
enterprise AI, data & security at @snowflake • 3x founder • co-founded @natomalabs (acq @snowflake), @atspoke (acq @okta), @appurify (acq @google) • @stanford
Joined January 2009
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Pratyus Patnaik retweeted
Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory).
AI starting to help us improve our understanding of the universe! openai.com/index/building-sc…
For the threat that will be disclosed tomorrow morning, how much of your environment is protected?
Threats are emerging faster than teams can patch, rotate credentials, and redeploy every affected system. How quickly can we turn knowledge of a new threat into tested, working defenses?
That's why MTTC - Mean Time to Coverage matters.
While remediation catches up, can you detect & block the attack path, or restrict exposure? Where do gaps remain?
You may not be able to remediate everything immediately. You should know what's covered - and what remains exposed.
Last week: PHP, LiteLLM, Railway, ConnectWise, GitLab, UpdraftPlus, Node.js, eMudhra..
Hope this week is kinder to security teams. Happy Monday.
Cybersecurity already has skyscraper-sized challenges.
But if AI cracks the cryptography securing the internet, we might need to stack every skyscraper on Earth just to draw the next bar on that chart.
Here's what's interesting.
Pretty much everything we trust online, from banking and cryptocurrencies to cloud infrastructure and digital identities, depends on mathematical problems that are incredibly hard to solve.
We've worried about quantum computers breaking that math for years. That's why we're moving toward Post-Quantum Cryptography (PQC), much of it built on lattices, another class of supposedly hard mathematical problems.
But what if AI finds shortcuts before quantum computers even get there?
Three recent developments caught my attention:
1\. Vitalik Buterin: What if AI delivers 50 years of mathematical progress in just 2 years? He's warning that even lattice-based cryptography could become significantly weaker, and advocates moving toward hash-based signatures where possible.
2\. Anthropic: Claude solved a 25-year-old mystery behind NIST's elliptic curves in under 30 minutes, using lattice mathematics. It didn't break cryptography, but showed how AI can accelerate mathematical discovery.
3\. eMudhra: Eight certificate-authority private keys were so weak that each had fewer than 256 possible values. Mozilla showed AI agents could recover them on their first attempt. Browsers rushed to block the certificates, but updating every legacy application and device is another story.
Two very different risks here. AI discovering new mathematical shortcuts, and AI exploiting cryptography already broken by bad implementation.
Maybe the immediate threat isn't AI figuring out how to break cryptography. It's AI figuring out just how much of our existing cryptography is already broken.
Vitalik: [lnkd.in/gKs8f9Kq](lnkd.in/gKs8f9Kq)
Anthropic: [lnkd.in/gV5jGf6R](lnkd.in/gV5jGf6R)
eMudhra: [lnkd.in/gShG6hR4](lnkd.in/gShG6hR4)
Pratyus Patnaik retweeted
private keys from 8 intermediate CA certs have been compromised (eMudhra)
Happy "Update everything" day to all my pager-carrying friends
emudhra.we-have-a-problem.co…
Pratyus Patnaik retweeted
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"
firefly.social/post/x/210783…
Personal takeaway from this week’s AI drama:
Maybe it’s time to at least stop using
--dangerously-skip-permissions.
Turns out AI agents need your permission… just like you need your wife’s. 😅
Some things are simply non-negotiable..
We're partnering with @huggingface to investigate an unprecedented security incident.
Cyber-capable OpenAI models compromised Hugging Face production during a benchmark evaluation.
Sharing preliminary findings to help defenders understand emerging risks:
openai.com/index/hugging-fac…
Pratyus Patnaik retweeted
It’s official…welcome to Snowflake, @natomalabs!
We’re bringing Natoma’s enterprise MCP platform for AI agents to Snowflake, making it easier to securely connect AI to tools like Slack, Jira, email, calendars and workflows through Snowflake Intelligence and Cortex Code.
More info ICYMI: bit.ly/4frxZiq
Pratyus Patnaik retweeted
We’re excited to share that @Snowflake has signed a definitive agreement to acquire Natoma!
Natoma was founded on a simple belief: AI agents will transform how work gets done inside enterprises, but only if organizations can securely connect, govern, and control how those agents access data, use tools, and take action.
Together with Snowflake, we’ll help enterprises bring together data, models, and control — enabling agentic AI that is trusted, connected, governed, and built for action.
Read more from our founders: natoma.ai/blog/natoma-snowfl…
Snowflake blog: snowflake.com/en/blog/snowfl…
Pratyus Patnaik retweeted
Invest and sign a definitive agreement in the same quarter? Well, this is a new one!
Congrats to @pratyus and the @natomalabs team on entering into a definitive agreement with @Snowflake.
The demand for agentic AI infrastructure is moving fast.
natoma.ai/blog/natoma-snowfl…
Pratyus Patnaik retweeted
Today we announced our intent to acquire Natoma, the enterprise MCP platform.
Once closed, @Snowflake users will be able to enrich their Snowflake data with critical application context and take action directly from Snowflake Intelligence and Cortex Code.
One platform for every data and workflow need. Send emails, summarize Slack threads, check calendars, and open Jira tickets, all without leaving Snowflake.
Snowflake already governs your data. With Natoma, we'll govern AI actions too.
Pratyus Patnaik retweeted
The release candidate for MCP 2026-07-28 is out. The protocol is now stateless: no handshake, no session id, any request can hit any server instance. Plus extensions as first-class (MCP Apps, Tasks), auth hardening, and a proper deprecation policy so we don't have to do this again.
blog.modelcontextprotocol.io…
Pratyus Patnaik retweeted
200K MCP servers on public IPs. STDIO transport runs any OS command it gets.
Anthropic says it's by design. 9 of 11 registries accepted a malicious test package without review.
This is not a security failure. It is an inventory failure.
venturebeat.com/security/mcp…
Pratyus Patnaik retweeted
Three paths to connect agents to external systems:
- Direct API: agent writes HTTP in a sandbox. Fine for 1 agent <> 1 service. Breaks at scale.
- CLI: agent runs shell commands. Works in local envs with a filesystem. Can't reach mobile, web, or cloud.
- MCP: agent connects to a server over a standard protocol. Built for cloud-hosted production agents.
Mature integrations ship all three.
MCP is the one that compounds. One remote server reaches every compatible client (Claude, ChatGPT, Cursor, VS Code) across every deployment environment, and gets more capable as new protocol extensions land without you shipping anything new.
New blog: Building agents that reach production systems with MCP.
When should agents use direct APIs vs CLIs vs MCP? Plus patterns for building MCP servers, context-efficient clients and pairing MCP with skills.
claude.com/blog/building-age…
Pratyus Patnaik retweeted
Your AI agent needs real tools to be useful. Giving it access to everything is a security problem.
NVIDIA NemoClaw isolates compute. Natoma governs tool calls.
Zero service credentials inside the sandbox.
Pratyus Patnaik retweeted
Past 50 MCP tools, agent accuracy drops. By 200+, you lose 1 in 7 queries and token costs spike 10x.
Our engineer @KarnikShreyas built the fix: search-then-execute with hybrid retrieval. 97.5% recall at enterprise scale. Constant context cost.
Full write-up: natoma.ai/blog/the-invisible…



















