Keyblind – encrypted secrets vault that hides API keys from AI agents
Keyblind is an encrypted secrets vault designed to protect API keys from AI agents. It ensures that secrets are resolved at runtime and never appear in conversation transcripts, addressing the common issue of developers accidentally leaking sensitive information. The tool supports multiple secret backends and is compatible with various AI tools that utilize the Model Context Protocol.
- ▪Keyblind keeps secrets encrypted at rest and resolves them at runtime, preventing plaintext values from being exposed.
- ▪Over 100,000 LLM conversations with exposed secrets were indexed by search engines in 2025.
- ▪Keyblind supports multiple secret backends, including 1Password and Bitwarden.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,800 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/aarifmms/keyblind |
| Publication time | Tue, 26 May 2026 22:47:33 +0000 |
| Retrieval time | 2026-05-26T22:57:54.715Z |
| Last seen | 2026-05-26T22:57:54.715Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 4SeqkG4aY4Qs |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Keyblind — Blind AI to Your Keys Encrypted secrets vault with MCP for AI agents. Secrets resolved at runtime, never leaked to LLM conversations. Why Developers regularly leak API keys, passwords, and tokens to AI coding tools. 100,000+ LLM conversations with exposed secrets were found indexed by search engines in 2025. AI agents read your .env files. They copy-paste secrets into conversations. They commit them accidentally. Keyblind stops this by keeping secrets encrypted at rest and resolving them at runtime — the plaintext value never touches the LLM transcript.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.