redamon

Unmask the hidden before the world does

RedAmon is an open-source, self-hosted AI penetration testing framework that maps your attack surface into a graph, autonomously exploits it from a Kali sandbox with human approval gates, and opens pull requests that fix what it finds. From first packet to merged patch, with human oversight at every critical step.

LEGAL DISCLAIMER: This tool is intended for authorized security testing, educational purposes, and research only. Never use this system to scan, probe, or attack any system you do not own or have explicit written permission to test. Unauthorized access is illegal and punishable by law. By using this tool, you accept full responsibility for your actions. Read Full Disclaimer

🏆 Flagship result: RedAmon solves 101 / 104 (97.1%) of the XBOW web-security benchmark fully black-box. Every solve ships a complete, unedited raw agent session and a reproducible, step-by-step walkthrough: open any row and read exactly how the flag fell, tool call by tool call. Auditable, line by line. See the XBOW Validation Benchmark scorecard.

📊 Coverage, measured and self-scored: the Bug Bounty Coverage — The Full Taxonomy Audit (PDF, 124 pages) audits RedAmon against all 397 classes of web bug-bounty finding, one line at a time, naming the exact module, skill or scanner behind every verdict, and scoring the recon pipeline and the agentic system separately against their own remit. It is deliberately self-critical, it records what is missing as plainly as what is covered: 134 classes complete, 210 in the build backlog, 44 deliberately excluded, with one explicit decision for every class.

Dynamic Multi-Tool Parallel Recon Pipeline

RedAmon launches multiple reconnaissance tools in parallel, each feeding results into a shared knowledge graph in real time. Tools spin up, adapt their scope based on live discoveries, and coordinate without manual intervention. The entire attack surface -- subdomains, ports, endpoints, parameters -- materializes in minutes, not hours.

Recon as a Living Knowledge Graph

Industry-standard scanners chained so each tool's output feeds the next, then merged into a single Neo4j knowledge graph. Findings are deduplicated, relationships are explicit, and the agent inherits a structured, fully connected attack surface ready to query in natural language.

Offense meets defense. One pipeline, full visibility.

Reconnaissance ➜ Exploitation ➜ Post-Exploitation ➜ AI Triage ➜ CodeFix Agent ➜ GitHub PR

RedAmon doesn't stop at finding vulnerabilities, it fixes them. The pipeline starts with a 6-phase reconnaissance engine that maps your target's entire attack surface, then hands control to an autonomous AI agent that validates CVE exploitability, tests credential policies, and maps lateral movement paths. Every finding is recorded in a Neo4j knowledge graph. When the offensive phase completes, CypherFix takes over: an AI triage agent correlates hundreds of findings, deduplicates them, and ranks them by exploitability. Then a CodeFix agent clones your repository, navigates the codebase with 11 code-aware tools, implements targeted fixes, and opens a GitHub pull request, ready for review and merge.

Roadmap & Community Contributions

We maintain a public Project Board with upcoming features open for community contributions. Pick a task and submit a PR!

Want to contribute? See CONTRIBUTING.md for how to get started.

Maintainers

Samuele Giampieri: Creator, Maintainer & AI Platform Architect AI Platform Architect & Full-Stack Lead with 15+ years of freelancing experience and more than 30 projects shipped to production, including enterprise-scale AI agentic systems. AWS-certified (DevOps Engineer, ML Specialty) and IBM-certified AI Engineer. Designs end-to-end ML solutions spanning deep learning, NLP, Computer Vision, and AI Agent systems with LangChain/LangGraph. LinkedIn · GitHub · Devergo Labs From the same creator: Pathbreak - Agentic Cloud Security See the attack path. Then break it. Pathbreak is an agentic cloud security platform that discovers and validates the real attack paths a threat actor would exploit across your cloud. It catches the control-plane chains most tools miss. Pathbreak - agentic cloud security pathbreak.io

