Ethereum co-founder Vitalik Buterin has outlined a comprehensive new framework for enhancing security in the cryptocurrency space, arguing that perfect security is impossible but that practical strategies can significantly protect users. The core of his proposal is to minimize the gap between a user's intent and the actual behavior of a system.
Buterin's insights, shared in a detailed post, arrive amidst ongoing challenges for crypto platforms, including wallet hacks, smart contract exploits, and complex privacy risks. He reframes security as an effort to specifically target "tail-risk" scenarios where adversarial behavior could lead to severe consequences, rather than aiming for an unattainable perfect state. "Perfect security is impossible—not because machines are flawed, or because humans designing them are flawed, but because the user’s intent is fundamentally an extremely complex object," Buterin wrote.
He explains that even a simple action like sending 1 ETH involves assumptions about identity, blockchain forks, and common-sense knowledge that cannot be fully encoded. More complex objectives, such as preserving privacy, add further layers of difficulty in distinguishing between trivial and catastrophic losses.
The proposed solution centers on redundancy and multi-angle verification. Buterin advocates for systems where users specify their intent through multiple overlapping methods, with action only taken when all specifications align. This approach can be applied across Ethereum wallets, operating systems, formal verification, and hardware security. Practical examples include:
Transaction simulations: Allowing users to preview the on-chain consequences of an action before confirming it.
Post-assertions: Requiring both the action and the expected outcome to match.
Multisig wallets and social recovery: Distributing authority to prevent single-point failures.
Formal verification: Adding mathematical property checks to ensure code behaves as intended.
Buterin also envisions a role for large language models (LLMs) as a complementary tool, describing them as "a simulation of intent." He suggests that generic or user-fine-tuned LLMs could help detect what is normal or unusual for an individual, but cautions that they should never be the sole determiner of intent. Integrating AI with traditional redundancy methods could enhance mismatch detection without creating new single points of failure.
Critically, Buterin emphasizes that this security framework must balance protection with usability. Low-risk tasks should remain easy or automated, while high-risk actions—like transfers to new addresses or unusually large sums—should require additional verification. This calibrated, human-centered approach aims to safeguard users and strengthen trust in decentralized systems without introducing unnecessary friction.
