by Fazmin · MCP Server · ★ 91
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
claude mcp add cve-intel -- "/Applications/CVE Intel.app/Contents/MacOS/cve-intel" mcp
docker run --rm -p 127.0.0.1:8000:8000 --env-file .env cve-intel
Taken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 91 |
| Forks | 5 |
| Language | Rust |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 65/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-23 |
| Created | 2026-10-18 |
| Platforms | mcp, rust |
| Est. Tokens | ~28k |
🛡️ CVE Intel Local security intelligence in native Rust — a Tauri desktop app, a one-click built-in small AI model (llama.cpp), an MCP server for any harness, a REST API, and a plain CLI. 31 tools, 25 data sources. Private on-device AI by default; OpenAI, Anthropic or Gemini are an opt-in setting.
These tools work well together with cve-intel for enhanced workflows:
Looking for a cve-intel alternative? If you're comparing cve-intel with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Hands-on code, labs, and reference material by Omar Santos for building, securing, and operating AI agents in
Agentic pentest tooling. Currently achieving 81% (KIMI K2.5) on XBOW's benchmark in full black-box. Completely
Tactical AI Workspace Monitor & EDR
Agentic pentest tooling. Currently achieving 81% (KIMI K2.5) on XBOW's benchmark in full black-box. Completely
A curated list of tools, papers, and datasets for applying AI to cybersecurity tasks. This list primarily focu
Local-first AI knowledge layer. Extract architecture, query from any AI tool via MCP. Private by architecture.
Explore other popular mcp server tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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.
Sources & who's responsible: