They treat probabilistic output like reliable evidence. LLM-only scanning can change its findings across identical runs, so without independent validation it creates noise, weakens auditability, and makes it hard to justify a deployment decision for contract code.
Why LLM-Only Scanning Breaks Down on Solidity
Solidity code rewards precision, not plausibility. An LLM can be useful for triage, but it is a weak basis for declaring a contract safe, unsafe, or ready to ship because the model is not executing the code, proving invariants, or checking state transitions against ground truth.
That gap matters more in smart contracts than in ordinary code review because small errors can change asset flow, ownership, access control, or upgrade behaviour. A model that sounds confident can still miss a reentrancy path, an authorization bug, or a subtle dependency on external calls.
For Solidity review, the real question is whether the scanner can anchor its output to evidence the team can reproduce, not whether the explanation reads well.
What Teams Misread as “Signal”
The biggest mistake is treating probabilistic commentary as if it were deterministic analysis. If the same source yields different findings across runs, the tool is better at generating hypotheses than at establishing reviewable evidence.
That becomes a process problem as much as a technical one. Teams start normalizing output that cannot be audited, compared, or explained to a release approver, which makes it hard to defend why one contract was blocked and another passed.
LLM-only results also tend to blur severity. They may mention a pattern that looks familiar but do not reliably distinguish between a real exploit path, a harmless code smell, and a condition that only matters under specific deployment assumptions.
When the scanner is used without a second verifier, that ambiguity can either create false confidence or swamp reviewers with noise. In both cases, the output stops being a decision aid and becomes an opinion generator.
What a Solidity Workflow Needs Instead
A sound workflow uses the LLM as one layer in a larger evidence chain. The model can help prioritize suspicious functions, summarize likely risk areas, or suggest places for deeper inspection, but the final decision should rest on reproducible checks such as compiler output, deterministic static analysis, test execution, fuzzing, and manual review of the specific control flow.
This is especially important for contract code because the security question is often not “does this look wrong?” but “can this state transition be triggered, and under what conditions?” That requires validation against the actual code path and, when relevant, the deployed environment.
Teams should also separate explanation from assurance. An LLM may produce a useful narrative for developers, but a narrative is not evidence of exploitability, impact, or remediation priority. If the process cannot show why a finding is true, it should not be used as the basis for release gating.
When teams need a broader security frame for AI-driven review and governance, NIST AI RMF and the NIST AI 600-1 GenAI Profile are useful references for testing, traceability, and accountability in generative AI workflows. For contract-specific verification, NIST SP 800-53 Rev 5 Security and Privacy Controls reinforces the need for auditable analysis, control validation, and change oversight.
How to Decide Whether the Output Is Trustworthy
Trust the scanner only when it can be paired with an independent check that would reach the same conclusion for the same reason. If the finding cannot survive a rerun, a test, or a code path review, it is not ready to drive a deployment decision.
That does not mean LLMs have no role. It means their role is bounded: useful for triage, useful for explanation, and risky when promoted to the status of sole verifier. Solidity teams get into trouble when they confuse speed with assurance.
For higher-confidence review, the best pattern is layered: use the model to surface candidates, then confirm with deterministic tooling and targeted human inspection. If the contract controls funds, permissions, or upgrade authority, require stronger evidence before accepting a clean bill of health.
The same discipline appears in OWASP’s work on API Security Top 10: the objective is not just finding issues, but making sure the analysis can reliably identify broken authorization and other high-impact failures. For a practitioner view of where LLM-based review can drift into false confidence, the OWASP Agentic AI Top 10 is a useful reminder that AI output must be constrained by validation and control, not treated as an authority by default.
Risk and Threat Considerations
When Solidity teams trust LLM-only scanning, the failure mode is usually false assurance. A contract can pass a review because the model sounded confident, even though the finding was unstable, incomplete, or dependent on a missed execution path.
Failure mechanism: The scanner produces non-deterministic judgments, so identical code may generate different conclusions across runs. That undermines auditability, weakens reviewer trust, and can let a vulnerable contract move forward without a reproducible basis for approval.
Impact: The team may ship code with unresolved economic or authorization flaws, or it may waste time chasing noisy findings that do not survive validation. In both cases, the review process loses credibility and the release decision becomes harder to justify.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST AI RMF, NIST SP 800-53 Rev 5 and OWASP ASVS set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | Govern | GenAI review workflows need accountable, testable oversight. |
| Recommendation — Define governance, validation, and escalation rules for LLM-assisted code review. | ||
| NIST SP 800-53 Rev 5 | AU-6 — Audit Record Review, Analysis, and Reporting | Auditability matters when model findings vary across runs. |
| CM-3 — Configuration Change Control | Contract deployment decisions depend on controlled, reviewable changes. | |
| Recommendation — Require traceable review evidence and retain outputs for comparison. Gate Solidity releases through controlled change review and approval. | ||
| OWASP ASVS | V15 — Secure Coding and Architecture | Solidity review depends on sound security analysis, not plausibility alone. |
| Recommendation — Verify contract logic with deterministic analysis before trusting AI-assisted findings. | ||
Practitioner Guidance
What to verify: Require at least one independent, deterministic confirmation path for any high-severity Solidity finding. If the same issue cannot be reproduced by another tool or by targeted code-path inspection, treat it as a hypothesis, not a release blocker.
Common mistake: Do not let fluent explanations substitute for proof. A strong narrative can be useful for triage, but it should never outrank test results, static analysis, or code review evidence when contract safety is on the line.
What good looks like: The LLM flags likely issues, deterministic tooling confirms or rejects them, and reviewers can explain the conclusion in a way that would hold up in an audit or post-incident review.
Practitioner takeaway: Use LLMs to accelerate scrutiny, not to certify correctness. For Solidity, repeatability and independent validation are what turn a suspicion into a defensible security decision.