Most "AI pentest" tools are a chat box bolted onto a single scanner. CyberStrikeAI makes the opposite bet: the hard part of an engagement isn't running nmap, it's deciding what to run next and stitching scattered results into a coherent attack chain. So it wires 100+ existing security tools behind a planning agent and records the whole path as a replayable graph.
What Sets It Apart
- Multi-agent orchestration on the CloudWeGo Eino ADK, with deep, plan-execute, and supervisor modes — the same toolset can run as one reasoning agent or a supervised team, depending on how much autonomy you're willing to grant.
- 100+ tools exposed through a YAML recipe system plus native MCP (HTTP, stdio, SSE) and MCP federation — adding or swapping a scanner is configuration, not code, and external MCP servers plug in without forking.
- An attack-chain graph with risk scoring and step-by-step replay — a finding is not just a log line; you can trace exactly how access was reached and re-run it.
- Provider-agnostic, driving any OpenAI-compatible model (GPT, Claude, DeepSeek) — sensitive engagements can stay on a self-hosted or local model.
Who It's For
Great fit if you are a red teamer or security researcher who already lives in a CLI toolchain and wants an agent to absorb the orchestration, note-taking, and reporting overhead — particularly in labs, CTFs, or sanctioned engagements where reproducibility matters. Look elsewhere if you want a turnkey, one-click scanner, or cannot supply written authorization: the project bundles a lightweight C2 and WebShell management and is explicitly scoped to systems you are authorized to test. The human-in-the-loop approval modes and tool allowlists exist precisely because unsupervised autonomous exploitation is risky, and running it without permission is illegal.
