Automation & Ops offensive-securitysecurity-reconred-teamingmcp-serverlocal-agentvulnerability-researchdocker

T3MP3ST Security Harness

Turn a connected coding agent into an orchestrated workflow for authorized security testing.

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What does this agent do, and when should you use it?

T3MP3ST is a multi-agent offensive-security framework for authorized testing, research, and education. It connects an already authenticated local coding agent or an API provider through a browser War Room, CLI, HTTP API, and MCP server. Mission Control, the Target Model, the Arsenal, and eight operator roles organize missions; the README marks the reconnaissance engine, mission engine, and evidence/reporting flow as stable while describing much of the coordinated downstream chain as experimental. It covers tool-backed web reconnaissance, white-box source analysis, CTF work, coordinated disclosure, and scaffolding for cloud, mobile, and binary static detection. It is designed for local or self-hosted deployment, and the documented Docker service binds only to 127.0.0.1:3333 by default.

An operator connects Claude Code, Codex, Hermes, OpenCode, or Oh My Pi in the War Room Settings, describes an authorized target to Op Admiral, and starts a mission. Mission Control coordinates the Target Model and Arsenal; the README describes Recon as a tool-backed loop that drives nmap, DNS, HTTP, and fingerprinting, with built-in networked tools refusing public hosts outside mission scope as well as loopback and private addresses. The system produces mission status, findings, evidence, reports, and coordinated-disclosure drafts. npm run server starts the HTTP API, while node dist/mcp-server.js exposes security_recon to MCP-aware clients. npm run verify-claims recomputes the README's benchmark claims from committed JSON under bench/.

  1. A penetration-testing team with written authorization needs local War Room-driven asset discovery, service fingerprinting, and mission evidence for a web application assessment.
  2. A security researcher reviewing open-source robotics, OT, or embedded software needs an OSV novelty, live-PoC, refuter-panel, and CVSS workflow for coordinated-disclosure drafts.
  3. A CTF learner working in an isolated authorized challenge environment wants to run hint-free Cybench-style tasks through a connected local coding agent.
  4. An application-security engineer needs white-box ingestion and decomposed vulnerability analysis for Python, JavaScript, TypeScript, Go, Java, C, or C++ repositories.
  5. A self-hosting team wants to expose `security_recon` to an MCP-aware agent while retaining local agent authentication or switching to OpenAI, Anthropic, OpenRouter, or Venice providers.

What are this agent's strengths and limitations?

Pros
  • It can use an already authenticated local coding agent while also documenting OpenRouter, Venice, Anthropic, OpenAI, and local OpenAI-compatible model paths; the local-agent path does not require a new API key.
  • War Room, CLI, HTTP API, and MCP `security_recon` provide both an operator interface and integration surface for MCP-aware workflows.
  • The README documents `verify-claims` as recomputing published metrics from committed benchmark JSON and reports black-box and white-box XBEN results separately.
  • Default egress-scope containment rejects out-of-scope public hosts plus loopback and private addresses, and the documented Docker deployment listens only on the local loopback interface.
Limitations
  • This is an offensive-security tool for owned or explicitly authorized targets only; adopters remain responsible for legal scope and rules of engagement.
  • The README explicitly says end-to-end coordinated swarm exploitation is unbenchmarked and unreliable; several downstream operators and white-box source analysis remain experimental.
  • Cloud, mobile, and binary modules are primarily static-detection or interface scaffolding, without benchmarks for live cloud exploitation, mobile dynamic exploitation, or binary solving/pwn work.
  • Local operation depends on npm, shell access, and either a local authenticated agent or configured model provider; Docker deployment and update workflows add Docker or Git dependencies.

How do you install or deploy this agent?

Install and start the local War Room:
npm install
npm run server
Open http://127.0.0.1:3333/ui/ and connect a local agent in Settings. Alternatively, configure OPENROUTER_API_KEY, VENICE_API_KEY, ANTHROPIC_API_KEY, or OPENAI_API_KEY for a provider path. For offline use, run ollama serve && ollama pull llama3, set TEMPEST_LOCAL_BASE_URL=http://localhost:11434/api and TEMPEST_LOCAL_MODEL=llama3, then run npm run build and npx tempest. For Docker, run cp .env.example .env and then docker compose up -d.

How do you use this agent?

Connect a local agent in War Room Settings, describe an authorized target to Op Admiral, and launch the mission. Check the local service with curl http://localhost:3333/api/health; the README also documents curl http://localhost:3333/api/bounty/platforms as an API example. For MCP integration, run node dist/mcp-server.js and use its exposed security_recon capability. Run npm run verify-claims when you want to recompute the repository's published benchmark claims.

FAQ

Do I need to buy a new model API?
Not necessarily. The README documents connections to already authenticated Claude Code, Codex, Hermes, OpenCode, and Oh My Pi, alongside OpenRouter, Venice, Anthropic, OpenAI, and local-model options.
Can I point it at any public target?
No. The project is limited to authorized testing, research, and education; the README requires that you own the target or have explicit written permission.
Has the eight-operator swarm been proven to exploit targets reliably?
No. The README marks reconnaissance as stable but says end-to-end swarm exploitation is unbenchmarked and still unreliable; the public benchmarks are primarily single-agent ReAct-loop results.
Can it run without a cloud model?
Yes. The README documents Ollama and OpenAI-compatible servers such as LM Studio, vLLM, and llama.cpp; configure the local base URL and model, then select the local provider through `npx tempest`.

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