CypherTalk podcast

Antonio Viggiano on how Monad is Using AI for Security

16/7/2026
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54:14
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Most of our guests come at security from the outside, as auditors and researchers. Antonio Viggiano sits on the other side of the table: he's a Senior Security Engineer at the Monad Foundation, working on protocol fuzzing for the chain's consensus and execution clients.

Monad's architecture makes that a genuinely different problem. Unlike Ethereum, with its many interchangeable clients, Monad has a single pair: a C++ execution client and a Rust consensus client, both built by Category Labs. That tight coupling buys optimisations, but it also means a bug that looks real in one client is often quietly mitigated by the other. Test one in isolation and you drown in false positives.

That's where Monad Bugfinder came from, and the origin story is the opposite of what most people assume. It didn't start as a bug finder at all. It started as a triager, because the team was being flooded with AI-generated reports: convincing write-ups, plausible proofs of concept, and hours of human time spent working out which ones were real. Bug hunters optimise for recall. When you're the one receiving the reports, you need precision. Antonio walks through the validation gates that made the difference, why AI has a higher false positive rate than humans, and why the best human reviewers still find twice as many bugs as the best AI systems.

Key Topics

AI-assisted vulnerability discovery and triage

Invariant testing and protocol fuzzing

In-house security teams at protocols

Chapters

00:00 Introduction

00:38 From Solidity Developer to Security Engineer

03:10 Founding Recon and Cloud Fuzzing

04:39 What Is Invariant Testing?

06:38 Fuzzing Smart Contracts vs Blockchain Clients

08:41 Tooling Maturity and System Complexity

09:47 Extracting Invariants at the Protocol Level

11:11 Why Most DeFi Teams Don't Know Their Properties

13:29 Monad's Architecture: Two Clients, Tightly Coupled

16:29 Why Single-Client Bugs Create False Positives

17:21 Monad Bugfinder: Why It Started as a Triager

20:19 Precision vs Recall: Finding Bugs vs Receiving Reports

23:54 Inside the Triage Process

25:59 Reconstructing Proofs of Concept End-to-End

27:13 Should Smaller Teams Build Their Own?

30:06 Build vs Buy, and the Attackers Building It Too

32:27 Validation Gates: EIP Support and Differential Testing

35:49 Testing Against Geth and Nethermind

37:43 Local Clusters and Controllable Validators

42:00 Do AI Reports Have More False Positives?

44:37 The Economics: AI Tokens vs Audit Firms

47:47 Why Humans Still Find Twice as Many Bugs

48:14 When Your Scaffolding Goes Stale

51:52 Offense vs Defense and the Human Weak Link

54:27 The Roadmap: Making the System Generic

56:51 UltraFuzz: Agentic Fuzzing for Solidity

1:00:32 Specifications, Spec Drift, and AI-Written Code

1:04:08 Will AI Ever Write Bug-Free Code?

1:09:45 Where to Find Antonio

Resources and Links

Antonio's X: https://x.com/aviggiano

Monad Bugfinder blog post: https://blog.monad.xyz/blog/monad-bugfinder

UltraFuzz blog post: https://www.monad.xyz/blog/ultrafuzz 

Monad: https://monad.xyz

Oak Security's Research: https://research.oaksecurity.io/

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