Software Composition Analysis: How It Works + 6 Best Practices

What Is Software Composition Analysis (SCA)?

Software Composition Analysis (SCA) is an automated process that identifies open-source and third-party components in a codebase. It evaluates these dependencies for security vulnerabilities (CVEs) and licensing compliance, and generates a Software Bill of Materials (SBOM).

How SCA works:

• Dependency discovery: Identifies all direct and transitive open-source dependencies used by the application.
• Package manifest and lockfile analysis: Parses manifest files and lockfiles to determine the exact packages and versions included in the build.
• Binary and container image scanning: Scans compiled binaries and container images to detect embedded open-source components.
• Vulnerability database matching: Compares identified components against CVE databases and security advisories to find known vulnerabilities.
• License detection: Identifies open-source licenses and flags components that violate organizational licensing policies.
• Transitive dependency mapping: Maps indirect dependencies to uncover hidden security and compliance risks.
• Risk prioritization and remediation guidance: Prioritizes findings based on risk and recommends package upgrades, replacements, or other remediation actions.

SCA vs. SAST:

People often confuse SCA with SAST (Static Application Security Testing), but they secure different aspects of an application:

• SAST analyzes the proprietary code that your developers write to find logic flaws and injection vulnerabilities.
• SCA evaluates the external open-source code your application depends on.

In this article:

• Why Software Composition Analysis Matters
• What SCA Tools Detect
• How Software Composition Analysis Works
• Software Composition Analysis vs. Other AppSec Testing Methods
• AI in Software Composition Analysis
• Software Composition Analysis Best Practices

Why Software Composition Analysis Matters 

Vulnerable Dependencies Create Hidden Risk

Modern software relies heavily on open-source packages and third-party libraries. While these components accelerate development, they can introduce security risks if not managed. Vulnerabilities in dependencies can be exploited by attackers, providing an entry point into otherwise secure applications. Many high-profile breaches have resulted from weaknesses in underlying libraries rather than flaws in the application’s own code.

Vulnerabilities in dependencies are not always obvious to development teams. Issues may arise in packages several layers deep in the dependency tree, making manual tracking difficult. SCA tools automate the identification of vulnerable components, alerting teams to issues that would otherwise remain undetected. This visibility supports proactive risk management and timely remediation.

Supply Chain Attacks Are Increasing

The software supply chain has become a primary attack vector. Attackers target the build and distribution process, injecting malicious code or altering dependencies to compromise many downstream users at once. Incidents like the SolarWinds breach and attacks on popular open-source libraries have shown the scale and impact of supply chain threats.

SCA helps defend against these attacks by monitoring dependencies for suspicious or malicious activity. By verifying the integrity and provenance of third-party packages, SCA tools can detect tampering and prevent compromised components from being introduced into production environments. This layer of protection is important as supply chain attacks increase in frequency and sophistication.

Security Teams Need Visibility Across the SDLC

Securing applications is not just a concern at deployment; it is a continuous process throughout the software development lifecycle (SDLC). Without clear visibility into open-source usage at every stage, security teams struggle to enforce policies, identify risks early, and ensure compliance. SCA provides a centralized view of components, their versions, and associated risks, enabling teams to act quickly when vulnerabilities are discovered.

Integrating SCA into the SDLC allows organizations to catch issues during development, testing, and deployment, reducing the window of exposure. This approach reduces costly post-release fixes and helps maintain a strong security posture. SCA visibility also supports regulatory compliance and audit requirements that mandate tracking and managing open-source software usage.

What SCA Tools Detect 

Known Vulnerabilities in Open-Source Packages

SCA tools scan applications to identify open-source packages in use, then compare them against databases of known vulnerabilities such as the National Vulnerability Database (NVD). This process enables teams to detect whether dependencies contain reported and cataloged flaws. By surfacing these vulnerabilities, SCA tools enable organizations to prioritize patching or updating affected components before attackers exploit them.

Why SCA is needed: 
Relying on open-source packages without visibility into their security posture leaves organizations exposed to known risks. SCA provides up-to-date vulnerability information, helping teams maintain a secure software supply chain by supporting prompt remediation and reducing the risk of incidents from outdated or insecure libraries.

