Aigently MCP Server

🆕 Catalog doubles to 12 stacks. Django, Rails, Go, iOS, and Android graduate to launch — alongside a brand-new AI / LLM Apps stack covering LangChain, LlamaIndex, Hugging Face transformers, vLLM, and Ollama. Plus EPSS exploit-probability scoring on every CVE. Read the launch post →

The open-source CVE catalog, pipeline, and MCP server behind aigent.ly. Every day, CI ingests fresh CVEs from six public threat sources, enriches them with AI-generated coding patterns, and commits ready-to-use security rules directly into this repo — formatted for Cursor, Claude Code, Windsurf, GitHub Copilot, and Cline.

12 stacks · 6 sources · daily CI · MCP-native · Apache 2.0 · 0 keys required to consume.

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How it works

CVE published  →  pipeline detects it  →  Claude generates safe-code patterns
    →  rule committed to this repo  →  your IDE enforces it while you type

AI coding assistants write production code fast. They don't know which CVEs landed last week, or how to write around them. Aigent.ly bridges that gap: it turns a live CVE feed into IDE rules that travel with your project, enforced at generation time — not discovered at audit time.

Why it exists

• AI assistants don't know which CVEs landed last week.
• SAST catches issues at audit time. Aigent.ly catches them at generation time.
• Free, open data. Private, paid product. The security boundary is by design.

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Quick start

No API keys needed to consume. CI commits fresh snapshots daily — point your IDE at the MCP server and you're done.

Use via MCP (recommended)

Add to your IDE's MCP config — works with Claude Code, Cursor, Windsurf, Copilot, and Cline:

{
  "mcpServers": {
    "aigently": {
      "command": "npx",
      "args": ["-y", "@aigently/mcp-server@latest"]
    }
  }
}

The MCP server reads static JSON from packages/catalog-data/ — no database, no API keys, no setup.

Available MCP tools

Tool	Description	Returns EPSS?
get_security_context	Detect your stack and return relevant rules and top CVEs	✅
compose_guardrail	Generate an IDE-ready rules file for your stack	–
search_threats	Full-text and faceted CVE search; ranks by KEV → severity → EPSS	✅
get_threat	Full CVE detail with AI-generated safe-code patterns	✅
detect_project_stack	Identify stack(s) from a file list	–
list_stacks	Enumerate all 12 supported stacks	–
get_manifest	Catalog version + counts	–

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🛡 What's covered

Stacks (12)

#	Stack	Ecosystem	Family	Status
1	Next.js	npm	owasp_web	live
2	Express / Node.js	npm	owasp_web	live
3	FastAPI / Python	PyPI	owasp_web	live
4	NestJS	npm	owasp_web	live
5	Nuxt	npm	owasp_web	live
6	React SPA	npm	owasp_web	live
7	Django	PyPI	owasp_web	🆕 NOW LIVE
8	Ruby on Rails	RubyGems	owasp_web	🆕 NOW LIVE
9	Go	Go	owasp_web	🆕 NOW LIVE
10	iOS / Swift	SwiftURL	owasp_web	🆕 NOW LIVE
11	Android / Kotlin	Maven	owasp_web	🆕 NOW LIVE
12	AI / LLM Apps	PyPI	owasp_llm	✨ NEW

To add a stack: open packages/mvp-catalog/src/stack-registry.ts, add a StackConfig entry (set family: "owasp_llm" for AI-application stacks; default is owasp_web), and open a PR.

