How should security teams use LLMs for code review without overtrusting the output?

What LLMs are good for in code review

LLMs are most useful as a triage layer. They can compress large diffs, surface suspicious patterns, and point reviewers toward the parts of a change that deserve closer inspection. That is especially valuable when the codebase is large, the review queue is crowded, or the team wants faster first-pass coverage before a deeper human review.

The right mental model is “accelerate inspection,” not “decide security.” A model can highlight potentially risky logic, but it cannot establish exploitability, business impact, or whether a finding is reachable in the deployed environment. For that reason, the output should be treated as a review aid that improves coverage, not as an authoritative conclusion.

Useful use cases include summarising control-flow changes, identifying new input paths, flagging unusual authentication or authorization logic, and comparing code against known weakness patterns. For example, an LLM can help a reviewer notice that a patch added a new API call path or a new privilege check, but the reviewer still has to validate whether the change is actually exposed and whether the enforcement is complete.

Where overtrust starts to break the review process

Overtrust usually appears when teams let the model’s confidence replace evidence. A fluent explanation can make a weak finding look stronger than it is, especially when the code “sounds” like a known vulnerability but the execution path, inputs, or permissions do not line up. The risk is not that the model is always wrong, but that it can be persuasive without being grounded in runtime reality.

Reviewers should also watch for false negatives. An LLM may miss an issue because the risky behaviour depends on cross-file state, environment-specific configuration, or an indirect call path that is not obvious from a single diff. It may also underweight abuse cases that only emerge when the code runs with real credentials, real data, or real integrations.

OWASP API Security Top 10 is useful here because a code review often needs to distinguish a suspicious pattern from a genuine authorization or access-control failure. In practice, that means treating the model as a prompt to inspect the surrounding control flow, not as proof that a weakness exists.

How to validate an LLM finding before you trust it

The safest workflow is to require independent corroboration. If the model flags a possible weakness, verify it with runtime testing, targeted log review, and access validation. That combination tells you whether the behaviour is observable in the running system, whether the expected security control is actually enforced, and whether the path is reachable under realistic conditions.

Use the model to narrow the search space, then confirm the claim with concrete artefacts: executed test cases, observed responses, audit trails, or permission checks. If the code path is security-sensitive, validate both the happy path and the abuse path. A review that never leaves static text is incomplete when the question is whether the system can actually be misused.

When the change touches authentication, authorization, or session handling, the reviewer should verify the effective identity context rather than the intended one. That means checking which principal is authenticated, what privilege is actually exercised, and whether the access decision survives redirects, retries, service-to-service calls, or fallback behaviour.

NIST AI 600-1 GenAI Profile supports this style of use because it frames GenAI as something to test, govern, and monitor rather than trust by default. A code-review workflow benefits from the same principle: the model can assist judgement, but the team owns the final security conclusion.

Risk and Threat Considerations

LLM-assisted review introduces two practical risks: false assurance from convincing but unverified findings, and missed issues when the model fails to connect the diff to the real execution context. Both can leave a team believing a change is safer than it is, or safer than it can be shown to be.

Failure mechanism: The model infers security meaning from source text, but exploitability depends on runtime state, permissions, configuration, and reachable paths. Without validation, a reviewer can mistake a plausible narrative for an actual control failure.

Impact: Weaknesses can survive review, while noisy findings can waste response time and dilute attention from the code paths that matter most. In security review, that means the control must be validated by evidence, not by language quality.

Practitioner Guidance

What to verify: Require a second source of truth for every important LLM finding. If the model says a change is risky, look for an execution path, a permission boundary, or a failed control that can be demonstrated in the system itself.

Decision rule: If the issue affects exploitable behaviour, treat the model output as a hypothesis until runtime evidence confirms it. If the issue is only stylistic or maintainability-related, the LLM can be more influential, but it still should not override human judgement on security-relevant code.

Common mistake: Teams often use the model as a final reviewer because it is fast and sounds decisive. That shortcut works until the code path depends on configuration, identity, or a downstream service that the model cannot actually observe.

Practitioner takeaway: The best use of LLMs in code review is to improve coverage and speed up investigation, while the security verdict stays grounded in observable behaviour, not model confidence.

Related resources from NHI Mgmt Group

• How should security teams use LLMs in vulnerability research without overtrusting them?
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• How should security teams use LLMs for identity analytics without losing control?