{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_4yc8jbkf9m7k","handle":"iflow-mcp-cybersecurityup-offensive-mcp-ai","url":"https://wellknown.network/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai","links":{"self":"https://wellknown.network/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai/record.json","html":"https://wellknown.network/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai","markdown":"https://wellknown.network/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai/record.md","api":"https://wellknown.network/api/v1/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai","status":"https://wellknown.network/api/v1/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai/status","claim":"https://wellknown.network/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai/claim.json","badge":"https://wellknown.network/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai/history","tools":"https://wellknown.network/api/v1/agents/iflow-mcp-cybersecurityup-offensive-mcp-ai/tools"},"ard":{"identifier":"urn:air::server:iflow-mcp-cybersecurityup-offensive-mcp-ai","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"iflow-mcp_cybersecurityup-offensive-mcp-ai","summary":"Offensive MCP AI - Red Team Tools for MCP","description":"# Offensive-MCP-AI\n\n## 🔮 Future Work Using MCP and AI\n\n1. **Autonomous Red Team Agents**  \n   Build LLM-driven agents that autonomously conduct reconnaissance, payload generation, exploitation and reporting, all orchestrated via MCP tools.\n\n2. **AI-Powered SOC Analyst**  \n   Integrate Wazuh + Suricata + Zeek logs and use MCP to let Claude analyze incidents, detect lateral movement, and recommend response actions in real-time.\n\n3. **Malware Dev Studio (LLM + MCP)**  \n   Use Claude + MCP to automate shellcode generation, obfuscation, sandbox evasion, and EDR bypass strategies through tools like Capstone, Donut, and Sliver.\n\n4. **Threat Hunting Automation**  \n   Develop proactive AI workflows that analyze logs, correlate indicators, and hunt based on threat intelligence feeds via MCP `resources` and `tools`.\n\n5. **Agent-Based Purple Team Simulator**  \n   Combine MCP with ATT&CK simulations, where Claude orchestrates both Red and Blue side techniques (Atomic Red Team, Caldera, Sigma/YARA rule generation).\n\n6. **CI/CD + DevSecOps Integration**  \n   Use MCP to review code pushed to GitHub, scan secrets, trigger security tools (Trufflehog, Gitleaks), and send secure alerts or PR recommendations.\n\n7. **Auto Incident Report Generator**  \n   Claude consumes logs and tool outputs via MCP and generates full incident reports (including diagrams and mitigations) in Markdown or PDF formats.\n\n8. **Cybersecurity Tutor / Trainer Mode**  \n   Claude explains what each tool does, simulates attacks in safe lab environments, and evaluates user responses via MCP simulation tools.\n\n---\n\n## 🔗 Installation & Integration Links\n\n### ✅ Install MCP CLI and SDK (Python)\n```bash\npip install modelcontextprotocol\n```\n\nDocs:  \n🔗 https://modelcontextprotocol.io/quickstart/server  \nGitHub:  \n🔗 https://github.com/jlowin/fastmcp  \n\n---\n\n### 🧠 Claude Desktop Configuration (Mac, Linux, Windows)\n\n1. Install Claude for Desktop  \n🔗 https://www.anthropic.com/index/claude-desktop\n\n2. Edit config file:\n\n##…","publisher":null,"homepage":"https://github.com/cybersecurityup/offensive-mcp-ai","repository":"https://github.com/cybersecurityup/offensive-mcp-ai","version":"0.1.1","license":"MIT","protocols":["mcp"],"tags":["mcp"],"pricing":null,"endpoints":[{"url":"pypi:iflow-mcp_cybersecurityup-offensive-mcp-ai","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","license":"pypi","repoUrl":"pypi","summary":"pypi","version":"pypi","description":"pypi","homepageUrl":"pypi"}},"derived":{"capabilities":[{"slug":"dev.ci-cd","name":"CI/CD & Deploy","confidence":1,"provenance":"derived"},{"slug":"automation.workflows","name":"Workflow Automation","confidence":0.814,"provenance":"derived"}],"categories":["automation","dev"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"iflow-mcp_cybersecurityup-offensive-mcp-ai","registry":"pypi","observedAt":"2026-10-28T04:28:14.463Z","publishedAt":"2026-02-07T10:03:52.665860Z","latestVersion":"0.1.1"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"iflow-mcp_cybersecurityup-offensive-mcp-ai","url":"https://pypi.org/project/iflow-mcp_cybersecurityup-offensive-mcp-ai/","firstSeenAt":"2026-09-09T18:21:36.841Z","fetchedAt":"2026-10-28T04:26:11.716Z","normalizedAt":"2026-10-28T04:26:11.716Z"}]},"firstSeenAt":"2026-09-09T18:21:36.841Z","updatedAt":"2026-10-28T04:28:14.463Z"}