Turn security findings into bounded agent work: one recipe, scoped context, required proof, and a human-reviewed result.
- ✓Open-source license (Apache-2.0)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Documented (README)
claude mcp add security-recipes-ai -- uvx security-recipes.ai{
"mcpServers": {
"security-recipes-ai": {
"command": "uvx",
"args": ["security-recipes.ai"]
}
}
}Resumen de MCP Servers
<div align="center"> <img src=".github/readme/hero.svg" alt="security-recipes.ai — Search CVEs. Remediate vulnerabilities with AI agents. Sourced NVD + CISA KEV intelligence, evidence-gated canonical records, and bounded remediation plans." width="100%" /> [](https://security-recipes.ai/) [](https://security-recipes.ai/cve-database/) [](https://security-recipes.ai/mcp-servers/) [](https://security-recipes.ai/docs/security-health-action/) [](https://security-recipes.ai/llms.txt) </div> # security-recipes.ai **Search CVEs. Remediate vulnerabilities with AI agents.** Sourced facts stay sourced, remediation stays bounded, and every plan carries verification, rollback, and stop conditions — the live site's contract, and this repo's. [security-recipes.ai](https://security-recipes.ai/) is an Eleventy site for sourced CVE intelligence and evidence-gated vulnerability remediation that AI agents can consume without inheriting deployment or production authority. The project is intentionally narrow: - a complete rolling Medium/High/Critical CVE database, - evidence-qualified canonical CVE remediation records, - practical security remediation recipes, - prompt and rules-file examples, - agent setup guides, - MCP integration patterns, - an optional read-only MCP server for recipe search and approved upstream MCP context, - a [reusable GitHub Action](https://security-recipes.ai/docs/security-health-action/) that turns this guidance into toggleable CI health checks. It is not a scanner, ticketing system, SOAR platform, deployment tool, or custom security toolkit. Existing security tools should produce the findings; this site helps agents use the right remediation context and stop at the right time. Start with the live [CVE Database](https://security-recipes.ai/cve-database/) for an exact vulnerability or the [AI Vulnerability Remediation Playbooks](https://security-recipes.ai/security-remediation/) for the evidence-to-patch workflow. Agent-specific guides cover [Codex](https://security-recipes.ai/codex/), [Claude Code](https://security-recipes.ai/claude/), [Cursor](https://security-recipes.ai/cursor/), [GitHub Copilot](https://security-recipes.ai/github_copilot/), [Devin](https://security-recipes.ai/devin/), [Shiba Studio](https://security-recipes.ai/agents/#shiba-studio), [Hermes Desktop](https://security-recipes.ai/agents/#hermes-desktop), and [OpenClaw](https://security-recipes.ai/agents/#openclaw). The [Visual Guide](https://security-recipes.ai/how-to-use/) shows the complete path from source qualification and search discovery to a bounded plan, proof, rollback, and human review. For the distinct problem of securing an agent system's identities, tools, connectors, context, memory, runtime, and recovery controls, use [AI Agent Security](https://security-recipes.ai/agentic-security/). ## Current product and workflow  ### Qualified search discovery  The complete catalog remains searchable, while public canonical CVE pages stay limited to reviewed or evidence-qualified records. Those pages ship unique search metadata, server-rendered core facts and affected-version evidence, one remediation authority (stable reviewed guidance first, otherwise complete source-linked AI enrichment), a short approval-gated AI implementation prompt, canonical URLs, breadcrumbs, and `Article`/`TechArticle` structured data. The CVE database describes the catalog as a `Dataset`; the remediation pillar exposes its visible seven-step workflow as a `HowTo`. Year-partitioned CVE sitemaps contain only indexable canonical routes, and the build fails when sitemap parity, canonical ownership, crawl reachability, metadata limits, or same-origin links drift. Indexability is also withheld from mass-templated recipe children. The 72 development code-hygiene recipes and 39 generated compliance-framework recipes remain browsable from their canonical hubs with `noindex,follow` while they share a common method. A bounded rendered-body similarity gate prevents a child from re-entering sitemaps until its evidence, examples, and tests are materially distinct. The hubs remain indexable and carry the shared discovery context. After an SEO-bearing release, the public revision must match the merge commit before sitemap submission or URL inspection. The [Caddy deployment guide](README.caddy-deploy.md#search-discovery-after-an-seo-release) documents the DNS-verified Search Console handoff, priority live-URL checks, sitemap submission, indexing requests, and query monitoring. Submission is a discovery hint; it does not guarantee indexing or a particular ranking. The remediation pillar also records a public repository example for [CVE-2026-13149 in `brace-expansion`](https://security-recipes.ai/security-remediation/#real-repository-case-study-cve-2026-13149-in-brace-expansion). It ties the dependency-only