Code written by a large language model rather than a human developer. It can accelerate delivery, but it must still be treated as untrusted output. The main risk is that the code may look correct, pass limited tests, and still contain logic flaws, security issues, or maintainability debt.
Expanded Definition
LLM-generated code is code produced by a large language model from prompts, examples, or task descriptions rather than written line by line by a human developer. In practice, it sits inside a broader software delivery chain that may also include human review, automated testing, and secure build controls. The key security question is not whether the code is machine-authored, but whether it has been validated before it reaches production.
Definitions vary across vendors and teams because some treat code completion as assistive tooling while others classify any model-authored block as generated output. In NHI and agentic AI environments, the distinction matters because generated code may embed API calls, secret-handling patterns, permission checks, or tool invocation logic that affect the identity and access posture of the system. Guidance from the NIST AI Risk Management Framework and the OWASP Top 10 for Agentic Applications 2026 both reinforce that generated output must be evaluated for security, reliability, and downstream misuse potential, not assumed safe because it compiles.
The most common misapplication is treating LLM-generated code as reviewed engineering output, which occurs when teams merge it after only syntax checks or narrow unit tests.
Examples and Use Cases
Implementing LLM-generated code rigorously often introduces review overhead and slower merge velocity, requiring organisations to weigh development speed against the cost of deeper validation.
- Boilerplate service code is generated for an internal API, then checked against secure coding standards before merge so the team does not inherit unsafe defaults.
- An AI agent writes a connector for secrets retrieval, but reviewers verify it does not log tokens or weaken authentication flows referenced in the OWASP NHI Top 10.
- A developer uses generated test scaffolding to accelerate coverage, then replaces brittle assertions with domain-specific checks aligned to the NIST AI 600-1 Generative AI Profile.
- A team prototypes infrastructure glue code for a workflow agent, and the output is compared with lessons from Analysis of Claude Code Security to catch hidden trust assumptions.
- Security engineering uses generated scripts for log parsing or dependency checks, but runs them in isolated CI jobs so tool access is constrained and observable.
These use cases are useful when the code is narrow, disposable, and heavily reviewed, but they become risky when teams let generated output own security-sensitive paths without human scrutiny.
Why It Matters in NHI Security
In NHI security, LLM-generated code can create new attack paths faster than traditional development cycles can absorb them. A generated client may mishandle service account tokens, over-broaden permissions, or expose tool calls that let an agent move beyond its intended scope. That is why NHI governance has to include code provenance, review standards, and runtime controls, not just model selection.
NHIMG research shows how quickly identity misuse becomes operational: in the LLMjacking analysis, exposed AWS credentials were often targeted within 17 minutes on average, and sometimes within 9 minutes. The same urgency applies when generated code introduces weak credential handling, unsafe logging, or permissive automation around NHIs. The broader trend is also clear in the AI Agents: The New Attack Surface report, which shows that many organisations still lack visibility into what their agents can access and do.
Organisations typically encounter the full impact only after a bad release, secret exposure, or agent misuse incident, at which point LLM-generated code becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF, NIST AI 600-1 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 | Generated code often mishandles secrets and identity-bound workflows. |
| OWASP Agentic AI Top 10 | A-04 | Agentic code can create unsafe tool use, scope creep, and hidden execution paths. |
| NIST AI RMF | Calls for governed, measurable AI outputs across the full lifecycle. | |
| NIST AI 600-1 | Addresses generative AI risks in software and operational contexts. | |
| NIST Zero Trust (SP 800-207) | SC.L2-3 | Generated code can weaken least-privilege and trust assumptions. |
Review generated code for secret handling, token exposure, and insecure NHI integration before deployment.
Related resources from NHI Mgmt Group
- What do security teams get wrong about LLM-generated authentication code?
- How do engineering and AppSec teams decide when to trust LLM-generated code suggestions?
- What is the difference between scanning AI-generated code and governing AI agent identity?
- When do AI-generated code and assistants increase secret exposure risk?
