency-Check perform binary and snippet scanning. They fingerprint compiled code (JAR files, DLLs, binaries) and match those fingerprints against known open-source components. This is essential for vendor risk assessments where you receive software without source code, and for verifying that what shipped to production actually matches what was scanned in CI.

Quick Comparison

All 28 active SCA tools side by side, grouped by license type.

BluBracket (acquired by HashiCorp in 2023) is listed separately at the bottom.

The SCA market has been consolidating rapidly through 2024-2025. Snyk and Black Duck hold the largest market share among commercial tools — Snyk with over 2.5 million developers on its platform and Black Duck with eight consecutive years as a Gartner Magic Quadrant Leader for AST. On the disruption side, Endor Labs has gained traction with organizations like OpenAI, Snowflake, and Atlassian by leading with function-level reachability analysis across 40+ languages. Socket carved out a niche by focusing on supply chain attack detection through behavioral analysis rather than CVE matching.

As of 2025, the category is moving beyond basic vulnerability scanning toward what some analysts call “SCA+” — a combination of CVE detection, reachability analysis, supply chain threat detection, and SBOM management in a single tool. Reachability analysis in particular is becoming table stakes rather than a differentiator. Snyk, Endor Labs, Contrast SCA, Qwiet AI, and FOSSA all offer some form of it now. The question is no longer whether a tool has reachability analysis but how deep it goes — package-level, class-level, or function-level.

Consolidation is reshaping the competitive landscape. Debricked was acquired by OpenText and integrated into the Fortify portfolio as OpenText Core SCA. Veracode acquired Phylum’s malicious package detection technology in January 2025 and sunset the standalone product. On the free side, Dependabot (free for all GitHub repos, 30+ ecosystems) and Renovate (open-source, 90+ package managers, works on GitHub/GitLab/Bitbucket/Azure DevOps) dominate the automated dependency update space, each taking a different approach to configuration flexibility and platform support.

SCA vs SAST

SCA scans third-party dependencies for known vulnerabilities and license issues, while SAST analyzes your own source code for security flaws like SQL injection and cross-site scripting. Both run before deployment, but they target completely different risk surfaces — SCA covers your open-source supply chain, SAST covers the code your team writes.

In practice, you want both. Consider a real-world scenario: your application code is clean — no SQL injection, no XSS, no insecure deserialization. SAST gives you a passing grade. But you import a database library that has a known SQL injection vulnerability in its query builder. SAST will not catch it because the vulnerable code is not in your repository. SCA will, because it checks the library version against vulnerability databases. The reverse is also true — SCA will not find the SQL injection bug you wrote yourself. Each tool covers a blind spot the other cannot see.

The lines between SCA and SAST are blurring as vendors expand their platforms. Checkmarx One bundles SAST and SCA (via Checkmarx SCA) in a single platform. Snyk offers both Snyk Open Source (SCA) and Snyk Code (SAST). Mend added SAST capabilities alongside its SCA engine. Endor Labs combines AI-native SAST with SCA and reachability analysis in one tool. For teams that want a unified view, these integrated platforms reduce context switching and let you correlate findings across your own code and your dependencies.

There is also a timing dimension. Traditional SCA runs at build time — you commit code, the CI pipeline scans your dependencies, and you get a report. But Contrast SCA takes a different approach by observing which libraries are actually loaded and called at runtime, giving you class-level execution data from production. This “shift right” approach complements the “shift left” scanning that most SCA tools do at the PR or build stage. Running both gives you coverage across the full lifecycle.

For a detailed breakdown, see our SAST vs SCA comparison.

Supply Chain Security in 2026

A software supply chain attack occurs when an attacker compromises a legitimate open-source package — or publishes a malicious one designed to look legitimate — to inject malicious code into downstream applications. Unlike known vulnerabilities with CVE identifiers, supply chain attacks are deliberate and have no advisory to match against because the malicious code was never a “bug.” Traditional SCA tools check your dependencies against databases of known vulnerabilities (CVEs), which works for issues that have already been reported. But there is no CVE for a supply chain attack because the code is intentionally malicious. CVE-only SCA tools miss these attacks entirely.

Four high-profile incidents illustrate how supply chain attacks work in practice.

The event-stream attack (2018): The event-stream npm package, with 2 million weekly downloads, was handed off to a new maintainer who injected code targeting a specific Bitcoin wallet application. The malicious code was hidden inside a newly added dependency, making it difficult to spot through normal code review.

The ua-parser-js hijack (2021): An attacker compromised the ua-parser-js npm package (nearly 8 million weekly downloads) and published versions containing cryptominers and password stealers, affecting applications that pulled the update automatically.

The colors.js/faker.js sabotage (2022): The maintainer of colors.js and faker.js intentionally corrupted his own widely-used packages as a protest, breaking thousands of projects that depended on them.

The xz-utils backdoor (2024): Perhaps the most sophisticated supply chain attack to date — a contributor spent two years building trust in the xz compression library project before inserting a backdoor into the library used by SSH on most Linux distributions. It was caught by accident when a developer noticed unexpected performance anomalies.

Two common attack vectors deserve special attention. Typosquatting is when an attacker publishes a package with a name nearly identical to a popular one — “lodahs” instead of “lodash,” or “python-dateutils” instead of “python-dateutil.” Developers make typos, and package managers install whatever you ask for without question. Dependency confusion exploits the way package managers resolve names: an attacker publishes a public package with the same name as your internal private package but with a higher version number. If your build system checks public registries before private ones, it pulls the attacker’s package instead of yours.

Socket takes a different approach to these threats. Instead of looking up CVEs, it analyzes what packages actually do: network calls, filesystem access, obfuscated code, install scripts. If a package suddenly starts making HTTP requests to an unknown server, Socket flags it, even if there is no CVE. Checkmarx SCA has a similar concept with its behavioral analysis feature, which evaluates package provider credibility and update cadence alongside vulnerability data.

The open-source ecosystem is building infrastructure to fight back. Sigstore (backed by the Linux Foundation) provides free code signing through cosign, letting maintainers cryptographically sign their releases so consumers can verify authenticity. The SLSA framework (Supply-chain Levels for Software Artifacts, v1.0) defines increasing levels of supply chain security, from basic build provenance at Build Level 1 to hardened builds at Build Level 3. npm, PyPI, and other registries are increasingly requiring two-factor authentication and supporting package provenance attestations.

AI code assistants add a new wrinkle to supply chain risk. Tools that suggest packages based on training data may recommend popular-but-vulnerable libraries, outdated packages, or even packages that no longer exist (opening the door for attackers to register those phantom package names). The suggestions are based on what appeared frequently in training data, not on current security posture.

The numbers make the case. Sonatype reported a 156% year-over-year increase in supply chain attacks against open-source repositories in their 2024 State of the Software Supply Chain report, with over 704,102 malicious packages catalogued since 2019. That is not a trend that is slowing down. Running a CVE-only SCA tool and calling it a day leaves a growing gap in your defenses.

SCA in Your CI/CD Pipeline

SCA tools integrate into five key points in the development workflow: IDE plugins for pre-commit scanning, pull request checks for new dependencies, policy gates that block merges on critical findings, auto-remediation bots that open fix PRs, and SBOM generation during release builds. Most teams start with PR-level scanning and add stages over time. Here is how each integration point works:

Pre-commit and IDE scanning. Run SCA checks in the developer’s IDE so they see vulnerable dependencies before committing. Snyk and JFrog Xray have strong IDE plugins for VS Code, IntelliJ, and other editors. This is the earliest possible feedback loop — developers catch issues before code even reaches a branch. The tradeoff is that IDE plugins only scan the local workspace and may not reflect what the CI pipeline resolves.

Pull request checks. Run SCA on every PR. If a new dependency or version bump introduces a known vulnerability, the PR gets flagged. Most tools post findings directly as PR comments with severity and remediation guidance. Socket is particularly effective here — it analyzes the behavior of any newly added packages and blocks the PR if it detects suspicious activity like unexpected network access or install scripts.

Policy gates. Block merges when critical vulnerabilities or license violations are detected. Define policies per severity level: block on critical/high, warn on medium, ignore low. The specifics depend on your risk tolerance. Some teams gate on CVSS score alone; others use EPSS (Exploit Prediction Scoring System) to factor in how likely a vulnerability is to be exploited in the wild.

Auto-remediation. Let the tool open PRs to bump vulnerable dependencies automatically. Snyk and Mend do this well. Dependabot and Renovate handle the automated dependency update side — Dependabot is free and built into GitHub with support for 30+ ecosystems, while Renovate supports 90+ package managers and works across GitHub, GitLab, Bitbucket, and Azure DevOps. Review auto-fix PRs before merging since version bumps can introduce breaking changes. Renovate’s merge confidence feature aggregates CI data from other repositories to predict how likely an update is to break your build, which helps teams decide which auto-updates are safe to merge without manual testing.

SBOM generation. Generate an SBOM as part of your release pipeline. This is useful for compliance and for responding quickly when a new vulnerability is disclosed — you can immediately check if you are affected. Store SBOMs alongside your release artifacts so you have a record of exactly what shipped in each version. Syft and Grype make this easy as a free, open-source pair: Syft generates the SBOM, and Grype scans it for vulnerabilities.

The concept of a “vulnerability window” matters for pipeline design. This is the time between when a CVE is disclosed and when your team patches it. Tools like Snyk and Dependabot offer continuous monitoring that re-checks your existing dependencies against newly published advisories, alerting you even when no new code was pushed. Without continuous monitoring, your vulnerability window depends entirely on how often you run your CI pipeline.

If your organization uses private registries like JFrog Artifactory or Sonatype Nexus for hosting internal packages, make sure your SCA tool can reach them. JFrog Xray has native integration with Artifactory for scanning artifacts as they are stored. Other tools need network access to the registry or scan the resolved dependencies from your lock file.

How to Choose an SCA Tool

Choosing the right SCA tool depends on your primary need: vulnerability scanning, license compliance, supply chain protection, or automated dependency updates. For free dependency scanning, OWASP Dependency-Check combined with Dependabot provides basic SCA coverage at no cost. For enterprise needs, the choice comes down to which combination of reachability analysis, license compliance depth, and ecosystem coverage matches your stack.

Here is what I would evaluate when picking an SCA tool:

Package manager support. Does it cover the ecosystems you use? npm, Maven, PyPI, NuGet, Go modules, RubyGems? Most commercial tools cover all of these, but double-check for less common ones like Cargo, Hex, or Pub. Renovate leads with 90+ package managers, while Dependabot covers 30+. If you are in a polyglot environment, breadth of ecosystem coverage is a hard filter.

Vulnerability database. How fast does it pick up new CVEs? OWASP Dependency-Check relies on NVD, which can lag behind disclosure. Snyk maintains a proprietary database that reports detecting vulnerabilities an average of 47 days faster than competing sources. Black Duck Security Advisories (BDSAs) also provide earlier intelligence than the NVD alone. Speed matters because the vulnerability window between disclosure and your patch is when attackers strike.

Reachability analysis. If alert fatigue is a concern (it usually is), look for tools that can tell you which vulnerabilities are actually reachable from your code. Endor Labs offers function-level reachability across 40+ languages and reports up to 97% alert noise reduction. Contrast SCA uses runtime instrumentation for class-level execution tracking. Qwiet AI uses AI-powered code property graphs. The depth of analysis varies — package-level reachability is a rough filter, while function-level reachability is far more precise.

License compliance. If you ship software commercially, this matters. FOSSA leads with 99.8% license detection accuracy, full-text analysis that catches modified licenses, and a policy engine designed with open-source licensing attorneys. Black Duck has the deepest feature set for enterprises needing attribution reports and audit-grade compliance. Snyk covers license compliance too, but with less depth than the dedicated tools.

CI/CD integration and auto-remediation. How easy is it to add to your pipeline? Does it open auto-fix PRs? Snyk is the easiest to get started with — install the CLI, run snyk test, and you are scanning. Mend is strong on auto-remediation. For dependency updates specifically, Dependabot is free and native to GitHub, while Renovate offers more configuration flexibility and platform support.

