Web3 Wallets in a "Season of Turmoil": Understanding the Evolution of Crypto Security's "Sword and Shield" in the AI Era

As attacks become automated and scaled, wallets must upgrade to a dynamic defense system that covers the entire usage cycle.

In the past month, the security nerves of the Crypto circle have once again been tightened.

First, Coldcard was exposed for a serious random number generation vulnerability, followed by Trezor and SafePal disclosing risks of user privacy data leaks.

At first glance, these three incidents seem to have little in common, but if we extend the timeline a bit, we find they point to an increasingly important question:

As AI begins to automate vulnerability discovery, attack development, and social engineering, how many areas of a crypto wallet might become the next weak link sought by attackers?

1. With AI, Hacker Attacks Transition from "Craftsmanship" to "Industrialization"

Objectively speaking, these three incidents exposed completely different attack surfaces.

Coldcard's issue lies in private key generation, which is a serious security problem; Trezor's issue arose from third-party logistics services, while SafePal's issue was related to order systems and plugin permissions, which are risks associated with privacy leaks.

Although there is currently no evidence proving that these three incidents are directly related to AI, it must be acknowledged that in the AI era, hackers' "toolkits" are undergoing a significant transformation.

In the past, many advanced cyber attacks were fundamentally limited by a very real constraint—human time.

Researching a large codebase, understanding call relationships, and finding long-hidden logical vulnerabilities require experienced security researchers to invest a lot of time; gathering identity information about a specific user, studying their habits, and designing a sufficiently convincing phishing email could even take months to construct a complex social engineering script.

This led to a trade-off in past attacks: either highly automated but relatively crude attack methods that catch a few users in a wide net; or meticulously designed attacks targeting specific high-value goals that are difficult to scale.

However, with the rapid evolution of AI capabilities today, hackers' toolkits have been completely upgraded:

• Automated Vulnerability Discovery: AI can assist attackers in quickly analyzing smart contracts, client-side code, and even firmware, automatically searching for zero-day vulnerabilities and logical flaws.
• Scaled Social Engineering: Phishing emails that once required careful crafting can now be automatically generated by AI based on leaked user identity data, producing highly customized and persuasive phishing content, text messages, or even voice/video communications;
• Intelligent Attack Implementation: From target selection to multi-channel concurrent deployment, the cost of the entire attack chain has dropped to historical lows;

It can be said that from target selection, vulnerability research, to malicious code generation, social engineering, and attack content deployment, the capabilities that were previously dispersed among different attackers are gradually being compressed into a more automated workflow.

This is also the truly profound impact of AI on cybersecurity.

It may not suddenly create an unprecedented attack method, but it is rapidly lowering the costs of existing attack methods—finding a vulnerability has become cheaper, analyzing a target faster, and generating a thousand different versions of phishing emails is also much easier than before.

In other words, the reason many systems were not attacked in the past does not necessarily mean there were no vulnerabilities; sometimes it was simply because vulnerabilities were too hard to find, the cost of attacks too high, and the cost-effectiveness of attacking the victims too low. Now, the invisible security boundary that relied on "attackers not having that much time" is gradually thinning.

From this perspective, the security offense and defense of crypto assets is also expanding from the relatively centralized "private key battle" to a full-chain tug-of-war covering code, devices, supply chains, user identities, and daily interactions.

What AI does is simply press the accelerator further.

2. The True Attack Surface of Wallets Goes Beyond Just a String of Mnemonic Words

This is why the recent incidents appear particularly representative when viewed together.

They hit different points in the wallet lifecycle, reminding us that the risks faced by wallets have long surpassed the single dimension of "whether the private key has been stolen" and are embedded in every link of private key generation, hardware devices, logistics supply chains, and even user privacy information.

We can break this down simply.

Coldcard is the most typical example; its issue occurred before users even started using the wallet.

The mnemonic words still appear to be 12 or 24 normal words, the device can sign and transfer normally, and users may find it hard to detect any anomalies, but the random number that generated this string of mnemonic words is not random, and even if your mnemonic words are not shared with anyone, you may still face risks.

Because the premise of "keeping the mnemonic words safe" is that this string of mnemonic words was generated in a sufficiently secure and unpredictable manner.

Then there are Trezor and SafePal.

Unlike Coldcard, their hardware itself was not compromised, and the mnemonic words are intact; however, they leaked users' purchase records—including names, phone numbers, emails, and even shipping addresses.

This is akin to buying a top-notch explosion-proof safe; the safe hasn't been broken into, but the shipping slip from the logistics company was lost, clearly stating your name, email, phone number, where you live, and that you purchased a hardware wallet specifically designed to store crypto assets.

What attackers gain is a potential lead on high-value crypto users, allowing them to impersonate wallet customer service to send "urgent firmware upgrade" notifications, customize phishing pages based on the purchased model, call claiming there is an issue with the order, and even further associate users' social media, public identities, and on-chain addresses.

In other words, just because cryptography cannot be cracked does not mean there are no avenues for attack.

