Security Testing
Software Composition Analysis (SCA) scans your project's dependencies—npm packages, Maven artifacts, pip libraries, Go modules—against vulnerability databases (NVD, OSV, GitHub Advisory) to identify known CVEs. Modern applications use hundreds of open source libraries, and 70–90% of the code in production is third-party. SCA is
Security Testing
A leaked API key costs on average $1.2 million to remediate when you include breach response, customer notification, regulatory fines, and reputational damage. Secrets end up in git repositories constantly — not because developers are careless, but because the workflow makes it easy: you add a credential temporarily to test
Docker
Container security scanners (Trivy, Grype, Docker Scout) analyze Docker images for known CVEs in OS packages and application dependencies. This guide covers running scans locally, integrating into GitHub Actions, enforcing severity thresholds, and reducing attack surface by choosing minimal base images. Every Docker image is a bundle of OS packages
Supply Chain Security
Software Composition Analysis (SCA) is now table stakes for secure development. But the tooling landscape is fragmented: there are commercial platforms, open source scanners, and everything in between. Choosing the wrong tool means either missing real vulnerabilities or drowning in false positives. This guide compares the four most widely-deployed
Security Testing
Software Composition Analysis (SCA) identifies vulnerable open-source dependencies in your project. Snyk, Dependabot, and OWASP Dependency-Check are the three most widely used tools — each with different vulnerability databases, false positive rates, auto-fix capabilities, and pricing models. This guide compares them head-to-head. Why SCA Matters Modern
Supply Chain Security
Generating an SBOM (Software Bill of Materials) is the easy part. The hard part is doing something useful with it — automatically enforcing policies that prevent vulnerable, unlicensed, or untrusted components from making it into your production builds. This guide covers the full SBOM lifecycle: generation, validation, policy enforcement, and continuous
Security Testing
Static Application Security Testing (SAST) catches vulnerabilities before they ever run. By analyzing source code at rest, SAST tools identify injection flaws, insecure configurations, hardcoded secrets, and dozens of other issues during development — not after a breach. Two tools dominate modern DevSecOps pipelines: SonarQube and Semgrep. They take fundamentally different
Security Testing
Static Application Security Testing (SAST) for Python means scanning your source code for vulnerabilities without running it. Bandit is the go-to Python-specific tool, Semgrep gives you customizable rules, and CodeQL provides deep dataflow analysis. This guide shows you how to configure all three in CI/CD and which
Security Testing
Java and Spring Boot applications carry a distinct set of security vulnerabilities: SQL injection through JPQL, insecure deserialization via Java serialization, XML External Entity (XXE) attacks, and Spring Security misconfigurations. Static Application Security Testing (SAST) catches these before runtime. This guide covers SpotBugs (Find Security Bugs plugin), SonarQube, and CodeQL
Security Testing
SAST tools commonly produce 30–70% false positive rates out of the box. Teams that don't manage this noise end up ignoring all SAST output. The solution is a combination of: baseline suppression (ignore existing issues, only alert on new ones), rule tuning (disable rules not relevant to
Supply Chain Security
Automated dependency updates are your first line of defense against supply chain vulnerabilities.
Security Testing
The average Node.js application has over 1,000 transitive dependencies. The average Python project has hundreds.