dorsal/arxiv
View SchemaZer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework
| Authors | Harshil Parmar, Pushti Vyas, Prayers Khristi, Priyank Panchal |
|---|---|
| Categories | |
| ArXiv ID | 2601.07019vv1 |
| URL | https://arxiv.org/abs/2601.07019 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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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"date_created": "2026-02-17T05:53:08.662000Z",
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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.",
"arxiv_id": "2601.07019",
"authors": [
"Harshil Parmar",
"Pushti Vyas",
"Prayers Khristi",
"Priyank Panchal"
],
"categories": [
"cs.CR",
"cs.AI",
"cs.SE"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework",
"url": "https://arxiv.org/abs/2601.07019",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
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"variant": "snapshot-2026-01-17",
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