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Keyblind – encrypted secrets vault that hides API keys from AI agents

Authors
aarifshaikhs
Source
Hacker News
Published
Collection
Software Intelligence
AI Agent SecuritySecrets ManagementMCPDeveloper ToolsCode Intelligence
Open GitHubMarkdown source

Summary

Keyblind is a local encrypted secrets vault for AI coding agents. It keeps real API keys out of model context by giving agents fake .env values and resolving secrets only when commands run.

Problem

  • AI coding tools can read .env files and send API keys, tokens, and passwords into LLM conversations or commits.
  • The excerpt cites 100,000+ LLM conversations with exposed secrets indexed by search engines in 2025.
  • Leaked secrets can lead to account takeover, cloud spend, data exposure, and compromised software delivery.

Approach

  • Keyblind stores secrets in a local SQLite vault encrypted with AES-256-GCM.
  • It derives encryption keys with PBKDF2 using 600K iterations, then wraps the key with a machine fingerprint.
  • An MCP server exposes 6 tools so agents can list or resolve secret names without seeing plaintext in the transcript.
  • keyblind sandbox replaces .env values with deterministic HMAC-SHA256 fakes per project and key name.
  • keyblind run -- <command> injects real secrets as environment variables at runtime.

Results

  • The excerpt reports no benchmark, user study, attack evaluation, or comparison against tools such as 1Password CLI, Bitwarden CLI, or dotenv vaults.
  • It claims support for any MCP-compatible editor, including Claude Code, Cursor, Windsurf, Copilot, Cline, and Zed.
  • It claims zero network access, zero telemetry, no cloud account, and a local vault stored under ~/.keyblind/ with 0700 permissions.
  • It claims multiple secret backends, including the built-in encrypted vault, 1Password, and Bitwarden.
  • Security-relevant numbers given are AES-256-GCM encryption, PBKDF2 with 600K iterations, 6 MCP tools, and 100,000+ exposed LLM conversations as the motivating incident scale.

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