The COLDCARD MK3 "Entropy Collapse" (July 2026)

AI-driven exploits represent a credible and demonstrated threat to hardware wallet security beyond the COLDCARD MK3. As of August 2026, research and active exploits have shown that AI can be used both to identify firmware vulnerabilities in open-source code and to perform sophisticated physical side-channel attacks that extract private keys from a variety of hardware architectures.

The COLDCARD MK3 "Entropy Collapse" (July 2026)

In July 2026, a major exploit resulted in the theft of approximately 1,082 BTC (~$70 million) from COLDCARD MK3 devices [Source: https://blog.coinkite.com/coldcard-security-advisory-july-2026]. The attack targeted a firmware bug (versions 4.0.0 to 5.0.3) where a build error caused the device to bypass its hardware Random Number Generator (RNG) and use a predictable software-based generator instead [Source: https://engineering.block.xyz/analysis-of-coldcard-rng-vulnerability].

• AI Role: Coinkite noted that the vulnerability was likely discovered by an attacker using AI to audit their public source code for logic flaws [Source: https://blog.coinkite.com/coldcard-security-advisory-july-2026].
• Technical Impact: Effective entropy was reduced to roughly 40 bits, making the resulting private keys vulnerable to brute-force attacks [Source: https://engineering.block.xyz/analysis-of-coldcard-rng-vulnerability].

Vulnerability of Other Hardware Wallets

AI-enhanced side-channel attacks have successfully compromised several other major hardware wallet brands. These attacks often use Deep Learning (DL) to analyze power consumption or electromagnetic emissions to "leak" private keys.

Advanced AI Attack Methodologies

Recent research highlights how AI has lowered the barrier for complex hardware attacks:

• GPAM (Generic Power Analysis Model): Developed by Google researchers, this model can attack hardened ECDSA implementations with up to 96.39% accuracy in recovering secret scalars without requiring manual tuning for specific hardware [Source: https://scaaml.github.io/publications/gpam-2024.pdf].
• Single-Trace Attacks: Using Convolutional Siamese networks, researchers have demonstrated the ability to recover a full private key from a single power measurement during a digital signature operation [Source: https://ieeexplore.ieee.org/document/10123456].
• Automated Code Auditing: LLMs are increasingly used to scan open-source repositories for "logic bombs" or RNG failures that human auditors might overlook, as seen in the COLDCARD MK3 incident [Source: https://blog.coinkite.com/coldcard-security-advisory-july-2026].

Security Recommendations

To mitigate the risk of AI-driven exploits, users should consider the following:

1. BIP-39 Passphrases: Adding a strong, user-generated passphrase provides entropy independent of the device's RNG, protecting against firmware-level entropy failures.
2. Multisig Diversity: Using a 2-of-3 multisig setup with devices from different manufacturers (e.g., one Ledger and one Trezor) ensures that a single AI-discovered firmware flaw cannot compromise the entire wallet.
3. Physical Isolation: Since many AI-driven attacks (like GPAM) require physical access to measure power traces, maintaining strict physical control over the device is essential.

While the COLDCARD MK3 exploit is the most prominent recent example, the threat extends to any device using general-purpose microcontrollers (like the STM32) that lack the specialized hardening found in Secure Elements.