Sources (6)

Source	Role	Auth	Rate limit (free)
NVD (NIST)	Authoritative CVE registry. Backfills CVSS scores and CWE IDs.	Optional API key	5 req/30s (50 with key)
CISA KEV	Actively-exploited CVEs. Sets isActivelyExploited as the hard prioritization signal.	None	Static feed
GHSA (GitHub)	Advisories across npm, PyPI, RubyGems, Maven, Go, Swift.	Bearer token	5,000 req/hr
OSV (Google)	Per-package vulnerabilities, scoped to packages your stacks declare.	None	No published limit
npm Audit	Direct package advisory scan per stack. Catches advisories not yet in OSV/GHSA.	None	No published limit
EPSS (FIRST.org)	🆕 Daily-updated exploit-probability score (0–1) per CVE.	None	1,000 req/min

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🚦 How threats are prioritized

Every threat in the catalog carries multiple ranking signals so the MCP layer can surface the CVEs that actually matter:

final_rank =  isActivelyExploited (CISA KEV)        ← ground truth: it's being exploited NOW
           +  severity (CVSS bucket)                 ← classic theoretical severity
           +  epssScore ≥ 0.5  (+1) / ≥ 0.9 (+2)    ← prediction: how likely 30-day exploitation
           +  family match (owasp_web | owasp_llm)   ← keep LLM threats from polluting web rankings
           +  intent overlap (auth/inject/csrf/rag)  ← what the developer is actually doing

EPSS is the difference between "CVSS 9.8 — patch in the next sprint" and "CVSS 9.8 with EPSS 0.94 — drop everything." We expose both.

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🧠 AI / LLM stack

The ai-llm stack ingests CVEs against the AI application toolchain and classifies them against the OWASP LLM Top 10 instead of the Web Top 10:

Watched packages: langchain, langchain-community, langchain-core, llama-index, llama-index-core, llama-cpp-python, transformers, huggingface_hub, vllm, gradio, ollama, anthropic, openai, pydantic-ai, crewai, autogen-agentchat, dspy-ai.

OWASP LLM mapping (excerpt — see pipeline/scripts/lib/normalise.ts):

OWASP LLM	Common CWEs	Example
LLM01 — Prompt Injection	CWE-20 / 77 / 78 / 94 / 1321	Untrusted retrieval context concatenated into the system prompt
LLM02 — Insecure Output / SSRF	CWE-200 / 918	Tool-calling agent fetches arbitrary internal URLs from a crafted prompt
LLM05 — Supply Chain	CWE-116 / 502	Untrusted model checkpoint deserialization
LLM06 — Sensitive Info / Authz	CWE-284 / 285	Embeddings store leaks training-data secrets

Threats from this stack flow through a dedicated LLM-aware prompt in amplify-threats.ts so guardrails are framed in LLM vocabulary (NEVER concatenate retrieved context into the system prompt without delimiters) rather than web vocabulary.

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Threat intelligence pipeline

Pipeline stages

Daily CI run (GitHub Actions, 06:00 UTC)

  Phase 0     CISA KEV map + stack registry preflight
  Phase 1–3   npm Audit + OSV + GHSA → raw advisories
  Phase 5     deduplicate (source-priority based)
  Phase 4a    EPSS exploit-probability enrichment    ← NEW (batched, 50 CVEs/req)
  Phase 4     NVD: backfill CVSS & CWE for low-confidence rows
  Phase 6–8   upsert threats + per-stack mapping + mitigation flags
  Phase 9     close sync log

  Amplify     Claude: 2–4 ALWAYS/NEVER patterns per CVE (family-aware prompt)
  Summarize   Claude: cluster CVEs into per-stack rule docs
  Synthesize  Claude: merge into pre-built guardrail blocks (patterns + deps)
  Export      write JSON snapshots to packages/catalog-data/
  Commit      auto-push catalog-data/ to this repo

Repository layout

Path	Contents
packages/catalog-data/	Live threat snapshots — JSON committed daily by CI
packages/mcp-server/	MCP server (@aigently/mcp-server) — exposes catalog to AI agents
packages/db/	Drizzle schema shared between the pipeline and the web app
packages/mvp-catalog/	Stack registry — add a stack entry here to onboard it
packages/api-client/	TypeScript client generated from the OpenAPI spec
pipeline/scripts/	sync, amplify, summarize, synthesize, export — the full pipeline
pipeline/scripts/lib/sources/	One file per data source (NVD, OSV, GHSA, npm-audit, CISA KEV, EPSS)
.github/workflows/sync-threats.yml	Daily CI: ingest CVEs → AI guardrails → commit

