AI x Crypto Systems Posted on May 31 AI Smart Contract Review: The Finding Is Not the Audit # ai # security # blockchain # web3 AI Smart Contract Review Disclosure: AI tools were used for source collection and editorial review. The article was written by a human author, who checked the facts, code, and conclusions. Crypto risk disclosure: This article is a technical explanation, not investment advice. It is not a recommendation to buy, sell or hold any cryptoasset. AI Smart Contract Review fails when a team treats a model sentence as an audit conclusion. The useful version of AI Smart Contract Review is narrower: the model can point at suspicious code, but the finding has to survive tool evidence, an execution path, a standard requirement, and human review before anyone calls it an audit result. The practical trap is not that models are always wrong. Papers such as GPTScan , iAudit , and Smart-LLaMA all support some model-assisted value. The problem is that useful triage is not the same claim as complete security review. Finding Boundary The first boundary in AI Smart Contract Review sits between "the model noticed something" and "the contract has an exploitable issue." That boundary matters because a model can explain a familiar vulnerability pattern while missing the deployment context, external call path, storage layout, or economic condition that makes the issue real. Ince et al.'s 2025 survey is a good starting constraint because the survey treats large-language-model vulnerability detection as promising but not ready to replace traditional tools. AI Smart Contract Review should inherit that caution: a model finding is a lead, not a sign-off. False Positive / False Negative The useful version of AI Smart Contract Review records how the finding failed. The artifact below is deliberately small because the audit decision needs a compact place to separate a model claim, tool evidence, missed context, and human review. Review aid What it can catch False positive sha
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