What to know:
- Anthropic’s public Mythos-class model Fable 5 redirects risky queries to Claude Opus 4.8, which previously helped find a Zcash vulnerability.
- The model can improve smart contract audits but may also make it easier to exploit unaudited protocols, risking liquidity pools, bridges, and governance systems.
- Industry calls for safeguards, user support, and AI-blockchain security collaboration, as advanced models could outpace current audit standards.
Anthropic has launched Claude Fable 5, a Mythos-class AI model that will be available to the public for the first time. This has sparked a lively discussion on AI, blockchain security, and decentralized finance. The unveiling highlights both the transformative potential of AI for auditing and the jeopardy of rapid discovery of crypto vulnerabilities.
Mythos-Class Model Introduces Safety Routing
Fable 5 is equipped with an automatic safety routing feature that reduces the risk of unauthorized or unsafe usage. For instance, requests related to cyberattacks, biological or chemical weapons, and model distillation are transferred to Claude Opus 4.8. Opus is the tool researchers utilized to locate a significant vulnerability in the Zcash Orchard protocol, thereby illustrating how cutting-edge AI can be instrumental in ensuring the security of a privacy coin, provided that it has the right controls in place.
Source: Aragon Research
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Implications for Smart Contract and DeFi Auditing
The updated model reveals that Web3 developers face a paradoxical scenario. While AI-powered tools can be used to polish smart contract audits, enhance the productivity of code reviewing, and fuel the development of decentralized finance infrastructures. But the very same features might make it easier for people to find vulnerabilities in protocols that haven’t been audited. This might cause issues with liquidity pools, cross-chain bridges, and governance mechanisms across different blockchain ecosystems without audited protocols.
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Balancing Innovation With Risk Management
The parties in the industry point out that it is paramount that the scale should help; on the contrary, that deployment of such AI should responsibly be one of the pillars that lead to reducing and even eliminating the problems and risks due to the use of AI tools.
One or more of these actions should be taken: inclusion of automated house rules; provision of assistance to the users; and establishment of collaboration at the level of AI labs and blockchain security companies to be able to minimize the risk to the whole system. There are still matters of concern that highly-skilled and highly-advanced models could be beyond the current human audit levels.
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