Why do traditional SCA tools struggle in environments that use AI-generated code and heavy open source reuse?

Why This Matters for Security Teams

Traditional software composition analysis works best when software is assembled from stable, named components with clear versioning and predictable dependency graphs. AI-generated code and heavy open source reuse break that assumption. The resulting risk is not just “what package is present,” but how code is produced, chained, copied, modified, and used at runtime. That is why inventory-only scanning often misses the real exposure surface.

This matters because modern software supply chains now include generated snippets, transitive dependencies, vendored code, and package confusion risks that do not always appear as a neat vulnerability match. NHIMG research on the LiteLLM PyPI package breach and the Nx Package Attack, 2,300+ Credentials Leaked shows how quickly trust can collapse when package integrity or developer workflow is compromised. NIST’s NIST Cybersecurity Framework 2.0 emphasises risk-informed identification and protection, which is exactly what simple component matching fails to deliver. In practice, many security teams discover these gaps only after code has already been promoted into production or copied into multiple internal repositories.

How It Works in Practice

AI-generated code changes the unit of analysis. A traditional SCA tool asks, “Which package versions are in the manifest?” but that answer is incomplete when a model produces code that inlines snippets, imports obscure libraries, or indirectly depends on transitive packages buried several layers deep. The right question is broader: “What code is actually executed, how did it enter the codebase, and what is its runtime exposure?”

Effective analysis usually combines dependency inventory with source-level and behaviour-aware checks. That means looking for hidden dependencies, generated files, copied examples, unsafe code patterns, and indirect package relationships that may not surface in a lockfile. It also means correlating SCA findings with source control history, build outputs, and runtime signals so teams can separate theoretical exposure from active risk. For AI-assisted development, review should include whether the model introduced credentials, insecure auth flows, or library calls that expand attack surface. NHIMG coverage of the DeepSeek breach and the PyPI Breach illustrates why trust in the package layer cannot be assumed.

• Track direct and transitive dependencies, including vendored or copied code.
• Scan generated code separately from human-authored code so review paths stay visible.
• Correlate SCA with build provenance, commit history, and runtime telemetry.
• Prioritise findings by exploitability and business context, not only by package presence.

Guidance aligned with the NIST Cybersecurity Framework 2.0 pushes teams toward stronger asset visibility and risk-based protection, but these controls tend to break down when generated code is merged into large monorepos because provenance becomes difficult to distinguish from ordinary developer changes.

Common Variations and Edge Cases

Tighter software supply chain controls often increase review overhead, requiring organisations to balance detection depth against developer velocity. That tradeoff becomes sharper when AI tools are used for scaffolding, refactoring, or code completion, because a large volume of low-signal changes can drown out the few issues that matter most.

There is no universal standard for how far SCA should extend into generated code, but current guidance suggests treating provenance as part of the control boundary. If code was produced by an assistant, copied from a package snippet, or stitched together from multiple open source sources, the review model should not stop at the manifest. Some teams add allowlists for approved packages, while others add policy checks for license, maintainer trust, and release integrity. Best practice is evolving toward combining SCA with code-aware analysis, secret detection, and build integrity checks. NHIMG’s research on the State of Secrets in AppSec is relevant here because AI-generated code often reproduces sensitive patterns that ordinary package scans will not flag.

The edge case that consistently defeats traditional SCA is when a dependency is technically “known” but its usage is novel, unsafe, or introduced through generated code paths that never appear in the dependency metadata.

Related resources from NHI Mgmt Group

• Why do fragmented AppSec tools struggle with AI-generated code?
• Why do traditional vulnerability tools struggle in multi-cloud and container-heavy environments?
• How should security teams use DAST to validate AI-generated code in production-like environments?
• How should security teams govern AI-generated code in production environments?