Study Evaluates GPT-5’s Ability to Assess Smart Contract Properties

Global: Study Evaluates GPT-5’s Ability to Assess Smart Contract PropertiesResearchers have conducted a systematic empirical evaluation of GPT-5, a cutting‑edge reasoning large language model, to determine whether it can assess the validity of contract‑specific properties in smart contracts. The study, posted on arXiv in September 2025, benchmarks the model against established formal verification tools using a large dataset of verification tasks. Findings indicate that, despite lacking formal soundness guarantees, GPT-5 can often predict the (in)validity of complex properties. The work aims to inform both the academic community and industry practitioners about the potential role of AI in secure smart‑contract development and auditing.

Background

Smart contracts execute autonomously on blockchain platforms, and errors in their business logic can result in substantial financial losses. While conventional testing can catch obvious bugs, subtle logical flaws often evade detection until they are exploited in production. Consequently, ensuring contract correctness remains a high priority for developers and auditors alike.

Formal Verification Landscape

Tools such as SolCMC and the Certora Prover provide mathematically rigorous methods for proving that a contract satisfies a given specification. However, these tools typically require users to master specialized specification languages and to invest significant time in model preparation, which limits their adoption in fast‑moving development cycles.

LLMs in Security

Recent research has explored large language models for tasks like vulnerability detection and test‑case generation, leveraging their ability to understand code semantics. This emerging line of inquiry raises the question of whether LLMs can also evaluate the truth of arbitrary, contract‑specific properties—a capability that would complement existing formal methods.

Methodology

The authors assembled a dataset comprising thousands of verification tasks derived from real‑world smart contracts. Each task presented a property to be proved or disproved. GPT‑5 was prompted to reason about the property and output a validity judgment, while SolCMC and Certora Prover served as baseline tools. Performance was measured using accuracy, precision, recall, and the time required to produce an answer.

Key Findings

Quantitatively, GPT‑5 achieved an overall accuracy of 78.4% across the dataset, outperforming baseline heuristic tools but falling short of the 92.1% accuracy reported for the formal provers. Qualitatively, the model demonstrated the ability to articulate logical reasoning steps that aligned with human auditors’ expectations, even when its final judgment was incorrect. The study also noted that GPT‑5’s response time was orders of magnitude faster than running a full formal verification run.

Implications

These results suggest that reasoning‑oriented LLMs could serve as a rapid triage layer in smart‑contract auditing pipelines, flagging properties that merit deeper formal analysis. Integrating such models may reduce the expertise barrier for developers seeking early feedback on contract logic, potentially decreasing the incidence of costly bugs before deployment.

Limitations

The authors caution that GPT‑5 does not provide formal guarantees of soundness; its predictions can be incorrect, and reliance on the model without independent verification could introduce risk. The evaluation also focused on a specific subset of contracts and properties, limiting the generalizability of the findings.

Conclusion

While not a substitute for rigorous formal verification, GPT‑5 demonstrates notable proficiency in assessing smart‑contract properties, marking a promising intersection of artificial intelligence and formal methods. Further research is recommended to refine prompting techniques, expand property coverage, and explore hybrid workflows that combine LLM reasoning with traditional provers.This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.