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AI-driven security is lowering barriers to smart contract audits
AI-powered security tools are rapidly changing how the crypto industry approaches smart contract safety. Systems like Mythos — an AI code auditor that briefly launched before being pulled from the U.S. market — promise to make audits faster, far cheaper and more widely accessible. That shift could alter expectations around due diligence for developers, projects and institutional investors alike.
From costly, one-time reviews to continuous, affordable scans
Historically, comprehensive smart contract audits were expensive and time-consuming. Projects often had to budget weeks and tens of thousands of dollars to secure a formal review. For many smaller teams or early-stage protocols, that expense meant audits were skipped or limited in scope.
AI tools change that economics. By automating vulnerability discovery and producing rapid assessments, these systems push the marginal cost of a basic audit toward zero. As Alexander Urbelis, CISO at ENS Labs, observed, what once required weeks and significant budgets can now be completed in minutes — enabling projects that previously couldn't afford professional reviews to run quick, repeatable checks.
How AI differs from traditional fuzzers and scanners
Automation in security is not new. For years, researchers have used fuzzers — automated tools that bombard code with inputs to trigger unexpected behavior — to find bugs. AI-based systems, however, operate differently. They can reason about code intent, infer expected behaviors and compare those expectations against actual implementations.
That reasoning capability makes AI tools feel more like an attacker: they iterate, adapt and follow chains of behavior rather than simply executing deterministic paths. As David Schwed, COO of SVRN and founder of a cybersecurity master’s program, noted, modern models can operate more like human adversaries, taking next steps based on what they discover in real time.
Continuous auditing: a potential new industry standard
One of the most significant implications of affordable AI audits is the shift from point-in-time reviews to continuous monitoring. Instead of paying for a single, expensive audit prior to launch, teams could run frequent automated checks with suggested remediations. This continuous approach reduces the window of exposure and makes ongoing security hygiene achievable for a wider range of projects.
If continuous AI-assisted scanning becomes commonplace, the industry’s standard of care could evolve. Legal and commercial expectations may follow: investors might begin to expect AI audits before funding, and regulators could view the absence of automated checks as a failure to take reasonable precautions.
Alexander Urbelis warns that as tools become ubiquitous and inexpensive, the argument that audits were too costly to perform will carry less weight. "A clean AI report will be seen as no defense," he explained — plaintiffs could argue that because the tool existed and was affordable, any overlooked vulnerability should have been detected.
Limitations: AI is powerful but not a complete solution
Despite their strengths, AI auditors are not a wholesale replacement for human expertise. There are two critical areas where human judgment remains essential:
- Economic and incentive-based vulnerabilities: Some of the largest crypto losses arise from design choices, governance incentives or economic attack vectors that require nuanced analysis of game theory and human behavior. AI excels at finding code-level flaws but is weaker at identifying adversarial incentives or malicious intent baked into a system’s economics.
- Social engineering and key compromises: Many major incidents — including breaches like Ronin, Bybit or the Drift compromise — were driven by compromised keys, manipulated signing processes or social engineering rather than exploitable smart contract code. No code scanner prevents an authorized signer from approving a malicious transaction.
Schwed emphasizes a practical warning: running an AI audit without the capacity to evaluate and interpret its findings is not a security program. "If the person running the tool can't evaluate what comes back, you haven't bought security — you've bought a false sense of it," he said.
Implications for developers, institutions and investors
For developers and security teams, AI offers a powerful new layer of defense: faster triage, automated remediation suggestions and continuous regression checks that catch new issues as code evolves. Open-source projects and smaller teams stand to benefit most, gaining access to higher-quality security feedback they previously could not afford.
Institutions and investors will likely adjust their diligence processes. Routine AI audits could become part of standard investment checklists, and failure to run such scans may raise red flags during fundraising or mergers. Communities and governance bodies may also incorporate automated monitoring into best-practice guidelines.
What the future might look like
AI will not eliminate crypto’s security challenges, but it can materially change one factor: the cost and speed of detecting code-level bugs. That change will elevate expectations and push the industry toward continuous security practices. Human auditors, security architects and governance experts will still be needed to analyze complex economic attacks and to manage operational risks like key custody and contributor trust.
Ultimately, the maturation of AI security tools will force a cultural shift. Projects that rely solely on an AI stamp of approval without expert oversight could be exposed to new reputational and legal risks. Conversely, teams that combine automated, continuous auditing with rigorous human review and resilient operational security will be better positioned to prevent both code flaws and the broader adversarial threats that have historically caused the largest losses in crypto.
As these tools become more accessible, the baseline for what counts as reasonable due diligence is likely to shift — and the industry will need to adapt its standards, legal expectations and best practices accordingly.
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