Outdated or Unsupported Components

SCA tools flag components that are outdated or no longer supported by maintainers. Using unsupported libraries increases the risk that new vulnerabilities will go unpatched because there is no active maintenance. Applications can become vulnerable to emerging threats over time, even if they were secure at initial release.

Why SCA is needed: 
By alerting teams to outdated dependencies, SCA encourages proactive maintenance. Keeping components current reduces security risk and improves stability and compatibility. Highlighting unsupported packages helps organizations avoid technical debt that can undermine security and operations.

Risky Open-Source Licenses

Open-source packages come with various licenses, some of which impose restrictions that create legal or business risks. SCA tools analyze the licenses of detected components, flagging those that may conflict with organizational policies or create obligations such as code disclosure or royalty payments. Ignoring license requirements can lead to compliance violations, legal disputes, or forced reengineering.

Why SCA is needed: 
SCA solutions provide license information and can enforce license policies automatically. By identifying and managing risky licenses early, organizations avoid compliance issues and help ensure their software remains legally and commercially viable.

Malicious Packages

Attackers attempt to distribute malicious packages through public repositories, disguising them as legitimate open-source libraries. SCA tools can detect suspicious or compromised packages by checking for indicators such as unusual publishing behavior, typosquatting, or known security advisories. Early detection prevents the accidental inclusion of malicious code in applications.

Why SCA is needed: 
Malicious packages can give attackers access to sensitive systems and data. SCA tools can identify and block these packages before they enter the build process, which helps defend against supply chain attacks and targeted exploits.

How Software Composition Analysis Works 

1. Dependency Discovery

The first step in SCA is identifying dependencies within a software project. This involves scanning source code, build files, and configuration files to extract a list of direct and transitive dependencies. SCA tools automate this process so components are not overlooked, regardless of how deeply nested they are in the dependency tree.

Accurate dependency discovery supports risk management. Without a complete inventory, organizations may miss vulnerabilities, license issues, or unsupported components. Automated discovery allows security teams to focus on mitigation rather than manual tracking.

2. Package Manifest and Lockfile Analysis

Most modern software projects use package managers that rely on manifest files such as package.json, pom.xml, or requirements.txt, and lockfiles such as package-lock.json or Pipfile.lock to specify dependencies. SCA tools analyze these files to determine which packages and versions are used in each build. This analysis captures intended dependencies and the versions resolved during installation.

Lockfile analysis matters because it reveals the full resolved dependency graph, including indirect dependencies. What actually ships is a separate question, since packages can be stripped during a multi-stage build or introduced by the base image. By understanding the full dependency graph, SCA tools can assess risk and reduce the chance of surprises when vulnerabilities are discovered in deeply nested packages.

3. Binary and Container Image Scanning

Applications are often distributed as compiled binaries or container images, which may include dependencies not listed in source code or manifests. SCA tools can scan these artifacts directly to identify embedded open-source components, even when source information is unavailable. This is critical for software distributed as Docker containers, compiled executables, or third-party binaries.

Scanning binaries and container images extends SCA coverage into deployment and production. It helps organizations detect risks introduced during packaging, such as outdated libraries bundled with the application.

4. Vulnerability Database Matching

Once dependencies are identified, SCA tools cross-reference them against vulnerability databases, including the NVD, vendor advisories, and community-maintained lists. This process surfaces known security issues affecting specific package versions. Automated, real-time database matching helps organizations stay aware of emerging threats by updating vulnerability intelligence continuously.

Effective vulnerability matching requires accurate package identification and version resolution. SCA tools use algorithms to reduce false positives and improve confidence in findings, which supports remediation prioritization.

5. License Detection

SCA tools analyze the licenses associated with each open-source package by parsing metadata, documentation, and source files to identify legal obligations. This helps organizations enforce licensing policies, avoid incompatible licenses, and maintain compliance with internal and external requirements.

Automated license detection reduces the risk of errors compared to manual review. SCA tools can flag problematic licenses early in development, allowing teams to select alternative packages or seek legal guidance before issues escalate.

