An AppSec Guide to Software Composition Analysis (SCA)

What do SolarWinds, Log4j, and XZ Utils have in common? They all started as trusted software components until vulnerabilities in their code exposed thousands of applications to attacks. Open-source dependencies power modern development, but without visibility into what’s running under the hood, you could be shipping risks with every release.

Most applications have at least 6 to 10 direct dependencies, but once you factor in the layers of transitive dependencies, that number balloons to around 180. That’s 180 potential points of vulnerability, which can range from SQL injection risks to outdated authentication libraries or a misconfigured logging library. 

The types of vulnerabilities are wide-ranging, but they all boil down to gaps an attacker could exploit if you don’t catch them first. Software Composition Analysis (SCA) provides a way to catalog all your dependencies, identify the vulnerable dependencies, and mitigate them effectively. 

What is Software Composition Analysis (SCA)?

Software Composition Analysis (SCA) is the security practice of scanning your codebase for open-source and third-party vulnerabilities. It looks at all the libraries and dependencies you’re using, checks them against lists of known vulnerabilities, and checks for license compliance. SCA matters now more than ever because open-source is everywhere—between 70 and 90% of your app’s code isn’t even yours. 

The task of inventorying a codebase has evolved alongside DevOps. What once involved a manual audit is now mostly automated and integrated with your CI/CD pipeline. This approach is much faster and helps developers keep pace with the speed of modern development. Tools like npm-audit for JavaScript, OSV-Scanner for Python and Java, and Nancy for Go projects are now the ones doing the heavy lifting. 

How Software Composition Analysis Works

SCA scans your code and lists every piece you use, including libraries, frameworks, and modules, down to their specific versions. From there, it cross-references this inventory against vulnerability databases like Common Vulnerabilities and Exposures (CVE) or National Vulnerability Database (NVD) and license registries.

It outputs a detailed report listing vulnerable dependencies, the severity of the vulnerability (often scored via CVSS), and sometimes remediation steps. In general, specific SCA tools are designed to handle specific programming languages. 



Key Benefits of Implementing Software Composition Analysis

• Early vulnerability detection: SCA turns early vulnerability scanning into a routine. It scans as you code and commits, flagging outdated dependencies with known CVEs. This saves you from dealing with exploits in a live environment.
• Cost and risk reduction: Proactive remediation reduces unresolved code issues (known as technical debt) and expensive breach cleanup costs. Fixing a vulnerability pre-deployment is far cheaper than a post-incident response.
• Easier compliance and licensing management: SCA does more than check for dependency vulnerabilities. It also checks for licensing or compliance issues, such as a library with a restrictive open-source license that could limit distribution.
• Streamlined developer workflow: It can deliver scan results and remediation suggestions where developers already work, such as version-control systems and IDEs. Less context-switching means faster, safer code.

Best Practices for Software Composition Analysis Implementation

1. Integrate SCA Early in the SDLC

The earlier SCA is integrated into your development lifecycle, the earlier you can catch security risks and compliance issues before they snowball into bigger problems. For example, catching the flawed Apache Commons Text library (CVE-2022-42889) during coding lets you replace it before it’s part of your API endpoints. This is way less painful than removing it post-deployment. 

Hook SCA into your IDE or version control system so you see alerts when you pull a package, and tie it to your CI pipeline to block merges if a critical CVE pops up. At this stage, you should also implement zero trust so that every new dependency is treated as untrusted until it’s thoroughly verified.



2. Leverage Transitive Dependency Analysis

Direct dependencies are just the tip of the iceberg. If you want to find vulnerabilities hidden in the deeper layers of your software, you need to look at transitive dependencies (the dependencies of your dependencies). What seems like a safe library might pull in an outdated library with a known issue, which you’d miss without transitive dependency analysis. 

Integrate tools like OSV-scanner that map your full dependency tree, not just your requirements.txt. Set up automated alerts to handle high-severity CVEs (CVSS 7.0+) when detected. Either pin safer versions or exclude risky sub-dependencies entirely using lock files or exclusion rules. This requires an upfront setup but pays dividends by protecting your application against obscure transitive bugs. 



3. Implement Contextual Prioritization

A blanket CVSS 10 rating might look scary, but it’s a blunt number that ignores context. In the real world, whether a vulnerability can be exploited matters more than some arbitrary severity score. You should focus your remediation efforts on vulnerabilities that are genuinely at risk based on how your application functions so you aren’t wasting time on low-impact fixes. 

Jit’s Context Engine offers a way to effectively prioritize vulnerabilities based on real factors like location and exploitability. This contextual prioritization also helps tackle the common challenge of false positives - especially from lesser-known libraries - by filtering out noise and focusing only on vulnerabilities that genuinely pose a risk to your application.

4. Continuous Monitoring and Database Updates

You will miss the latest threats if your SCA tool runs on stale data. Not only do you need to scan as new code is added at least once a day, but you also need to sync with the latest CVE and NVD feeds to ensure that your vulnerability databases are 100% updated. You can do this by connecting to these databases with an API pull, which most decent SCA tools will do out of the box. 

5. Automate Remediation Workflows

The last thing you want to do is implement workflows that kill development momentum. If you automate the triaging of vulnerable dependencies, devs don’t have to Google fixes or debate which version removes the risk. 

Jit continuously scans your codebase and adds any dependency vulnerabilities to a backlog with Priority Scores. All you need to do is open a fix pull request, and Jit automatically takes you to GitHub, where a suggested code fix is waiting. From there, upgrading to a safe version is as easy as clicking “Commit suggestion.” 



What To Look For in SCA Security Tools

Just as you would use LLM benchmarks to evaluate AI, you can apply similar performance metrics (such as detection accuracy, speed, and coverage) to assess how effectively your SCA tools identify vulnerabilities and adapt to evolving threats. Here are a few metrics to consider: 

1. Integrations

Your SCA tool should seamlessly integrate into your workflow, acting as a supportive extension of your team rather than a roadblock. It should align with the tools your developers already use (whether that's GitLab, VS Code, or others) so they can receive real-time results without interrupting their focus. 

2. Contextual Insights

Next, make sure your tool offers real-time detection with contextual insights. A tool that flags every CVE can waste a lot of precious time. Instead, it should prioritize vulnerabilities based on their exploitability and the specific impact they could have on your business so you can address the most critical issues first.

3. Remediation

Ideally, your tool will include automated remediation guidance like patch suggestions or version upgrades. That way, you know what’s important to fix and can take steps to minimize the risk it poses immediately.

4. Flexibility

Look for tools that allow you to customize security policies to align with your specific technology stack and workflows. Flexibility means you can adapt scans based on the types of code and dependencies you use, whether you're working with particular programming languages, frameworks, or cloud environments.

Mitigate Open-Source Risks with Jit’s SCA Integrations

Today’s software leans on open source, but dozens or hundreds of direct and transitive dependencies per app can be a hotbed of risk. SCA catches vulnerabilities early and provides a way to prioritize fixes while slashing costs and chaos. 

Jit’s Product Security platform integrates with top SCA tools like npm-audit, OSV-Scanner, and Nancy, along with many other security tools to cover your entire SDLC. With real-time scans, a Context Engine for exploitability insights, and one-click GitHub PRs for remediation, this platform enables you to not only improve but simplify your security operations. Explore more here.