Ross ROSS = Recommend OSS · open-source software intelligence for agents

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

github.com/Armur-Ai/Pentest-Swarm-AI · Go · AGPL-3.0 (copyleft) 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
Armur-Ai/Pentest-Swarm-AImain75

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