6. Transitive Dependency Mapping

Many vulnerabilities and license issues reside in transitive, indirect dependencies required by other libraries. SCA tools build a map of the dependency graph, tracing relationships to uncover issues that may be several layers deep. This visibility is important because transitive dependencies are often overlooked.

Transitive dependency mapping enables organizations to understand their open-source exposure and address vulnerabilities and compliance issues across the full dependency graph. By visualizing dependency chains, SCA tools help developers understand which packages introduce vulnerable components and identify the most effective remediation path. This can include upgrading a parent package, replacing a dependency, or applying a patch when a direct update is unavailable.

7. Risk Prioritization and Remediation Guidance

Not every vulnerability requires immediate action. SCA tools help teams prioritize findings based on severity, exploit availability, affected package, business impact, and whether the vulnerable code is reachable. Reachability is a starting point rather than a verdict, since a reachable function still cannot be exploited unless the vulnerability’s other prerequisites are met. This context allows teams to focus on issues that present the greatest risk instead of treating every alert as equally urgent.

SCA tools also provide remediation guidance. They recommend secure package versions, identify upgrade paths, highlight breaking changes, and sometimes suggest temporary mitigations when an immediate upgrade is not possible. Many tools integrate with issue trackers, CI/CD pipelines, and source control platforms to support remediation workflows as part of normal development processes.

Software Composition Analysis vs. Other AppSec Testing Methods 

SCA vs. SAST

Software Composition Analysis (SCA) and Static Application Security Testing (SAST) address different parts of application security. SCA focuses on third-party and open-source dependencies, identifying known vulnerabilities, license issues, and outdated components. SAST analyzes an organization’s source code, bytecode, or binaries to find security flaws such as SQL injection, cross-site scripting (XSS), insecure authentication, or hardcoded secrets before deployment.

The two approaches complement each other: SAST helps developers fix vulnerabilities in their own code, while SCA identifies risks from external libraries. Using both provides broader coverage across proprietary code and open-source components.

SCA vs. DAST

Dynamic Application Security Testing (DAST) evaluates a running application by interacting with it from the outside, similar to an attacker. Instead of analyzing source code or dependencies, DAST identifies vulnerabilities exposed during runtime, including authentication weaknesses, insecure configurations, and exploitable behavior.

SCA operates earlier in the software development lifecycle by analyzing dependencies before deployment. While DAST reveals runtime security issues, it cannot determine whether an application contains vulnerable open-source libraries that are not directly exposed during testing. Combining SCA with DAST provides visibility into supply chain risks and runtime vulnerabilities.

SCA vs. SBOM

A Software Bill of Materials (SBOM) is a structured inventory of the software components, libraries, and dependencies included in an application. It provides transparency into what software is present but does not determine whether those components are vulnerable, outdated, or subject to problematic licenses.

SCA tools generate and consume SBOMs as part of their workflow, but they go beyond inventory creation. They analyze components against vulnerability databases, evaluate license compliance, monitor for newly disclosed issues, and provide remediation guidance. An SBOM supplies the component inventory; SCA provides ongoing security and compliance analysis to manage supply chain risk.

Related content: Read our guide to cloud application security to see how these testing methods fit into a broader cloud AppSec program.

AI in Software Composition Analysis 

AI-Driven Exploitability Investigation

Reachability tells you whether a vulnerable function can be called. It does not tell you whether the vulnerability can be exploited. Most CVEs carry prerequisites, a feature that must be enabled, a configuration that must be set, an input an attacker must control, a package that must survive the build. Checking those conditions one CVE at a time is what a security engineer does by hand, and it is why triage has never scaled.

AI changes what is possible here. Agents can read the CVE, work out what each prerequisite requires, gather the relevant signals from code, build, and runtime, and reach a verdict with the evidence attached. Findings where a required condition for exploitability is absent get closed rather than ranked. The output is a set of conclusions instead of a shorter list of maybes.

Example:
A container image includes a vulnerable XML parsing library rated critical. Reachability analysis confirms the application calls the library. Investigation goes further and finds the vulnerable parsing mode is never enabled in the application’s configuration and therefore not exploitable, so the finding is closed with the configuration evidence recorded and the team never sees it.