change to the [reviewed pull request](https://github.com/stevologic/security-recipes.ai/pull/89), tests, advisory evidence, and recovery path while explicitly separating the same PR's unrelated Fail2Ban work. | CVE search to canonical record | CVE evidence to bounded agent plan | | --- | --- | |  |  | | Proof and human review | Read-only MCP context | |  |  | ## What this project is for AI coding agents can help close security findings when their work is bounded: one finding, one recipe, one reviewed output. security-recipes.ai helps teams answer: - Which recipe matches this finding? - Which prompt should the agent use? - Where do I put the instructions for Copilot, Claude, Cursor, Codex, or Devin? - Which MCP servers should the agent read for advisory, scanner, repository, or runbook context? - What should the PR or triage note include before a reviewer trusts it? ## What ships - Eleventy documentation site (fast static builds, no Go toolchain). - CVE-first observatory home page and data-first CVE database. - Recipe hubs for dependency, SAST, sensitive-data, base-image, CVE, and default-hardening remediation. - CVE intelligence intake policy, prompt, fixtures, and evaluator for routing advisory signals before an agent patches. - A complete rolling ten-year Medium/High/Critical CVE catalog composed from integrity-verified NVD JSON 2.0 feeds, CISA KEV metadata, and every applicable vetted remediation archetype. Only reviewed `stable` Markdown pages override that conservative baseline. - An integrity-hashed search allowlist that publishes canonical CVE pages only for reviewed stable Markdown or AI enrichment that passes the deterministic recipe-ready evidence contract. The full database remains searchable even when a record is not eligible for search indexing. - A versioned seven-phase agentic change contract for every catalog CVE: discover, assess, mitigate, remediate, verify, rollback, and triage. Each action declares likely file targets, mutation and approval boundaries, required evidence, outputs, and failure behavior without guessing a patch or fixed version. - A structured compliance library spanning 39 security, privacy, assurance, resilience, and software-supply-chain frameworks without reproducing licensed control text. Its framework hub is the search surface; templated child assessments remain `noindex,follow` until differentiated. - A 72-recipe code-hygiene library covering cross-language and ecosystem- specific audit, remediation, verification, and stop-condition workflows. Its development children remain `noindex,follow` while their bodies share a generated template. - Recipes with existing prompt collections preserved. - Agent setup guides for GitHub Copilot, Claude, Cursor, Codex, and Devin. - MCP integration guidance for public and organization-approved security data sources. - Optional read-only FastMCP server in `mcp_server.py` for recipe search, retrieval, and opt-in upstream MCP context. - Docker and Docker Compose configuration for local or droplet hosting. - Helper scripts for site maintenance, validation, imports, and deployment. ## Repository map | Path | Purpose | | --- | --- | | `content/` | Recipes, documentation, remediation guides, and agent setup pages. | | `eleventy.config.js` | Site build configuration (permalinks, feeds, tag pages). | | `_includes/` | Page layouts: docs chrome and the standalone home page. | | `lib/` | Build modules: shortcode ports, JSON feed builders, SEO head. | | `assets/` | Site CSS and JavaScript for the recipe browser, navigation, and helper tools. | | `static/` | Images, logos, schemas, and static assets. | | `static/api/cve-catalog/` | Complete sharded CVE catalog, year-partitioned machine index, compressed browser-search index, provenance manife
Lo que la gente pregunta sobre security-recipes.ai
¿Qué es stevologic/security-recipes.ai?
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stevologic/security-recipes.ai es mcp servers para el ecosistema de Claude AI. Turn security findings into bounded agent work: one recipe, scoped context, required proof, and a human-reviewed result. Tiene 1 estrellas en GitHub y su última actualización registrada es del 2026-08-27.
¿Cómo se instala security-recipes.ai?
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Puedes instalar security-recipes.ai clonando el repositorio (https://github.com/stevologic/security-recipes.ai) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar stevologic/security-recipes.ai?
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Nuestro agente de seguridad ha analizado stevologic/security-recipes.ai y le ha asignado un Trust Score de 87/100 (tier: Trusted). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene stevologic/security-recipes.ai?
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stevologic/security-recipes.ai es mantenido por stevologic. La última actividad registrada en GitHub es del 2026-08-27, con 2 issues abiertos.
¿Hay alternativas a security-recipes.ai?
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Sí. En ClaudeWave puedes explorar mcp servers similares en /categories/mcp, ordenados por popularidad o actividad reciente.
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