Budget. OWASP Dependency-Check is free and works well for basic scanning. Grype and Syft are free for vulnerability scanning and SBOM generation. Dependabot is free for all GitHub repos. Most commercial tools have free tiers that cover small teams. Enterprise features (reachability, compliance dashboards, priority support) require paid plans.

Here is a simplified decision framework based on primary use case:

• Free and simple — OWASP Dependency-Check for vulnerability scanning + Dependabot or Renovate for automated dependency updates
• Best developer experience — Snyk Open Source for auto-fix PRs, IDE plugins, and a fast vulnerability database
• License compliance is critical — FOSSA for 99.8% accuracy or Black Duck for enterprise audit-grade compliance
• Supply chain attacks worry you most — Socket for behavioral analysis of packages before they enter your codebase
• Reachability analysis to cut alert noise — Endor Labs for function-level reachability across 40+ languages, or Contrast SCA for runtime-based prioritization
• All-in on JFrog — JFrog Xray for native Artifactory integration and binary scanning

Common SCA Mistakes

Even teams that adopt SCA tools make avoidable mistakes that undermine the value of scanning. The most common errors include scanning only direct dependencies while missing transitive ones, treating all CVEs as equally urgent without reachability context, and ignoring license compliance until a legal issue surfaces. Here are the mistakes we see most often and how to avoid them:

Only scanning direct dependencies. If your SCA tool only checks what is listed in your manifest file (package.json, pom.xml) without resolving the full dependency tree, you miss the transitive dependencies where most vulnerabilities live. Make sure your tool resolves lock files and scans the complete graph. A direct dependency with zero CVEs can pull in dozens of transitive packages with known issues.

Treating all CVEs equally without reachability context. A critical-severity CVE in a function your code never calls is less urgent than a medium-severity CVE in a code path that runs on every request. Without reachability analysis, teams either try to fix everything (burnout) or ignore everything below a CVSS threshold (missed real risks). Tools like Endor Labs, Contrast SCA, and Qwiet AI help prioritize based on actual exploitability.

Not updating vulnerability databases regularly. The NVD publishes over 100 new CVEs per day. If your OWASP Dependency-Check instance is running with a stale database because the NVD API key expired or the update job stopped, you are scanning against old data. Commercial tools handle database updates automatically, but self-hosted and open-source tools need maintenance.

Ignoring license compliance. Vulnerability scanning gets all the attention, but license violations carry real legal consequences. If your commercial application includes a GPL-licensed component, you may be obligated to release your source code. The Synopsys 2024 OSSRA report found license conflicts in 53% of audited codebases. Set up license policies early — it is much harder to untangle license obligations after shipping.

Not scanning container base images. Your Dockerfile starts with FROM node:18-alpine or FROM python:3.11-slim. Those base images contain OS-level packages (openssl, zlib, curl) with their own vulnerabilities. Manifest-level SCA does not catch these. Tools like Grype, Trivy, Anchore, and Black Duck scan container images to surface OS-level vulnerabilities that would otherwise slip through.

Relying solely on CVE databases. CVE-based scanning only catches vulnerabilities that have been reported, assigned a CVE number, and added to a database. Malicious packages published through typosquatting or dependency confusion have no CVE — they are deliberate attacks, not bugs. Supply-chain-aware tools like Socket and behavioral analysis features in Checkmarx SCA address this gap by analyzing what packages actually do rather than waiting for someone to file a report.

SBOM Deep Dive

A Software Bill of Materials (SBOM) is a complete, machine-readable inventory of every component in your software — direct dependencies, transitive dependencies, OS packages, and their versions, licenses, and relationships. Think of it as a nutrition label for software. When a new vulnerability like Log4Shell is disclosed, an SBOM lets you answer the question “are we affected?” in minutes instead of hours by searching the inventory rather than rescanning every application from scratch.

Two formats dominate: CycloneDX (maintained by OWASP) and SPDX (maintained by the Linux Foundation, now an ISO standard). CycloneDX was designed with security use cases in mind — it natively supports vulnerability data, dependency graphs, and component hashes, and it is widely adopted by security-focused SCA tools. SPDX has deeper roots in license compliance and legal workflows, which makes it the preferred choice for organizations focused on open-source licensing obligations. Most SCA tools support both formats, so the choice often comes down to which downstream tools and processes your organization uses.

