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arXiv · 2601.07019

Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework

Harshil Parmar·Pushti Vyas·Prayers Khristi·Priyank Panchal
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Abstract

As vulnerability research increasingly adopts generative AI, a critical reliance on opaque model outputs has emerged, creating a "trust gap" in security automation. We address this by introducing Zer0n, a framework that anchors the reasoning capabilities of Large Language Models (LLMs) to the immutable audit trails of blockchain technology. Specifically, we integrate Gemini 2.0 Pro for logic-based vulnerability detection with the Avalanche C-Chain for tamper-evident artifact logging. Unlike fully decentralized solutions that suffer from high latency, Zer0n employs a hybrid architecture: execution remains off-chain for performance, while integrity proofs are finalized on-chain. Our evaluation on a dataset of 500 endpoints reveals that this approach achieves 80% detection accuracy with only a marginal 22.9% overhead, effectively demonstrating that decentralized integrity can coexist with high-speed security workflows.

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Harshil Parmar, Pushti Vyas, Prayers Khristi, Priyank Panchal. 2026-01-11. Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework. https://arxiv.org/abs/2601.07019

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