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October 9, 2026
Key Takeaways:
- Vitalik Buterin states that the rise of today’s cryptography is outpaced by AI-accelerated mathematics.
- In addition to quantum-robust schemes, he is especially wary of lattice-based systems and ECDSA.
- Buterin favors hash-based designs where possible and says fresh addresses can reduce exposure.
Buterin, co-founder of Ethereum, is concerned about the potential cryptographic threats posed by the speedy development of AI-based mathematics. He’s not suggesting an immediate push to move all money off of systems relying on mathematical facts as security, but it’s a beginning.
I don’t recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography.
The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices.
(and it’s also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the “fresh address” recommendation)
So far most people have been in the mode of thinking “elliptic curves broken, hashes safe, lattices safe”. But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math.
The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover – but bots soon will be?
This is a major part of the reason why for the past year ethereum’s lean roadmap has been going in the “hash-only” direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-.
For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* – and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption.
And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable “structure” – either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10.
To me that’s a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a “naive factoring -> GNFS” level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety.
And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all.
Theoretically, of course it’s possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it’s much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems.
For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size).
Concrete TLDR, my own personal views:
* Hash-based > lattice-based, in those situations where hash-based is possible at all
* For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs …
* For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism.
* If it’s not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it’s easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**.
* For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig “gracefully degrades” to a 1-of-1 where the 1 is whoever was gathering the signatures – a much better place to be than “anyone can take the money”
https://t.co/oVjwZog2lL— vitalik.eth (@VitalikButerin) October 7, 2026
AI Could Expose Weaknesses in ECDSA and Lattice Cryptography
However, Buterin is not only worried about quantum computing. AI systems could advance mathematical discovery and find new ways of attacking currently unbroken structures, such as elliptic curves and lattice-based cryptography, he says.
ECDSA is commonly used to secure crypto wallets and based on the difficulty of the elliptic curve discrete logarithm problem. However, Buterin says that future fast-evolving artificial intelligence systems may be able to see through the mathematics and discover a shortcut.
He also makes this remark about lattices like ML-DSA, LWE, and RLWE, as well as about various others.
Read More: Ethereum’s 2030 Shift: Vitalik Maps a Cryptographic World Computer Beyond Blockchain
Ethereum’s Lean Roadmap Favors Hash-Based Cryptography
This risk is one of the many reasons the lean roadmap of Ethereum has more and more chosen hash-based cryptography, Buterin said. He mentioned that the designs that he used as examples did not use a lot of lattices but were designed as WOTS or SPHINCS style constructions for signatures.
Dominick has posted this on his Facebook page to explain why he thinks that the hash functions give up less for the sake of being exploitable but elliptic curves and lattices give up more because of their properties.
That doesn’t mean hashes can’t be broken, but Buterin considers them to be less likely to reveal an important design flaw.
Fresh Addresses Could Reduce Public-Key Exposure
Buterin also advised you a useful idea for any crypto asset holders. Storing money in addresses that haven’t received a transaction to them will also help to lower exposure if it is easy to do so safely: the public key isn’t onchain yet.
It is a concern if the ECDSA was to be compromised at any time as some public keys might be easier to compromise. He cautioned, however, against hasty migrations.
If there are operational errors during wallet transfers, then it can be even more dangerous than the annotated theoretical crypto risk, Buterin said.
Multisig and Privacy Designs May Need Changes
Additionally, Buterin mentioned multisig wallets and privacy protocols. If they require multisig, he prefers to gather the confirmations offchain if possible, otherwise they are never unnecessarily exposed.
He cautioned against storing encrypted notes onchain if an offchain method can get the job done when it comes to privacy systems.
The overall message is that crypto security not only needs to plan for quantum computers but for AI systems that can vastly outpace existing cryptanalysis and carry out mathematical research in unprecedented fashion.
Read More: Robinhood Launches AI-Native Chain, Stock Tokens in 120+ Countries and 7% Crypto Yield
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