PurpleAILAB/Decepticon

Health v2 · maintenance only

Adoption not part of the score

6328 stars · 1214 forks observed · 2026-10-19

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Decepticon is an autonomous AI red-team hacking agent that uses LLMs (built on LangChain/LangGraph) to plan and execute context-aware offensive security engagements under enforced scope, rules of engagement, and an OPPLAN. It is open source and self-hostable via Docker, with an optional managed cloud control plane.

Use cases

• run autonomous red team engagements against authorized targets
• simulate realistic attacker behavior to test blue team detection
• discover logical and context-based vulnerabilities beyond checklist scanners
• orchestrate LLM-driven pentest agents with scope and OPSEC controls
• generate evidence-backed findings and reports for security assessments
• self-host an AI offensive security agent in Docker

When to choose

• you need autonomous, context-aware red teaming rather than static vulnerability scanning
• you want runtime-enforced scope, rules of engagement, and OPSEC for AI agents
• you prefer self-hosting with your own LLM provider keys (BYOK)
• you want evidence and transcripts from real attack chains like SQL injection to cross-tenant access

When to avoid

• you need a traditional compliance-oriented vulnerability scanner with static checklists
• you lack authorization to attack the target systems
• you want a fully free managed service without any LLM token costs
• you need a simple one-shot nmap-style scanning script

Facets

application · maturity active

agent-framework penetration-testing llm-inference security cli security penetration-testing artificial-intelligence large-language-models windows python self-hosted red-team autonomous-hacking langgraph pentesting offensive-security llm-agent cybersecurity ai-agents docker linux macos web-server

Member repositories

For agents

markdown · JSON · MCP: product_card(name="BitterSecurity/Decepticon")