Opinion by: Jesus Rodriguez, co-founder of Sentora
AI for coding has successfully established product-market fit, and Web3 is part of this trend. Among the areas AI is set to revolutionize, smart contract audits are particularly primed for transformation.
Current audits are often one-time, snapshot assessments that struggle in a fluid, competitive market and frequently overlook economic failure scenarios.
The focus is shifting from manual reports to continuous, tool-based assurance: models integrated with solvers, fuzzers, simulations, and live telemetry. Teams that embrace this will deliver faster with wider coverage; those that don’t risk falling into obsolescence and uninsurability.
Audits are less frequent than perceived
Audits have become the go-to due diligence practice in Web3, serving as proof that someone attempted to compromise your system before the market does. However, this practice is a remnant of a pre-DevOps age.
In traditional software, assurance is woven into the development process: tests, continuous integration/continuous deployment checkpoints, static and dynamic analysis, canaries, feature flags, and in-depth observability. Security functions as micro-audits with every merge. Web3 has revived the explicit checkpoint due to immutability and adversarial economics eliminating rollback options. The logical next step is to weave platform practices with AI, ensuring assurance is a constant, not a sporadic task.
Limitations of smart contract audits
Audits provide crucial time and insights. They compel teams to clarify invariants (value conservation, access control, sequencing), validate assumptions (oracle integrity, upgrade authority), and rigorously test failure parameters before capital is invested. Effective audits yield valuable artifacts: threat models that persist across versions, executable properties that serve as regression tests, and runbooks that simplify incident management. The field must advance.
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The constraints are inherent. An audit captures a dynamic, composable system at a single point in time. Upstream modifications, liquidity variations, Maximum Extractable Value (MEV) strategies, and governance activities can invalidate previous assurances. The scope is limited by time and budget, emphasizing known bug classes while emergent behaviors (like bridges, reflexive incentives, and interactions across decentralized autonomous organizations) remain hidden. Reports can foster a misleading sense of closure as launch timelines accelerate the triage process. The most critical failures often stem from economic factors rather than syntactical issues, necessitating simulation, agent modeling, and real-time telemetry.
AI’s current limitations in smart contract coding
Modern AI thrives in environments rich in data and feedback. Compilers provide guidance at the token level, and models are now capable of scaffolding projects, translating languages, and refactoring code. However, smart contract engineering is more challenging. Correctness is both temporal and adversarial. In Solidity, safety hinges on execution order, alongside the potential for attacks (like reentrancy, MEV, and frontrunning), upgrade pathways (including proxy layout and delegatecall contexts), and gas/refund dynamics.
Numerous invariants extend across transactions and protocols. On Solana, factors like the accounts model and parallel runtime impose additional constraints (PDA derivations, CPI graphs, compute budgets, rent-exempt balances, and serialization layouts). Such properties are often lacking in training data and difficult to encapsulate with unit tests alone. Current models have limitations here, but improvements can be achieved through better data, stronger labels, and tool-grounded feedback.
A practical approach to AI auditing
A realistic development strategy comprises three essential components.
First, audit models that merge large language models with symbolic and simulation backends. Allow models to deduce intent, suggest invariants, and generalize from established patterns; enable solvers/model-checkers to provide guarantees through proofs or counterexamples. Suggestions should be grounded in verified patterns. Output artifacts should be proof-carrying specifications and reproducible exploit traces, rather than just persuasive narratives.
Next, agent processes coordinate specialized agents: a property miner; a dependency crawler building risk graphs across bridges/oracles/vaults; a mempool-aware red team seeking minimal-capital exploits; an economics agent evaluating incentives; an upgrade director rehearsing canaries, timelocks, and kill-switch drills; and a summarizer producing governance-ready briefings. This system functions like a nervous system—constantly sensing, reasoning, and acting.
Finally, evaluations based on what truly matters. Beyond unit tests, assess property coverage, counterexample yield, novelty of state-space, time taken to discover economic failures, minimal exploit capital, and runtime alert accuracy. Public benchmarks derived from incidents should evaluate bug families (reentrancy, proxy drift, oracle skew, CPI abuses) and the quality of triage, not merely detection. Assurance should operate as a service, complete with explicit Service Level Agreements that insurers, exchanges, and governance can rely upon.
Consider a generalist AI auditor
The hybrid approach is persuasive, but trends in scale suggest another alternative. In related fields, generalist models that manage tools end-to-end have matched or even exceeded specialized pipelines.
For audits, a sufficiently advanced model—with extensive context, reliable tool APIs, and verifiable outputs—could internalize security practices, reason over whole traces, and treat solvers/fuzzers as implicit subroutines. Accompanied by long-term memory, a single loop could draft properties, suggest exploits, guide searches, and explain fixes. Even so, anchors like proofs, counterexamples, and monitored invariants are crucial—pursue hybrid soundness for now while observing if generalist models can enhance components of the pipeline in the future.
AI smart contract auditors are on the horizon
Web3 embodies immutability, composability, and adversarial dynamics—an environment where episodic, handcrafted audits cannot cope with a state that evolves every block. AI excels when code is plentiful, feedback is rich, and verification is mechanical. These dynamics are converging. Whether the prevailing model is today’s hybrid or tomorrow’s generalist, integrating tools end-to-end, assurance is shifting from milestone-based to platform-based—continuous, machine-enhanced, and supported by proofs, counterexamples, and monitored invariants.
Approach audits as a product, not just as a deliverable. Initiate the hybrid loop—executable properties in CI, solver-aware assistants, mempool-aware simulations, dependency risk graphs, invariant sentinels—and let generalist models streamline the pipeline as they develop.
AI-augmented assurance transcends mere checkbox verification; it evolves into a functional capability for a composable, adversarial ecosystem.
Opinion by: Jesus Rodriguez, co-founder of Sentora.
This article is for general information purposes and is not intended to be and should not be taken as legal or investment advice. The views, thoughts, and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.