Ritesh Gohil: Maintainer & Lead Security Researcher Cyber Security Engineer at Workday with over 7 years of experience in Web, API, Mobile, Network, and Cloud penetration testing. Published 11 CVEs in MITRE, with security acknowledgements from Google (4×) and Apple (6×). Secured 200+ web and mobile applications and contributed to Exploit Database, Google Hacking Database, and the AWS Community. Holds AWS Security Specialty, eWPTXv2, eCPPTv2, CRTP, and CEH certifications with expertise in red teaming, cloud security, CVE research, and security architecture review. LinkedIn · GitHub

Anthropic Cyber Verification Program: the team behind RedAmon is approved under Anthropic’s Cyber Verification Program.

Quick Start

Prerequisites

• Docker Engine 28.0.0 or newer & Docker Compose v2+ (check with docker version --format '{{.Server.Version}}')
  • macOS: Docker Desktop, with Memory raised to at least 4 GB (8 GB with --gvm) in Settings → Resources. Clone under ~/ so the path is inside the default File Sharing list.
  • Windows: Docker Desktop with the WSL2 backend, run from inside the WSL2 filesystem (~/), not /mnt/c/.

That’s it. No Node.js, Python, or security tools needed on your host.

Minimum System Requirements

RAM is checked at start. ./redamon.sh refuses to start when the memory Docker can use does not cover the minimum of every service the profile runs (the memory allocator’s per-service floors, see README.MEMORY_GOVERNOR.md). With the default reserves that is about 6.5 GB for the core stack and about 9.6 GB with OpenVAS. REDAMON_SKIP_RAM_GATE=1 skips the check.

Without OpenVAS runs 9 containers: webapp, postgres, neo4j, agent, kali-sandbox, recon-orchestrator, docker-broker (a filtering Docker-socket proxy that the recon orchestrator’s spawned scan containers go through, so they can only launch the known tool images), and the plugin platform’s plugin-runner and plugin-gateway. With OpenVAS adds 4 more runtime containers (gvmd, ospd-openvas, gvm-postgres, gvm-redis) plus ~8 one-shot data-init containers for vulnerability feeds (~170K+ NVTs). First launch takes ~30 minutes for GVM feed synchronization. Dynamic recon and scan containers are spawned on-demand during operations and require additional resources.

Disk sizing for a real deployment. The figures above are bare minimums to build and run the stack (fixed platform footprint: ~70 GB without OpenVAS, ~95 GB with - measured by true on-disk image size after deduplicating the shared Kali base layers). For an always-on server running real engagements, add ~100 GB of free space for operational data - the Neo4j attack graph, scan artifacts/outputs, Postgres, and container logs all grow with use. That means 200 GB without OpenVAS, 250 GB with OpenVAS, each leaving well over 100 GB free for operations. See the deployment guide for the full breakdown.

1. Clone & Install

git clone https://github.com/samugit83/redamon.git
cd redamon

# Without GVM (lighter, faster startup):
./redamon.sh install

# With GVM / OpenVAS (full stack, ~30 min first run):
./redamon.sh install --gvm

The script builds all images and starts the services.

2. Create Admin Account

At the end of the install (and on every ./redamon.sh up or ./redamon.sh update if no admin exists), you will be prompted in the terminal:

[WARN] No admin user found. Let's create one.

  Admin name: Your Name
  Admin email: [email protected]
  Admin password: ********
  Confirm password: ********

• Admin name – display name shown in the UI (e.g. Admin, your name, anything you want).
• Admin email – used to log in at http://localhost:3000/login.
• Admin password – minimum 12 characters.

After creation, open http://localhost:3000 and sign in with the email and password you just set.

No prompt appeared? On a heavy first boot (especially --gvm, or a small VM) the webapp can take a while to come up, so the automatic prompt may be skipped. Create the admin at any time with:

./redamon.sh create-admin

It waits for the webapp, then prompts for the same details. It is safe to re-run: reusing an existing admin’s email resets that password, a new email adds another admin.

