AI-powered security tools are reshaping the crypto industry’s approach to code audits, promising lower costs, faster detection and around-the-clock monitoring. But experts warn that the technology is no silver bullet.
The launch of Mythos, a new AI-driven smart contract auditing platform, has reignited debate about how much due diligence is enough in an industry that has lost billions of dollars to hacks and exploits. Mythos claims it can scan a codebase for known vulnerability patterns in minutes, a process that typically takes human auditors days or weeks. The tool’s pricing is a fraction of traditional audit rates, which can range from $10,000 for a simple contract to over $100,000 for a complex protocol.
For startups and smaller projects that have struggled to afford thorough security reviews, AI-powered auditing could be a game-changer. “We’ve seen too many projects launch with virtually no security checks because they couldn’t afford a proper audit,” said Alice Chen, a blockchain security researcher at a leading DeFi firm. “If AI can bring the cost down to a few thousand dollars and give near-instant feedback, that could raise the baseline security across the entire ecosystem.”
But Chen and other experts caution that AI is not a substitute for human judgment. “AI is excellent at pattern recognition—it can spot reentrancy attacks, integer overflows, or timestamp dependencies that have been seen many times before,” she explained. “But novel logic flaws or complex economic attacks often require deep contextual understanding. A machine might miss the forest for the trees.”
According to data from blockchain security firms, the majority of crypto losses in 2025 did not come from smart contract bugs. Instead, the largest incidents involved private key theft, social engineering, phishing campaigns, and compromise of off-chain infrastructure. The $625 million Axie Infinity hack, the $570 million Binance bridge exploit, and the $200 million Euler Finance incident all stemmed from stolen credentials or operational failures, not flaws in Solidity or Vyper code.
“We keep focusing on code audits, but the biggest risks are often people and processes,” said James Park, a former cybersecurity analyst at a major exchange. “AI can’t stop someone from clicking a malicious link or handing over their seed phrase. And it can’t protect against a rogue employee or a compromised server.”
Nevertheless, the push toward AI-enhanced security is gaining momentum. Several audit firms have already integrated machine learning models into their workflows to automate repetitive checks and flag suspicious patterns. Mythos, developed by a team of ex-Google engineers, takes this a step further by offering a fully automated pipeline that can be plugged into a project’s continuous integration system. Every time a developer pushes code, the tool runs a suite of tests and generates a report within minutes.
Proponents argue this continuous approach represents a paradigm shift. Traditional point-in-time audits, they say, leave code vulnerable after deployment. “The reality is that smart contracts evolve—upgrades, parameter changes, new integrations—all of which can introduce new bugs,” said Maria Torres, CTO of a blockchain infrastructure startup. “Allowing developers to check every change for vulnerabilities in real time could reduce the window of exposure dramatically.”
However, the industry has been burned by over-reliance on automation before. In 2023, a popular AI auditing tool mistakenly flagged hundreds of safe functions as dangerous, causing delays and confusion. More recently, a DeFi protocol using an AI-based monitor failed to detect a sophisticated flash loan attack that exploited a math error in a liquidity pool calculation. “The AI simply didn’t have training data for that specific attack pattern,” said Park. “We’re in an arms race: hackers are also using AI to find novel ways to break code.”
The conversation around AI and crypto security also touches on liability and insurance. If an AI tool approves a smart contract that later gets hacked, who bears responsibility? Traditional auditors carry professional indemnity insurance and can be sued for negligence. For AI providers, the legal landscape remains murky. “Right now, most AI audit tools come with disclaimers that they provide ‘assistance’ and not a guarantee,” said Chen. “That might change if regulators start imposing stricter standards for code security.”
Some insurers are already adjusting their policies. A Lloyd’s syndicate recently introduced a product that offers lower premiums for projects that use both a human audit and an AI-powered continuous monitoring solution. “We view AI as a way to reduce residual risk, but we still require human review for critical business logic,” said a spokesperson. “The combination seems to be the most effective approach.”
The shift toward AI-driven security also raises questions about the future of the audit profession. Many auditors worry that automation could commoditize their services and squeeze margins. Others see an opportunity to focus on higher-value work. “Routine checks can be handed off to machines, allowing human experts to concentrate on architecture reviews, threat modeling, and economic analysis,” said Torres. “The role will evolve, but it won’t disappear.”
Meanwhile, the crypto industry is grappling with broader security challenges that no technology can fully solve. Phishing attacks targeting DAO members have become increasingly sophisticated, with fake proposals mimicking legitimate ones and tricking voters into signing malicious transactions. Social engineering via Discord and Telegram remains a staple of hackers’ playbooks. Even Multi-Party Computation (MPC) wallets, designed to decentralize key control, have been breached through social manipulation of operators.
“We are investing heavily in user education and operational security training,” said the security chief at a large crypto exchange. “But it’s an uphill battle because the attackers constantly adapt. AI can help us detect unusual behavior patterns, but the human element is still the weakest link.”
Despite these limitations, the momentum behind AI in crypto security is undeniable. Venture capital funding for AI-security startups in the blockchain space reached $1.2 billion in the first half of 2026, more than double the total for all of 2025. Open-source AI models for smart contract analysis have seen thousands of downloads, and several blockchain networks are exploring native integration of AI monitoring tools at the protocol level.
For now, the most prudent approach appears to be a hybrid one: using AI to accelerate and broaden the scope of security checks, while keeping humans in the loop for judgment calls, complex analysis, and final sign-off. “AI is making security cheaper, faster, and harder to ignore,” said Chen. “But it’s not making it foolproof. We need to be humble about what these tools can and cannot do.”
As the crypto industry matures, the standards for due diligence are likely to rise. What was considered acceptable five years ago—a basic audit every twelve months—is no longer sufficient. With AI-powered tools available, regulators and insurers may begin to expect continuous monitoring and rapid response to emerging threats. The bar is being raised, and the industry will have to adapt.
Source: Coindesk News