In reality, there is even an extreme saying that has circulated in the Crypto community for many years—the "$5 wrench attack": no matter how strong the encryption algorithm, it cannot solve the problem of attackers directly finding the asset holders.

This is not entirely a theoretical risk. According to data provided by Chainalysis to the Financial Times, as of mid-August 2026, there have already been at least 46 recorded violent attacks against crypto holders this year, with over half involving kidnappings and more than a third involving home invasions.

So looking back at these three incidents, we find that today's so-called "wallet security" has actually become a long chain:

From wallet code, random number and key generation, to chips, firmware, and devices, then to official websites, purchasing channels, supply chains, logistics, and order databases; once users truly start using it, it will connect to RPC, DApps, browser plugins, and smart contracts, and then involve authorizations, signatures, customer service, social media, and even AI Agents.

Any weak link in this chain could bypass the security defenses established by other links.

3. As Attacks Begin to Automate, Defense Must Integrate AI

If AI continues to evolve at its current pace, the issues exposed today may only be the beginning.

Because one of the things AI excels at is continuously searching for anomalies, repeating patterns, and weak links in a large system.

Attackers can have Agents continuously scan open-source code, batch test web pages, APIs, and plugin permissions, and automatically collect information from social media and public databases to filter potential high-value targets.

Even phishing itself may evolve from the past monotonous messages of "your wallet is about to expire, please enter your mnemonic words" to real-time conversations that truly understand who you are:

• If attackers know you just purchased a specific model of hardware wallet, they can generate a corresponding "firmware security notification" for you;
• If they know you recently participated in a certain DeFi protocol, they can impersonate the project party to have you migrate to a new protocol vault;
• If they further obtain your social accounts and public statements, they can even mimic familiar team members, KOLs, or customer service personnel to communicate with you;

From this perspective, a significant challenge that wallets will face in the future is whether defense can still rely solely on static rules when attacks have upgraded from "fixed rules" to systems that can analyze, judge, and change.

After all, previous wallet security mechanisms were still relatively close to a "rulebook": if a certain address is marked as a phishing address, a pop-up reminder appears; if a certain domain enters a blacklist, access is prohibited; if a certain authorization model is high-risk, an additional prompt is added.

These mechanisms are still important, but in the face of increasingly dynamic attacks, relying solely on risks that have already occurred to identify the next risk is clearly insufficient.

AI can precisely become a very important supplement to the defense side; in fact, this is not a suddenly emerging new proposition.

In previous discussions around "AI × Web3 Security," similar directions have been proposed: the future security capabilities of wallets should not only stop at address blacklists, risk labels, and fixed pop-ups, but can leverage AI to move security judgments further upstream in the user's entire transaction process.

For example, before code enters the production environment, AI can continuously review code dependencies, call paths, and abnormal logic; when a user accesses a DApp, it can assess whether it is abnormal by combining domain history, front-end behavior, contract addresses, and on-chain relationships; before signing, it can simulate the actual results of the transaction execution rather than just presenting users with a string of incomprehensible hexadecimal data.

Going a step further, wallets can even gradually establish dynamic security models for each user.

An account that has only conducted a few hundred dollars in transfers suddenly preparing to authorize all assets to a newly deployed contract just two hours old is an abnormal signal in itself; a user who has never interacted with a certain address suddenly requesting unlimited Approval should also receive a higher priority risk alert; and an email claiming to be from the wallet's official source, requesting users to enter their mnemonic words, regardless of how realistic the content is, should be directly classified as high risk.

Thus, the changes brought by AI may not only be about "automatically helping users determine whether an address is safe"; it is more about enabling wallets to evolve from a relatively passive key management and signing tool to gradually possessing a proactive risk judgment capability.

This also makes the previously discussed additional layer of security boundaries even more important, namely that AI can help users understand and execute complex operations, but the control of assets cannot be infinitely relinquished; for significant transfers, new address authorizations, sensitive contract interactions, and other critical actions, it is still necessary to limit AI's capabilities within clearly defined authorization scopes through minimum permissions, human confirmations, pre-execution simulations, and clear explainability.

Especially in truly abnormal situations, clearly informing users "why it is dangerous," "what will happen after execution," and "where the risks lie."

In other words, the significance of AI defense lies in promoting wallets to evolve from a passive signing tool to gradually possessing the ability to actively understand transactions, identify anomalies, and constrain execution.

In Conclusion

The recent series of wallet security incidents does not mean that the self-custody model has lost its value, nor does it mean that users should return all asset control to centralized platforms.

What they truly remind us of is that self-custody has never equated to inherent security; it merely returns the absolute control of assets to users.

And protecting this control requires a security system that can evolve and upgrade with the times, because security is not a one-time product delivery; it is a long-term dynamic evolution that requires the joint efforts of users, project parties, and wallet manufacturers.

Attackers can use AI to understand code, users, and environments, and defenders can do the same.

This will be a protracted "sword and shield" upgrade race.

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