Software Composition Analysis (SCA): Securing Your Dependencies

Software Composition Analysis (SCA) is the practice of identifying and analyzing the open source and third-party components in a software application. SCA tools scan dependency manifests (package.json, requirements.txt, pom.xml, go.mod), resolve the full dependency tree including transitive dependencies, and check each component against vulnerability databases for known CVEs.

Modern applications are composed primarily of open source code. Industry studies estimate that 70-90% of any application's codebase consists of third-party dependencies. When a vulnerability is discovered in a popular library, every application that includes that library is affected—as demonstrated by Log4Shell (CVE-2021-44228), which impacted millions of Java applications worldwide.

SCA goes beyond vulnerability detection. A complete SCA tool also provides license compliance analysis (identifying GPL, MIT, Apache, and other licenses in your dependency tree), SBOM generation (producing a Software Bill of Materials in SPDX or CycloneDX format), and malicious package detection (identifying typosquatting, dependency confusion, and known malware packages).

SCA is essential because your application's security posture is only as strong as its weakest dependency. You can write perfectly secure application code, but if you depend on a library with a critical RCE vulnerability, your application is exploitable.

SCA and SAST are complementary analysis techniques that examine different attack surfaces:

• SAST analyzes your first-party code—the code your team writes. It detects injection patterns, cryptographic misuse, access control flaws, and coding errors in your application logic.
• SCA analyzes your third-party code—the libraries and frameworks your application depends on. It detects known CVEs, license violations, and malicious packages in your dependency tree.

Different Inputs, Different Outputs

// SAST analyzes this (your code):
const query = "SELECT * FROM users WHERE id = " + userId;
// Finding: CWE-89 SQL Injection in your code

// SCA analyzes this (your dependencies):
// package.json: "lodash": "4.17.20"
// Finding: CVE-2021-23337 in lodash < 4.17.21

A vulnerability in your code (SAST) requires you to fix your logic. A vulnerability in a dependency (SCA) requires you to upgrade or replace the package. The remediation workflows are fundamentally different.

Both are necessary because attackers exploit both: custom code vulnerabilities like injection and dependency vulnerabilities like Log4Shell. An application security program that only runs SAST misses dependency risks; one that only runs SCA misses custom code vulnerabilities. Complete coverage requires both.

Open source dependencies introduce risk through three primary channels:

Known Vulnerabilities (CVEs)

Libraries have bugs, including security bugs. When a CVE is published, every application using the affected version is exposed. The challenge is visibility: most teams do not know which versions of which libraries are in their full dependency tree, including transitive dependencies.

Supply Chain Attacks

Attackers increasingly target the software supply chain directly:

• Typosquatting: Publishing malicious packages with names similar to popular ones (lodas instead of lodash)
• Dependency confusion: Exploiting private/public registry resolution to inject malicious packages
• Maintainer compromise: Gaining control of a legitimate package's publishing credentials (event-stream incident, 2018)
• Protestware: Maintainers intentionally adding destructive code (node-ipc, 2022)

Transitive Dependency Risk

Your application might depend on 50 packages directly, but those 50 packages pull in hundreds of transitive dependencies that you never explicitly chose. A vulnerability 4 levels deep in the dependency tree is just as exploitable as one in a direct dependency. The npm ecosystem averages 79 transitive dependencies per package.

CodeSlick integrates SCA capabilities alongside SAST, giving teams dependency analysis in the same tool that scans their application code:

• Dependency scanning: Analyzes package.json/package-lock.json (npm), requirements.txt (pip), pom.xml (Maven), and go.mod (Go modules) for known vulnerabilities
• Malicious package detection: Identifies 66 known malicious packages using OSV.dev integration, catching typosquatting and supply chain attacks
• SBOM generation: Produces Software Bill of Materials in both SPDX and CycloneDX formats for compliance and inventory management
• CVSS scoring: All dependency findings include CVSS 3.1 severity scores for consistent prioritization alongside SAST findings

By combining SAST and SCA in a single scan, CodeSlick gives developers a complete picture of their application's security posture—both the code they write and the code they import—in a single report with consistent CWE mapping and severity scoring.