lumoslab-innovation/openhunteraiLocal-first AI red team for web, API, and LLM application security. Attacker-style reasoning, evidence-backed findings, and skills for AI coding agents.
Your local AI red team.
Attacker-style reasoning for web, API, and LLM application security.
Project website · Quick start · Agent skill · Assessment model · Architecture · Docs · Star history
OpenHunterAI brings scope, scan activity, findings, and remediation into one local workspace. Start without an account, connect your model providers, and assess verified public applications you own or are authorized to test.
The public landing introduces the project; the operational workspace runs locally. See public versus local builds.
| Capability | What you get |
|---|---|
| Local workspace | No signup; projects and scan records in your local database |
| Explicit scope | Domain verification, deterministic plans, human approval gates |
| Multiple signal sources | Browser inspection, recon, ZAP, and a Nuclei adapter |
| AI-assisted investigation | OpenHack/Strix adapter boundaries; bring your own runtimes |
| Evidence & remediation | Sanitized findings, coverage gaps, fix guidance, manual retest |
| Portable workflow | A skill for Codex, Claude Code, Gemini CLI, Grok Build, and OMP |
Alpha. Integration coverage depends on configured runtimes. A healthy workspace or an empty report does not prove a target is secure.
OpenHunterAI keeps the useful core of the 48bf9036 technical requirements: assess externally observable behaviour from an attacker's perspective. Source code is not required. Every run stays constrained to a verified public target and an approved scope.
flowchart LR
S["1. Lock scope"] --> M["2. Map surface"]
M --> B["3. Establish baseline"]
B --> H["4. Test bounded hypotheses"]
H --> E["5. Gate evidence"]
E --> R["6. Report and retest"]
| Stage | Output | Control |
|---|---|---|
| Scope lock | Verified host, approved paths, scan plan | Authorization before work begins |
| Surface mapping | Browser and recon signals | Public, in-scope surface only |
| Baseline | Normal responses and coverage context | Bounded workers and adapter policies |
| Hypothesis testing | Candidate security signals | No destructive actions; sensitive validation needs approval |
| Evidence gate | Sanitized, reproducible finding | Separate observed signals from analyst judgement |
| Report and retest | Fix guidance, limitations, manual confirmation | A human owns impact decisions and closure |
OpenAPI/Swagger descriptions, Postman examples, sanitized HTTP captures, and dedicated test accounts can help define the agreed test surface. Automated import or retention of raw sensitive traffic is not a promised capability.
Requires Git, Node.js 22+, and Docker with Compose v2.
git clone https://github.com/LumosLab-Innovation/OpenHunterAI.git
cd OpenHunterAI
node ops/local.mjs start
Open localhost:3001. First startup builds the images,
applies database migrations, and creates a private .env.local with generated secrets.
.env.local; run node ops/local.mjs restart.| Command | Purpose |
|---|---|
node ops/local.mjs status | Inspect services |
node ops/local.mjs logs public-api | Diagnose API errors |
node ops/local.mjs stop | Stop; keep database volumes |
Included: browser inspection, ZAP, recon, and the Nuclei adapter.
Bring separately: OpenHack and Strix runtimes.
Optional interactive target-login capture: node ops/local.mjs start --with-browser.
Before scanning, resolve the Nuclei template-policy blocker. The adapter is packaged, but a reviewed template bundle is not yet supplied.
Local means the workspace runs on your machine, not permission to scan localhost/private networks. Keep its ports on loopback. Setup, model configuration, and troubleshooting →
Run in the application repository you want your agent to assess:
npx --yes skills add LumosLab-Innovation/OpenHunterAI --skill openhunter --agent codex claude-code gemini-cli grok --yes
Reload your coding agent, then ask:
Use OpenHunter to check my workspace and prepare a scoped security assessment. Wait for my approval before starting a scan.
OMP discovers the shared .agents/skills installation through its Agents
provider. The skill includes a read-only API helper; installation does not start
a scan. Client compatibility →
flowchart LR
UI["Local workspace<br/>React + Vite"] --> API["Public API<br/>Scope + scan plan"]
API --> BUS["NATS<br/>Orchestrator + workers"]
BUS --> EVIDENCE["Internal API<br/>Findings + reporting"]
EVIDENCE --> DB[("Postgres")]
API <--> DB
API -->|"Activity + reports"| UI
The public API builds the plan; Go workers execute it through bounded adapters. Signals pass through the findings policy before entering reports. Reporting is a backend service, not a separate scan worker.
Runtime & data flow · Worker mapping · Integration setup