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Run the pipeline locally

git clone https://github.com/aelbuni/aigently-catalog
cd aigently-catalog
npm install

cp pipeline/.env.example pipeline/.env   # default DATABASE_URL matches docker-compose
npm run db:setup                         # start Postgres, migrate, seed

# pipeline/.env — keys you need only when running the pipeline yourself:
ANTHROPIC_API_KEY=...   # required for amplify, summarize, synthesize
GITHUB_TOKEN=...        # required for GHSA source
NVD_API_KEY=...         # optional — increases NVD rate limit 10×
# EPSS requires no key.

npm run sync:threats           # ingest CVEs from all six sources
npm run amplify:threats        # Claude: ALWAYS/NEVER patterns per CVE (family-aware)
npm run summarize:rules        # Claude: cluster into per-stack rule docs
npm run synthesize:guardrails  # Claude: pre-build guardrail blocks
npm run export:catalog         # write JSON to packages/catalog-data/

All scripts & environment variables

Scripts

Script	Purpose
npm run db:up	Start Postgres via Docker Compose
npm run db:setup	First-time setup: start Postgres + migrate + seed
npm run db:migrate	Apply Drizzle migrations
npm run db:seed	Full catalog seed
npm run db:seed:upsert	Non-destructive upsert
npm run sync:threats	Ingest CVEs from all six sources
npm run amplify:threats	AI-generate patterns for new threats
npm run summarize:rules	AI-cluster CVEs into rule summaries
npm run synthesize:guardrails	Pre-build per-stack guardrail blocks
npm run export:catalog	Export DB → packages/catalog-data/ JSON

Environment variables

Variable	Required	Purpose
DATABASE_URL	Always	Postgres connection string
ANTHROPIC_API_KEY	AI steps	Claude API access
GITHUB_TOKEN	Sync	GitHub advisory source (GHSA)
NVD_API_KEY	Optional	10× NVD rate limit
STACK_FILTER	Optional	Limit a sync run to one stack slug (e.g. ai-llm)
DRY_RUN	Optional	Enrich + dedup without writing to DB

Prerequisites

• Node.js 22+
• Docker (for local Postgres)
• Anthropic API key (AI pipeline steps only)

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🗺 Roadmap

Already shipped: 12 stacks · 6 sources · EPSS-ranked prioritization · family-aware (web + LLM) amplifier prompts.

Next on deck (PRs welcome):

• PoC / exploit-availability detection — GitHub + Exploit-DB + Nuclei templates
• Vendor advisories — MSRC + Red Hat + Ubuntu (for base-image / OS-level CVEs)
• STIX 2.1 export — for SIEM ingestion
• KEV / EPSS watchlist webhooks — alert when a CVE in your detected stack moves into KEV or above an EPSS threshold
• Spring Boot, Laravel, .NET stacks — open to contribution

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Contributing

PRs are welcome. The highest-value contributions are:

• New stacks — add to packages/mvp-catalog/src/stack-registry.ts (set family: "owasp_llm" for AI/LLM stacks)
• CVE curation — improve mustLines, ruleContext, or alwaysPin in packages/catalog-data/seed-master.json
• Pattern quality — open an issue if an ALWAYS/NEVER line is wrong or too generic
• New threat sources — add a module under pipeline/scripts/lib/sources/

See CONTRIBUTING.md for full guidelines.

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License

Apache 2.0 — threat data sourced from public domain (NVD, CISA KEV, GHSA, OSV, EPSS).

"We open-sourced everything the community needs — the data, the pipeline, the stack registry. The web app that runs aigent.ly is private. Because a security product should practice what it preaches."

Aigent.ly and the Aigent.ly logo are trademarks of Aigently, Inc.