Software Composition Analysis for AI-Generated Code: Challenges, Lessons, and Best Practices

AI coding assistants change the economics of software creation. They can produce working features, tests, configuration, and glue code in minutes. They can also introduce dependencies at the same speed—sometimes implicitly, sometimes unnecessarily, and occasionally by recommending packages that do not exist.

That makes software composition analysis (SCA) more important, but it also changes how SCA must be used. A scanner that runs once before release is no longer enough. In an AI-assisted development environment, composition analysis needs to operate as a continuous control: identifying every component, checking its provenance and policy status, and feeding useful decisions back into the same pull request where the dependency appeared.

The central problem: generation is fast, verification is not

Traditional dependency risk already includes known vulnerabilities, abandoned projects, risky licenses, compromised maintainers, transitive dependencies, and packages that have reached end of life. AI-generated code adds three kinds of pressure.

1. Dependency creation becomes less deliberate

A developer who manually adds a library often visits its repository, reads its documentation, compares alternatives, and considers whether it is maintained. An AI assistant can silently add an import, modify a manifest, or propose an installation command as a side effect of solving another problem. The dependency decision becomes easy to miss during review.

2. Models can recommend nonexistent or incorrect packages

Package hallucination is not theoretical. Research presented at USENIX Security 2025 evaluated hundreds of thousands of generated code samples and found that code-generating models can produce plausible-looking package names that do not exist. An attacker can register a recurring hallucinated name and wait for a developer—or an autonomous agent—to install it. This pattern is often called “slopsquatting.”

SCA can help, but only if it verifies package identity and provenance before installation. A conventional “known CVEs” check may say nothing about a brand-new malicious package with no vulnerability record.

3. More findings can create less security

AI-assisted development can increase the volume of dependency changes and therefore the number of alerts. If every advisory receives the same treatment, teams quickly learn to ignore the queue. SCA without application context produces noise: a vulnerable function may not be reachable, a development-only package may never ship, or a severe transitive issue may sit on an exposed production path.

The challenge is not merely detection. It is prioritization and ownership.

What SCA does well—and where it needs help

SCA is strongest at producing an inventory and correlating components with known information: vulnerabilities, licenses, package metadata, and policy rules. It can reveal direct and transitive dependencies, generate or consume a software bill of materials (SBOM), and continuously monitor deployed components when new advisories appear.

But SCA is not a complete verdict on whether AI-generated code is safe. It usually cannot determine whether business logic is correct, whether an API is used securely, whether credentials were embedded, or whether a package is malicious despite having no published CVE. Those concerns require complementary controls such as code review, static analysis, secret scanning, tests, artifact verification, and runtime monitoring.

The right operating model is layered. NIST’s Secure Software Development Framework treats secure development as a set of outcomes spanning preparation, software protection, secure production, and vulnerability response. OWASP’s Software Component Verification Standard similarly emphasizes incremental supply-chain controls rather than relying on a single tool.

Lessons learned from applying SCA to AI-assisted development

Lesson 1: Treat generated code as untrusted input

“Generated” should describe authorship, not assurance. AI-created changes should pass the same—or stronger—controls as human-created changes. The pipeline should inspect the resulting code and artifacts, not trust the model’s explanation of what it changed.

Lesson 2: Scan the change, the build, and the deployed artifact

A manifest scan at pull-request time gives fast feedback, but it can miss conditional, vendored, dynamically loaded, or build-introduced components. Generate an SBOM from the actual build artifact as well, then compare it with the source-level inventory. Differences are valuable signals.

Lesson 3: New dependencies deserve more scrutiny than familiar ones

A newly introduced package changes the trust boundary. Require evidence that it exists in the expected registry, has the intended owner and repository, is actively maintained, uses an acceptable license, and has a history consistent with the project’s risk tolerance. For sensitive systems, verify signatures, hashes, provenance attestations, or an approved internal mirror.

Lesson 4: Reachability and runtime context matter

CVSS alone is not a remediation strategy. Combine advisory severity with exploitability, reachability, data sensitivity, environment, internet exposure, and whether the affected component is present in production. This reduces alert fatigue without hiding genuine risk.

Lesson 5: The lockfile is a security artifact

AI agents should not be allowed to rewrite dependency manifests or lockfiles invisibly. Review both. Pin versions where appropriate, retain integrity hashes, and fail builds when the lockfile and manifest disagree. Deterministic builds make investigation and rollback far easier.

A practical best-practice playbook

Make dependency changes explicit. Configure coding agents to summarize every added, removed, or upgraded package. Use repository rules to label dependency-changing pull requests automatically.
Run SCA before merge. Scan manifests and lockfiles in each pull request. Block newly introduced critical risk, prohibited licenses, unapproved registries, and packages that fail minimum provenance or maintenance criteria.
Verify package existence before installation. Resolve names against an allowlisted registry or internal proxy. Do not let an agent install an unfamiliar package merely because the name looks plausible.
Prefer approved components. Give the AI assistant a current catalog of supported libraries, versions, and internal patterns. Grounding generation in approved choices prevents risk more effectively than cleaning it up later.
Generate an SBOM from the build. Store it with the release, sign it where possible, and retain the dependency relationships needed to trace transitive exposure. CISA guidance distinguishes source and build SBOMs because they answer different questions.
Continuously monitor what is deployed. A clean build can become vulnerable tomorrow. Re-evaluate stored SBOMs when new advisories, end-of-life notices, or policy changes appear.
Prioritize using context. Enrich findings with reachability, deployment status, exploit maturity, and asset criticality. Define time-bound service levels and a documented exception process.
Use complementary security checks. Pair SCA with static analysis, secret scanning, tests, code review, artifact signing, and runtime controls. SCA governs components; it does not validate all generated logic.
Keep a human accountable. Every AI-generated pull request needs an owner who understands the change and can defend each new dependency. Approval must represent a real decision, not a ceremonial click.
Measure outcomes, not scan volume. Track time to remediate exploitable findings, percentage of releases with verified SBOMs, rate of unapproved dependency introductions, exception age, and dependency freshness. A growing alert count is not evidence of improving security.

A useful policy boundary

Organizations do not need to prohibit AI-generated dependencies. They need a clear boundary between suggestion and adoption. The assistant may propose a component; the engineering system must verify it; an accountable person must accept the residual risk.

That division of labor preserves the speed advantage of AI while preventing plausible-looking code from bypassing software supply-chain discipline. The strongest teams will not use SCA as a late-stage scanner. They will use it as the feedback system that makes fast generation compatible with traceable, maintainable, and defensible software.