No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by antonpk1 · MCP Server · ★ 215
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gibber-mcp safe to install? View the security audit →
Tiny Cryptography MCP Server A Model Context Protocol server built with Express.js that provides cryptographic tools including key pair generation, shared secret derivation, and message encryption/decryption. Now available at: http://104.248.174.57/sse Powered by Stanford Javascript Crypto Library (SJCL) What is MCP? The Model Context Protocol (MCP) is an open standard that defines how AI models and tools communicate. It enables seamless interoperability between language models and external capabilities, allowing AI systems to use tools more effectively. MCP standardizes the way models request information and actions, making it easier to build complex AI applications with multiple components. Features Generate SJCL P-256 key pairs Derive shared secrets for secure communication Encrypt messages using SJCL AES-CCM Decrypt encrypted messages Server-sent events (SSE) for real-time communication Installation Environment Variables The server uses the following environment variables: : The port on which the server will run (default: 3006) Development Production API E
| Stars | 215 |
| Forks | 24 |
| Language | JavaScript |
| Category | MCP Server |
| Quality Score | 75.7468939569559/100 |
| Open Issues | 4 |
| Last Updated | 2025-03-03 |
| Created | 2025-03-02 |
| Platforms | mcp, node |
| Est. Tokens | ~3k |
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gibber-mcp is Tiny MCP server with cryptography tools, sufficient to establish end-to-end encryption between LLM agents. It is categorized as a MCP Server with 215 GitHub stars.
gibber-mcp is primarily written in JavaScript.
You can find installation instructions and usage details in the gibber-mcp GitHub repository at github.com/antonpk1/gibber-mcp. The project has 215 stars and 24 forks, indicating an active community.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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