AI-Assisted Vulnerability Prioritization

Traditional SCA tools often generate many findings, some of which pose little practical risk. AI reduces this noise by analyzing context such as exploit availability, package usage, code reachability, deployment environment, and historical attack patterns. Instead of relying only on CVSS scores, AI can estimate the likelihood of exploitation and potential impact. This context-aware prioritization helps security teams focus on higher-risk vulnerabilities and reduce alert fatigue.

Example:
A financial services company discovers 350 vulnerable open-source packages during an SCA scan. Instead of prioritizing solely by CVSS scores, the platform uses AI to identify the 12 vulnerabilities that are internet-facing, reachable by the application’s code, and confirmed exploitable in that environment. The security team remediates these issues first, significantly reducing risk while postponing lower-priority updates for the next maintenance cycle.

Smarter Remediation Recommendations

Selecting a safe upgrade path is often more difficult than identifying a vulnerable dependency. AI can analyze dependency graphs, release notes, compatibility information, and historical upgrade patterns to recommend package versions that resolve vulnerabilities while reducing the risk of breaking functionality. It can also identify cases where updating a parent dependency remediates multiple transitive vulnerabilities. Some AI-enabled SCA platforms generate pull requests, suggest configuration changes, or explain tradeoffs between remediation options.

Example:
An eCommerce application depends on a vulnerable logging library through multiple frameworks. Instead of recommending dozens of individual package updates, the AI identifies that upgrading a single parent framework safely resolves all affected transitive dependencies and generates a pull request with the required version changes.

AI-Generated SBOM Insights

As SBOMs grow larger and more complex, AI can help organizations extract insights from them. Instead of presenting only a list of components, AI can identify high-risk packages, summarize security posture, detect unusual dependency patterns, and highlight components that require attention. AI can also correlate SBOM data with newly disclosed vulnerabilities, threat intelligence, and organizational policies to identify emerging risks automatically. Teams receive prioritized summaries and recommendations when new threats affect software in use.

Example:
After a critical vulnerability is disclosed in a widely used compression library, an AI-enabled SCA platform analyzes the organization’s SBOMs and identifies every application containing the affected component. It ranks impacted systems by internet exposure and business criticality, then provides security teams with a prioritized remediation plan.

Software Composition Analysis Best Practices 

Organizations can improve their SCA approach by incorporating these best practices.

1. Maintain a Complete Open-Source Inventory

An accurate inventory of open-source components is the foundation of an SCA program. Organizations should track every dependency, including direct and transitive packages, across applications and environments. This inventory should include package names, versions, licenses, and usage locations. Keeping the inventory current allows teams to determine whether newly disclosed vulnerabilities affect their software and supports compliance, incident response, and maintenance.

Key actions:

• Inventory all direct and transitive dependencies.
• Generate and maintain an up-to-date SBOM.
• Track package versions, licenses, and usage locations.
• Continuously update inventories as dependencies change.
• Review inventories regularly for unsupported components.

2. Prioritize Based on Real Risk, Not Just CVSS Scores

CVSS scores measure vulnerability severity, but they should not be the only factor driving remediation decisions. Organizations should consider exploit availability, code reachability, application exposure, and business impact, and then whether the vulnerability is exploitable under those specific conditions. This context helps distinguish critical issues from those with limited practical risk. Risk-based prioritization enables teams to remediate meaningful vulnerabilities faster and use resources more efficiently.

Key actions:

• Consider exploit availability and active exploitation.
• Evaluate code reachability, then confirm exploitability in your environment.
• Include business impact and asset criticality in prioritization.
• Focus remediation on the highest-risk components first.
• Review priorities as new threat intelligence becomes available.

3. Track Direct and Transitive Dependencies

Many security issues originate in transitive dependencies that developers do not add directly. Because these packages are installed automatically by package managers, they can remain unnoticed until a vulnerability is disclosed. SCA tools should map the entire dependency graph so every component is visible and monitored. Organizations should review dependency trees regularly and remove unnecessary packages. Reducing dependency complexity decreases the attack surface and simplifies maintenance.

Key actions:

• Map complete dependency trees.
• Monitor indirect dependencies for new vulnerabilities.
• Remove unnecessary packages to reduce attack surface.
• Review dependency changes before deployment.
• Update parent packages when they resolve transitive risks.