What the admin can do:

• Switch between all users via the user dropdown in the header (including users without a password).
• Create new users (with or without a password) and assign them admin or standard roles.
• Set or change any user’s password.
• Delete users (except themselves).
• Access the Users management page from the header navigation.

Standard users can only log in (if they have a password set by an admin), change their own password, and use the app within their own scope. They cannot switch users, create users, or access user management.

If you forget the admin password, reset it from the terminal:

./redamon.sh reset-password   # reset an EXISTING user's password
./redamon.sh create-admin     # create the first admin, or reset an admin by re-entering its email

Use create-admin when no admin exists yet (it upserts on email); reset-password only updates a user that already exists.

3. Configure

Open http://localhost:3000/settings (gear icon in the header) to configure everything. No .env file is needed.

• LLM Providers – add API keys for OpenAI, Anthropic, OpenRouter, AWS Bedrock, or any OpenAI-compatible endpoint (Ollama, vLLM, Groq, etc.). Each provider can be tested before saving. The model selector in project settings dynamically fetches available models from configured providers.
• API Keys – Tavily, Shodan, SerpAPI, NVD, Vulners, URLScan, and threat intelligence keys (Censys, FOFA, OTX, Netlas, VirusTotal, ZoomEye, CriminalIP) to enable extended agent capabilities (web search, OSINT, CVE lookups, passive threat intel). Uncover multi-engine search keys (Quake, Hunter, PublicWWW, HunterHow, Google, Onyphe, Driftnet) expand target discovery across 13 search engines – shared keys (Shodan, Censys, FOFA, etc.) are automatically reused. Supports key rotation – configure multiple keys per tool with automatic round-robin rotation to avoid rate limits.
• Tunneling – configure ngrok or chisel for reverse shell tunneling. Changes apply immediately without container restarts.

All settings are stored per-user in the database. See the AI Model Providers wiki page for detailed setup instructions.

4. Open the Webapp

Go to http://localhost:3000 – create a project, configure your target, and start scanning.

For a detailed walkthrough of every feature, check the Wiki.

Prefer video? Watch the Product Demos playlist on YouTube for step-by-step tutorials.

Having issues? See the Troubleshooting guide or the Wiki Troubleshooting page.

Management Commands

All lifecycle management is handled by a single script:

Flags can be combined: ./redamon.sh install --gvm --kbase

Updating to a New Version

Just run:

./redamon.sh update

The script pulls the latest code from GitHub, detects which Dockerfiles and source files changed, rebuilds only the affected images, and restarts the updated services. Your databases, scan results, and reports are preserved – volumes are never deleted.

One-time note when a release adds a new background service. update re-execs the freshly-pulled script so new build/start rules apply automatically – but only for the script version you are updating from. When updating from a version that predates this self-heal (i.e. your first update onto it), run ./redamon.sh up once right after update so any newly added core service is started:

./redamon.sh update && ./redamon.sh up

up is idempotent – it starts only what is missing and leaves running containers untouched. After this one-time step, plain ./redamon.sh update handles everything on its own.

Upgrading across 6.9 (repository reorganization). 6.9 grouped the top-level directories under scanners/, services/, testing/, tooling/ and docs/. git pull moves only tracked files, so data that git does not track – the knowledge-base index, past scan outputs, and the single-host deploy .env and TLS material – would otherwise be left behind at the old paths. update (and up/status) migrate it for you automatically and print what moved. If any file was written by a container as root and could not be moved, finish it with:

sudo ./redamon.sh migrate-layout

The webapp also checks for updates automatically and shows a notification in the UI when a new version is available.