Several tools stand out for SBOM generation quality. Black Duck produces comprehensive SBOMs with deep license metadata, making it the go-to for compliance-heavy environments. Syft (by Anchore, open-source, 8.4k GitHub stars) generates SBOMs from container images, filesystems, and archives with support for dozens of package ecosystems — and it is free. Anchore Enterprise stores SBOMs centrally and continuously matches them against updated vulnerability feeds, so when a new CVE drops, you know which components are affected without rescanning. FOSSA offers an SBOM Portal for centralized distribution with access tokens, version control, and searchable archives.

Regulatory mandates are the primary driver of SBOM adoption as of 2026. US Executive Order 14028 (May 2021) requires SBOMs for all software sold to federal agencies. The EU Cyber Resilience Act mandates SBOM disclosure for any product with digital elements sold in the EU market. The FDA now expects SBOMs for medical device software submissions. PCI DSS 4.0, requirement 6.3.2, requires organizations to maintain an inventory of all third-party software components. These are not optional best practices — they carry audit and enforcement consequences for non-compliance.

For practical SBOM management, generate your SBOM as a CI pipeline artifact alongside each release. Store it in a versioned repository so you can look up exactly what shipped in any given version. When a new vulnerability hits, scan your stored SBOMs with Grype or a similar tool to identify affected releases without rebuilding anything. If customers or auditors request SBOMs, use a distribution tool like FOSSA’s SBOM Portal or host them alongside your release artifacts. Automate the process — an SBOM that is generated manually will eventually be forgotten.

AN

Anchore

NEW

SBOM-First Container Security Platform

Commercial (Open-Source tools available)

Anchore Grype

Fast Container Vulnerability Scanner

Free (Open-Source, Apache 2.0)

Arnica

NEW

Pipelineless SCA with Package Reputation

Freemium

Black Duck

SBOM & License Compliance

Commercial 12 langs

CAST Highlight

Chrome Extension, SBOM Export

Commercial 15 langs

CH

Chainguard

NEW

Zero-CVE Hardened Container Images

Commercial (Free tier available)

Checkmarx SCA

Three-Pronged Analysis

Commercial (with Free Trial) 7 langs

Contrast SCA

Runtime Library Prioritization

Commercial (with Free Trial) 7 langs

Endor Labs

NEW

AI-Native AppSec with 97% Noise Reduction

Commercial

FOSSA

Enterprise License Compliance

Freemium

GitGuardian

Enterprise Secrets Detection

Freemium

GitHub Dependabot

GitHub-Native Dependency Security

Free (GitHub native)

JFrog Xray

Binary Management Integration

Commercial (Pro X, Enterprise X, or Enterprise+ subscription)

Mend SCA

Forrester Strong Performer, Auto-Remediation

Commercial

OpenText Core SCA (Debricked)

Fortify Integration, Developer-Friendly

Freemium 8 langs

OS

OSV-Scanner

NEW

Google-Backed OSV Database Scanner

Free (Open-Source, Apache 2.0)

OWASP Dependency-Check

Long-Standing Open-Source SCA

Free (Open-Source, Apache 2.0)

OWASP Dependency-Track

SBOM-First Vulnerability Management

Free (Open-Source, Apache 2.0)

Renovate

Automated Dependency Updates

Free (Open-Source, AGPL-3.0)

Revenera FlexNet Code Insight

License Compliance & IP Protection Leader

Commercial 10 langs

SCANOSS

Lightweight Open-Source SCA

Freemium 5 langs

Snyk

All-in-One Developer Security

Freemium

Snyk Container

Developer-first container security

Freemium

Snyk Open Source

Developer-First SCA with Automated Fix PRs

Freemium

Socket

NEW

Detects Malware, Not Just CVEs

Commercial (with Free tier for open source)

Sonatype Lifecycle

Gartner Visionary, SDLC Integration

Commercial

SY

Syft

SBOM generation tool

Free (Open-Source, Apache-2.0)

Veracode SCA

Open-Source Library Scanning

Commercial 8 langs

Frequently Asked Questions

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SCA 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 →