4. Enforce Clear Dependency Approval Policies

Organizations should establish policies defining which packages, repositories, licenses, and maintainers are approved for use. New dependencies should be evaluated for security, maintenance activity, licensing, and community reputation before introduction into production applications. SCA tools can automate policy enforcement by blocking builds or generating alerts when unapproved dependencies are detected, helping apply security requirements consistently.

Key actions:

• Define approved repositories and package sources.
• Review new dependencies before adoption.
• Enforce license and security policies automatically.
• Block unapproved or high-risk packages in CI/CD pipelines.
• Periodically review approved dependency lists.

5. Include Containers and Build Artifacts in SCA Coverage

Scanning source code alone is insufficient because deployed applications often contain additional components introduced during build or packaging. Organizations should extend SCA to container images, compiled binaries, virtual machine images, and other deployment artifacts to identify embedded libraries not listed in package manifests. This broader coverage helps detect outdated operating system packages, bundled runtimes, and third-party components that might otherwise remain hidden.

Key actions:

• Scan container images before deployment.
• Analyze compiled binaries and deployment artifacts.
• Detect vulnerable operating system packages and runtimes.
• Integrate artifact scanning into CI/CD pipelines.
• Continuously rescan production images as new vulnerabilities are disclosed.

6. Combine SCA with SAST, DAST, and API Security Testing

No single application security testing method identifies every type of risk. SCA detects vulnerabilities in third-party components, SAST finds flaws in proprietary source code, DAST identifies vulnerabilities in running applications, and API security testing evaluates APIs for issues such as weak authentication, excessive data exposure, and authorization failures. Using these approaches together provides coverage across the software development lifecycle and supports earlier detection and remediation of application and supply chain risks.

Key actions:

• Use SAST to identify vulnerabilities in proprietary code.
• Use DAST to identify runtime vulnerabilities.
• Include API security testing for exposed endpoints.
• Correlate findings across multiple AppSec tools.
• Integrate all testing methods into the secure SDLC.

Investigating Dependency Risk with Maze Code

Maze Code uses AI agents to find, investigate, and fix vulnerabilities across both your third-party dependencies (AI-SCA) and the code your team writes (AI-SAST) in one place. Rather than handing teams another list of findings to research, Maze agents trace each vulnerable dependency through your code and cloud to prove what is actually exploitable in your environment, then route a verified fix to the developer who owns it — so security teams spend their time on the handful of findings that represent real risk instead of a backlog of noise.

Key capabilities of Maze Code:

• Every dependency investigated with deep context: Maze finds every vulnerable dependency, then prioritizes the ones that create real risk for your organization, using AI-built call graphs that pick up where traditional methods stop and determine exploitability with context from your code and cloud.
• Exploitability, not just reachability: Maze proves what is exploitable in your environment rather than only reachable in your code; findings that are reachable but cannot be exploited in practice are closed before they ever reach your team.
• Full call-chain analysis: Instead of stopping at the first hop to check whether your code calls a vulnerable function directly, Maze agents trace the full call chain however many hops deep the vulnerability sits, then check runtime and cloud to confirm whether that path is actually exploitable.
• Built-in scanner or bring your own: Maze Code is both the scanner and the AI investigation and fix layer — it can ingest and deduplicate findings from the scanners you already run, or act as the scanner itself.
• Automated, developer-ready remediation: When a dependency vulnerability is exploitable, agents trace its root cause, validate a fix, and ship it as a pull request to the owning developer; when no fix exists yet, they recommend a mitigation instead.
• Unified code and cloud context: Running on the same platform as Maze Cloud, code and cloud findings enrich each other, so a vulnerable library is judged by how it actually runs in production and you get one unified ticket per issue instead of duplicate alerts.
• Fits into existing developer workflows: Maze Code runs in your CI/CD pipeline (GitHub Actions, GitLab CI, CircleCI) and surfaces findings right at the pull request, with fixes that flow into the coding agents your developers already use.

Learn how Maze Code investigates and remediates dependency risk across your entire codebase. Explore Maze Code.