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English(EN) Stop Trusting AI Audits Blindly: Adding Cryptographic Provenance to Cursor & Claude via MCP

ProofCore为TON区块链上的AI输出添加加密溯源

ProofCore推出了一套新的系统,为AI生成的输出提供加密溯源,以解决AI驱动任务(如代码审计)中的安全问题。ProofCore MCP服务器利用模型上下文协议(MCP),允许AI代理在The Open Network(TON)区块链上对其发现进行公证。这种零存储架构对输出进行哈希处理,对其进行签名,并将根哈希锚定到区块链上,从而实现可验证的工件,这些工件可以由其他AI代理或人类独立审计。 AI

影响 增强了AI生成输出的信任度和可验证性,这对于代码审计和智能合约分析等应用至关重要。

排序理由 第三方供应商发布新产品,集成了现有的AI模型和平台。

在 dev.to — MCP tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

ProofCore为TON区块链上的AI输出添加加密溯源

本文如何被排名

Signal score
54 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
第三方供应商发布新产品,集成了现有的AI模型和平台。
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, infra
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准

报道来源 [1]

  1. dev.to — MCP tag TIER_1 English(EN) · ProofCore Protocol ·

    停止盲目信任AI审计:通过MCP为Cursor和Claude添加加密溯源

    <p>AI agents are now writing our code, drafting legal agreements, and auditing smart contracts. But this introduces a massive security flaw: <strong>How do you mathematically prove that a specific AI generated a specific verdict at a specific time?</strong> </p> <p>If a Cursor ag…