Armur-Ai/Pentest-Swarm-AI
Autonomous penetration testing using a swarm of AI agents. Orchestrates recon, classification, exploitation, and reporting specialists with ReAct reasoning — supports bug bounty, continuous monitoring, and CTF modes. Built with Go, Claude API, and 7+ native security tools. observed · 2026-08-28
Health v2 · maintenance only
75/100
- Activity 99
- Release rhythm 51
- Longevity 63
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 890
- days_rel: 118
- days_push: 7
- n_releases_24m: 1
Adoption not part of the score
2381 stars · 446 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
An open-source autonomous penetration testing application that orchestrates a swarm of AI agents (recon, classification, exploitation, reporting) using ReAct reasoning and a shared stigmergic blackboard. Built in Go, it supports bug bounty, continuous monitoring, and CTF modes and works with Claude, OpenAI-compatible APIs, or fully local models via Ollama/LM Studio.
Use cases
- automate penetration testing with ai agents
- run autonomous recon and exploitation on a target
- find and verify vulnerabilities for bug bounty hunting
- continuous security monitoring of my attack surface
- practice ctf challenges with an ai agent
- run pentesting fully locally with ollama
- generate pentest reports with evidence automatically
When to choose
- you want autonomous, evidence-backed pentesting rather than passive scanning
- you need parallel coverage of large attack surfaces like thousands of subdomains
- you want to use local or self-chosen LLMs as the reasoning engine
- you run bug bounty, CTF, or continuous monitoring engagements
When to avoid
- you need a mature, compliance-certified commercial pentest product
- you cannot legally authorize testing against your targets
- you require guaranteed accuracy — the project is alpha and LLM-driven findings need human review
- you need a lightweight passive scanner rather than an active exploitation harness
Facets
application · maturity experimental
agent-framework penetration-testing security llm-inference workflow-automation security penetration-testing artificial-intelligence go cli ai-swarm offensive-security bug-bounty ctf react-agents autonomous-pentesting stigmergic-blackboard ollama claude-api ai-agents linux macos docker
1 source
- readme: https://github.com/Armur-Ai/Pentest-Swarm-AI · fetched 2026-08-28 · dbe4b30acd8c
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Armur-Ai/Pentest-Swarm-AI | main | 75 |
For agents
markdown · JSON · MCP: product_card(name="Armur-Ai/Pentest-Swarm-AI")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem