Mythos, Anthropic’s new AI model that has sparked fear and confusion in traditional technology and finance, is also driving a massive shift in the way the crypto industry thinks about security.
For years, decentralized finance has focused its defenses on smart contracts. Code is audited, vulnerabilities are catalogued, and many common exploits are well understood. But Mythos, a model designed to identify and chain weaknesses across systems, is moving the focus beyond the code and toward the infrastructure that supports it.
“The biggest risks are in the infrastructure,” said Paul Vijender, head of security at Gauntlet, a risk management company. “When I think about AI-driven threats, I’m less concerned about smart contract exploits and more focused on AI-assisted attacks against human and infrastructure layers.”
That includes key management systems, signing services, bridges, Oracle networks, and the cryptographic layers that connect them. These components are less visible than smart contracts and often fall outside the scope of traditional auditing.
In fact, this month, web infrastructure provider Vercel, which many crypto companies use, disclosed a security breach that may have exposed customers’ API keys, causing crypto projects to rotate credentials and revise their code. Vercel traced the intrusion to a compromised Google Workspace connection through the third-party AI tool Context.ai, which was used by an employee.
Mythos belongs to a new class of artificial intelligence systems created to simulate adversaries. Instead of looking for known bugs, it explores how protocols interact, testing how small weaknesses can be compounded into real-world exploits. That approach has drawn attention beyond cryptocurrencies. Banks like JP Morgan are increasingly treating AI-driven cyber risk as systemic and are exploring tools like Mythos for stress testing. Earlier this month, Coinbase and Binance reportedly approached Anthropic about testing Mythos.
Early findings from models like Mythos have identified weaknesses in the behind-the-scenes systems that keep crypto platforms secure, including technology that protects keys and handles communication between systems.
“I think there are two areas where AI models are especially valuable,” Vijender said. “First, multi-step exploit chains that historically are only discovered after money is lost. Second, infrastructure layer vulnerabilities that traditional audits never touch.”
That change is important in a system based on composability, where DeFi protocols can connect and leverage each other’s services.
DeFi protocols are designed to be interconnected. They share liquidity, rely on common oracles, and interact through layers of integrations that are difficult to fully map. That interconnectedness has fueled growth, but also creates avenues for risk to spread, as seen in recent bridging attacks like the Hyperbridge attack, in which an attacker minted $1 billion in bridged Polkadot tokens on Ethereum by exploiting a flaw in the way cross-chain messages were verified.
“Composability is what makes DeFi capital efficient and innovative,” Vijender said. “But it also means that a minor vulnerability in a protocol can become a critical exploitation vector with potential for ecosystem-wide contagion.”
Without AI, those dependencies are difficult to track. With AI, they can be mapped and exploited at scale. The result is a shift from isolated exploits to systemic flaws that cascade through protocols.
Evolution of AI attacks
Still, some industry leaders see Mythos as an acceleration rather than an inflection point.
At Aave Labs, founder Stani Kulechov said AI reflects the dynamics already at play in the DeFi adversary environment.
“Web3 is no stranger to motivated and well-funded adversaries,” he told CoinDesk. “AI models represent an evolution in the tools used to achieve exploits.”
From that perspective, DeFi is already designed for machine-speed attacks. Smart contracts are executed automatically and defenses such as settlement mechanisms and risk parameters operate without human intervention.
“DeFi operates at the speed of computing, so AI does not introduce a new dynamic,” Kulechov said. “It intensifies an environment that has always required constant vigilance.”
Still, Aave is seeing AI bring new categories of vulnerabilities to light, including issues that human auditors may have previously deprioritized.
“The Mythos paper shows that AI can discover old bugs that were not previously prioritized,” he said.
That breadth remains important in a system where even smaller vulnerabilities can undermine trust or combine into larger vulnerabilities.
If attackers can move faster, the question is whether defenses will be able to keep up.
For both Gauntlet and Aave, the answer lies in changing the security model itself. Pre-implementation audits and post-implementation monitoring were designed for human-paced threats. AI compresses that timeline.
“To defend against offensive AI, we will need to take an AI-centric approach where speed and continuous adaptation are essential,” said Gauntlet’s Vijender. That includes continuous auditing, real-time simulation, and systems built assuming breaches will occur.
A ‘higher path’
Aave has already integrated AI into its workflows, using it for simulations and code reviews alongside human auditors. “We take an AI-first approach that adds clear value,” said Aave Labs’ Kulechov. “But it complements, rather than replaces, human-led auditing.”
In that sense, AI equips both attackers and defenders.
For builders, the long-term effect may be less disruptive than divergence.
“We haven’t tested Mythos yet, but we’re really interested in what it and similar tools can do for protocol security,” said Hayden Adams, founder and CEO of Uniswap Labs. “AI gives builders better ways to stress test and harden systems.”
Over time, Adams expects the gap between secure and insecure protocols to widen.
“Projects that prioritize security will have a greater ability to test and harden systems before they are launched,” he said. “Projects that don’t do so will be at greater risk.”
That may be the real change. Security is no longer about eliminating vulnerabilities. It’s about continually adapting to a system in which those vulnerabilities are constantly rediscovered and recombined.
Read more: Forget bitcoin and quantum risks. Anthropic’s Mythos AI Could Have Major Implications for DeFi
