{
  "annotation_id": "b907dfa0-d4b7-47e1-847b-b2ed94c49d5f",
  "date_created": "2026-02-17T05:53:08.662000Z",
  "date_modified": "2026-02-17T05:53:08.662000Z",
  "file_hash": "189db4cbee0cfaf7822ce07b4511be128efcb6ecf784da7842ca9bfe11defe01",
  "private": false,
  "record": {
    "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": {
    "execution_id": "7b966979-8bf7-46fe-b0c9-5dc74bf12d7d",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}