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Crypto security teams seek Anthropic’s frontier Mythos model amid AI‑driven hacks; only a handful have entry, highlighting a growing defender‑attacker gap.
Crypto firms are racing to secure Anthropic’s restricted Mythos AI model after reports that AI‑assisted attacks on Bitcoin bridges and hardware wallets have accelerated, prompting calls for faster defender access [1].
| At a glance | |
|---|---|
| Firms with confirmed access | Coinbase |
| Firms seeking access | Binance, Fireblocks |
| Model under restriction | Anthropic Mythos 5 (cyber‑capable) |
| Catalyst | Rise in AI‑assisted exploits on crypto infrastructure [2] |
Anthropic’s “Project Glasswing” grants vetted security teams entry to Mythos 5, a version of the model that removes the safety filters present in the public Fable 5 and is capable of advanced cybersecurity tasks [1]. Coinbase has confirmed it holds access, while Zcash co‑founder Zooko Wilcox disclosed that Mythos was used to audit the Zcash protocol at Shielded Labs’ request [1]. Binance’s chief security officer Jimmy Su says the exchange has not yet obtained the most advanced model, warning that broader release could empower attackers faster than defenders [1].
Recent incidents underscore the urgency. Boltz, a Bitcoin swap service, paused its non‑custodial bridge after attackers leveraged AI to outpace its patching speed [1]. Likewise, hardware‑wallet maker Coinkite reported that a flaw in seed generation was likely discovered using AI to comb through prior firmware revisions [1]. Security teams have observed a surge in critical‑severity vulnerabilities coinciding with previews of these advanced models [1].
Solana Foundation CISO Michael Coates and Blockchain Capital’s Sean Cheetham argue that the verification process for “verified defenders” should be accelerated, noting that the pool of legitimate security researchers far exceeds the small groups capable of running sophisticated attacks [2]. HackerOne has joined Project Glasswing but limits its testing to its own systems, while Fireblocks continues to rely on public AI models for penetration testing [2]. The tension remains: developers cite risk management for gating powerful models, yet defenders fear the gap will widen as public releases approach the capabilities of restricted versions [2].
The scramble for restricted AI tools highlights a pivotal inflection point: if defenders cannot match the speed of AI‑enhanced attackers, the security of crypto infrastructure may hinge on how quickly model developers broaden vetted access.
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AI-assisted synthesis by the TrendWatcher Editorial Desk · sourced from 2 outlets · Aug 4, 2026 · How we report
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