Anthropic "Myth" model unexpectedly leaked: encryption and DeFi security situation suddenly becomes more tense

Title

Anthropic accidentally leaked the “Myth” model (internal code name Capybara), which reportedly surpasses Opus in multiple tasks. The cryptocurrency security community is starting to seriously discuss what this means.

Summary

• A tweet from AI commentator Xiao Hu (@xiaohu) brought Anthropic’s internal testing model “Myth” to the public’s attention. The leaked cached data contains draft materials on coding, academic reasoning, and cybersecurity performance.
• Anthropic confirmed the model’s existence and stated that it is undergoing early testing with a small number of clients. It is widely believed to be significantly stronger than Opus, with even executives from other labs taking notice.
• For the cryptocurrency sector, this is a double-edged sword: on one hand, it can strengthen smart contract audits, while on the other, it can enable faster identification and exploitation of on-chain vulnerabilities.
• Bottom line assessment: Both offense and defense will accelerate, raising the pace and thresholds of security confrontations.

Analysis

According to CoinDesk reports from March 27-28, 2026:

• The leaked materials indicate that “Myth” is significantly stronger than Opus in multiple benchmark tests (including cybersecurity tasks), suggesting that the competition between Anthropic and OpenAI has entered a new phase. Xiao Hu joked that Altman “can’t sleep,” reflecting the industry’s general sense of escalating competition.
• This aligns with the consistent pattern of AI expansion: larger models often unlock new scenarios such as “agent-based applications” and “automated vulnerability discovery.” However, the draft documents directly mentioned “unprecedented cybersecurity risks,” highlighting the evident tension between offense and defense.
• Decentralized AI projects (like Bittensor) face a long-term issue: centralized labs continuously raise the performance curve, and if they can’t keep up with iterations, narrative premiums and marginal relevance will be compressed.
• The leak itself is a fundamental operational oversight. This once again reminds us: security shortfalls primarily stem from humans, not the models themselves.

Impact Assessment

• Importance: High
• Category: Model Progress / Security / Research

Key Points

• Performance thresholds have been raised: Surpassing Opus in coding and security tasks, cutting-edge capabilities have once again been pushed higher.
• Accelerated offense and defense: Stronger auditing tools are available, but attack vectors have also been compressed.
• Operational lessons: A single unfortified cache led to the leak, proving once again that human error is the greatest risk source.
• Funding repricing: Decentralized AI tokens (such as Bittensor’s TAO) may face pressure; security-related projects have a relative advantage.
• Public opinion spillover: Xiao Hu’s transmission amplified the impact of this event in the Chinese community, potentially triggering policy discussions.

Background Context

• Dissemination path: Xiao Hu’s tweet → External attention → Anthropic confirms internal testing → Media follow-up (CoinDesk).
• Capability enhancement dimensions: Coding, academic reasoning, cybersecurity.
• Intuitive risks: The thresholds for automated vulnerability discovery, exploitation, and horizontal expansion are all decreasing.

Potentially Affected Groups

• Developers and security teams:
  • Benefits: Stronger static/dynamic analysis tools available.
  • Drawbacks: The cycle from zero-day discovery to exploitation has been compressed, and the audit window is narrower.
• Traders and funders:
  • Benefits: Increased focus on the security sector may lead to periodic capital inflows.
  • Drawbacks: The decentralized AI narrative may be suppressed by “performance gaps,” putting pressure on related tokens.
• Ecosystem and infrastructure parties:
  • Priorities: Vulnerability bounties, real-time monitoring, post-incident rollback, and insurance mechanism configurations.

Tags

AI Models, Competition, Security, Research

Conclusion: In response to this news, security teams must act immediately, traders can consider gaming the relative returns in the security sector, and long-term holders and funds need to adjust their security budgets and monitor the pace of offense and defense. Projects that miss the current reinforcement window will find themselves in a passive position.