What breaks when teams rely on SBOMs and SCA alone for generated code?

Why This Matters for Security Teams

SBOMs and SCA are useful, but they answer a narrow question: what third-party components are present, and are any of them known to be vulnerable? That is not the same as proving generated code is safe. For AI-assisted development, the risky part is often the behaviour the model produced, not the package list it introduced. A clean inventory can miss insecure logic, unsafe defaults, prompt-injected code paths, and overbroad access patterns.

This is why NHI Management Group treats software composition checks as only one layer of assurance. The broader risk picture includes how generated code handles secrets, tool calls, permissions, and execution context. That concern aligns with the NIST Cybersecurity Framework 2.0, which emphasises ongoing risk management rather than point-in-time evidence. It also mirrors the findings in the Ultimate Guide to NHIs, which notes that 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.

In practice, many security teams discover the gap only after generated code has already shipped with unsafe runtime behaviour, rather than through intentional composition review.

How It Works in Practice

SBOMs and SCA are strongest when the problem is dependency exposure. They can tell teams whether a generated service pulled in a vulnerable library, whether a transitive package needs patching, and whether a known CVE is present. For generated code, that is necessary but incomplete. A model can generate bespoke logic that contains no high-risk dependency at all and still introduce privilege escalation, insecure deserialisation, weak validation, or hidden data flows.

The practical fix is to pair inventory controls with behavioural assurance. Current guidance suggests treating generated code as untrusted until it passes runtime and policy checks. That means scanning the output, but also testing what it does under adversarial prompts, malformed inputs, and unexpected tool sequences. Security teams should also verify whether the code respects least privilege, avoids long-lived secrets, and uses guarded access to APIs and internal services. The Ultimate Guide to NHIs is relevant here because generated code often becomes a non-human workload with its own credentials, lifecycle, and exposure path.

• Use SBOMs to identify what was imported, not to certify what was intended.
• Run SCA on both the generated artifact and any downstream build outputs.
• Add behavioural tests for tool use, data handling, and privilege boundaries.
• Evaluate policies at request time, especially for code that calls external services.
• Review whether secrets are embedded, cached, or passed through unsafe channels.

For control framing, the NIST Cybersecurity Framework 2.0 helps teams connect inventory with protect and detect activities, but it does not replace behavioural validation. These controls tend to break down when generated code is produced rapidly into CI/CD pipelines because the approval gate focuses on dependency hygiene while ignoring runtime intent.

Common Variations and Edge Cases

Tighter dependency controls often increase delivery overhead, requiring organisations to balance release speed against confidence in generated behaviour. That tradeoff becomes sharper when teams use code assistants for internal tools, agent workflows, or glue code that touches sensitive systems. In those cases, a low-risk SBOM can hide high-risk execution paths because the code is custom, short-lived, and difficult to review line by line.

There is no universal standard for this yet, but best practice is evolving toward layered verification. For simple utility scripts, SCA plus review may be enough. For code that handles customer data, credentials, or infrastructure actions, current guidance suggests adding adversarial testing, policy-as-code checks, and explicit approval for privileged operations. This is especially important when generated code sits inside CI/CD, because build-time scanners rarely understand the actual runtime permissions or downstream automation.

Teams should also be careful not to confuse absence of known dependencies with absence of risk. The Ultimate Guide to NHIs shows why credential and lifecycle discipline matter: 90% of IT leaders say properly managing NHIs is essential for a successful zero-trust implementation. That is a reminder that generated code often behaves like a workload identity problem, not just a packaging problem.

For teams formalising this posture, SCA should remain a gate, but never the final gate.

Related resources from NHI Mgmt Group

• What breaks when teams rely on test coverage or complexity metrics to judge AI-generated code?
• What breaks when security teams rely only on pull request scanning for AI-generated code?
• What breaks when organisations rely on static checks alone for AI generated code
• What breaks when organisations rely on post-code scanning alone for AI-generated code?