We think that Clean is the future of circuit development, so we created a programmer-first introduction to show circuit developers who have never used formal verification how they can build circuits faster, safer and better. It's not scary, we promise.
You can find this on our new official Clean site!
Cryptographic software is error-prone and failures are catastrophic, therefore formal verification is a powerful and increasingly practical tool for greatly improving the assurances of our cryptographic software. But who verifies the formal verification? Formal verification itself is software too, what is proved, under which assumptions and its relation to the real-world implementation lie beyond the scope of the machine-checked proof. In this post, we want to give some insights into our three-week audit of the Ironwood formalization and some of the common pitfalls that formal verification more broadly can encounter.
In Session 11 of "Proof is in the Pudding," we look at what it takes to prove model inference. We cover transformer computation, sumcheck, GKR, lookup arguments, quantization, and KV caching, then discuss scaling and possible uses for verifiable ML.
This blog post looks at Fiat-Shamir from a practical security perspective: it explains why something that seems simple in theory can become surprisingly easy to get wrong. By using vulnerabilities found in real-world projects, we give developers and reviewers a better way to think about Fiat-Shamir when designing, implementing, and auditing proof systems.
We're diving into our new project called **clean**, aimed at creating an embedded DSL and formal verification framework for Zero Knowledge (ZK) circuits using Lean4. Imagine being able to not only define ZK circuits but also formally prove their correctness. Sounds like a game-changer, right? We'll walk you through our process of building a robust library of reusable, verified circuit gadgets, focusing on the importance of soundness and completeness. Plus, you'll get a peek at some cool examples like 8-bit addition and how we're tackling ZKVM design with techniques borrowed from Fibonacci sequences. It's exciting stuff, and if you're curious about how we're paving the way for bug-free ZK circuits, this is a read you won't want to miss!
Kevin Buzzard, a mathematician with a cautious view on human-checked proofs, found solace in interactive theorem provers, which verify mathematical proofs much like type-checking in programming. We explore how these tools, which are gaining traction in fields like applied cryptography, ensure rigorous and reliable proofs. With Lean as our focus, you'll discover how to dive into this fascinating world, see a proof in action, and learn how this technology is revolutionizing areas like zero-knowledge virtual machines. Curious about building rock-solid, machine-verified proofs? Check out our beginner-friendly guide!
We walk through a Lean 4 proof of correctness for a Clean model of circomlib's optimized Poseidon hash circuit at arity 1. The theorem says the modeled constraints are sound and complete with respect to the optimized Poseidon spec. After weeks of work, the only remaining `sorry` was a primality proof for the BN254 scalar field: a 254-bit number that no proof assistant can decide by trial division. Closing it requires a Pratt certificate, a recursive proof structure based on a theorem Lucas published in 1876.
Join our cofounder David Wong on the latest zk podcast as he dives into his compelling journey through cryptography, from his early days as a security consultant to his pivotal roles in major projects like Facebook's crypto initiatives and Mina. Get an insider's view on how we approach auditing in a Zero Knowledge context, the common pitfalls in ZK code, and how these insights shape our work. It's an engaging and informative chat for anyone fascinated by the world of cryptography and ZK technology!
In this part of our series, we start introducing the algebraic language needed to formalize sum-check as a tensor reduction. We start with the basics of rings and modules. Rings generalize fields by dropping the requirement that every non-zero element has a multiplicative inverse. Modules then generalize vector spaces by allowing scalars to come from a ring instead of a field. In this post, we’ll use plenty of examples to make these ideas concrete and build intuition along the way.
Ever wondered how zkVMs simplify the use of zero-knowledge proofs in coding? We dive into how they let developers focus more on application logic by abstracting complex cryptographic aspects, using familiar languages like Rust or C++. But hold on, it's not all smooth sailing. Despite these benefits, a single bug anywhere in the complex system of compilers, proof systems, or verification can lead to serious security issues. In the post, we break down the zkVM workflow, explore common vulnerabilities at each phase, and highlight the importance of understanding these layers to build more secure, zk-powered applications. Curious about how this all plays out? Let’s unravel it together!