Deploy to a Server (Production)

The Quick Start above runs RedAmon locally on localhost. To run a shared, internet-reachable instance on a Linux server (EC2, DigitalOcean, Hetzner, or bare metal), use the single-host deploy in tooling/deploy/single-host/. It drives redamon.sh over SSH from your laptop and wraps the stack in the internet-facing security layer RedAmon omits by default: nginx + TLS (Let’s Encrypt), a host firewall, SSH hardening, and fail2ban, with a single public HTTPS origin so only the login page is reachable and everything else stays bound to loopback.

cd tooling/deploy/single-host
cp .env.example .env      # set HOST_IP, DOMAIN, SSH_KEY_PATH, OPERATOR_ALLOW_CIDRS,
                          # LETSENCRYPT_EMAIL, ADMIN_* ...
./deploy.sh init          # first build takes 30-60 min, then log in at https://<domain>/

Full walkthrough: Wiki: Deploying to a Server. Complete reference: tooling/deploy/single-host/README.md.

Development Mode

For contributors and active development with Next.js fast refresh:

./redamon.sh up dev           # auto-detects GVM mode from install

Tool images are built automatically on first run if they don’t exist yet. The dev override swaps the production webapp image for a dev container with your source code volume-mounted. Every file save triggers instant hot-reload in the browser.

When to Rebuild vs Restart

Rebuild a single service:

docker compose build <service>                    # Rebuild one image
docker compose up -d --no-deps <service>          # Restart only that service

Common dev commands:

docker compose ps                                 # Check service status
docker compose logs -f <service>                  # Follow logs for a service
docker compose down                               # Stop all (preserves volumes)
docker compose --profile tools down --rmi local   # Remove built images
docker compose --profile tools down --rmi local --volumes --remove-orphans  # Full cleanup

Reclaim disk space:

docker system df                                  # Show Docker disk usage (add -v for per-image breakdown)
docker image prune -f                             # Remove dangling images (auto-run by `./redamon.sh install` and `update`)
docker builder prune -f                           # Drop unused build cache (auto-run after every successful build; opt out with REDAMON_NO_AUTO_PRUNE=1)
docker builder prune -af                          # Drop ALL build cache, including what the current images reuse -- frees no extra disk and makes the next rebuild cold
docker container prune -f                         # Remove stopped containers

For a complete development reference – hot-reload rules, common commands, important rules, and AI-assisted coding guidelines – see the Developer Guide.

Knowledge Base (RAG-Enhanced Web Search)

The agent’s web_search tool includes a local Knowledge Base – a RAG pipeline that searches curated security datasets (GTFOBins, LOLBAS, OWASP WSTG, NVD CVEs, ExploitDB, Nuclei templates, and agent skill docs) before falling back to Tavily web search. When the KB returns a high-confidence match, Tavily is skipped entirely for faster, offline-capable results.

How it works: When the KB is enabled, install / up / update builds a lightweight KB index (~1,200 chunks in 10-15 min on CPU). At query time, the agent runs a hybrid retrieval pipeline (FAISS vector search + Neo4j fulltext), reranks with a cross-encoder, and checks a confidence threshold. If the score is high enough, results come from the local KB. Otherwise, it falls back to Tavily or merges both.

Default behavior: The KB is opt-in. ./redamon.sh install produces a lightweight install (~4.4 GB lighter, Tavily-only web search). To enable the local KB, pass --kbase:

./redamon.sh install --kbase

On first install with --kbase, RedAmon detects your hardware (GPU / CPU / API) and offers a quick-start profile. The choice is persisted, so subsequent update / up commands respect it without re-passing the flag.

Speed up ingestion with API embeddings: By default, embeddings run locally on CPU/GPU. On CPU-only machines, large datasets (ExploitDB, NVD) can take hours. You can offload embedding to an external API by creating a .env file from the template:

cp .env.example .env

Then configure the embedding API in .env:

Example with Ollama (free, local, no API key cost):

KB_EMBEDDING_USE_API=true
KB_EMBEDDING_API_BASE_URL=http://host.docker.internal:11434/v1
KB_EMBEDDING_API_KEY=ollama
KB_EMBEDDING_API_MODEL=nomic-embed-text

Important: Ingestion and query must use the same model. If you switch models, rebuild the index: make -C services/knowledge_base kb-rebuild-lite MODE=docker

Manage the KB:

./redamon.sh kb build lite          # Build with lite profile (~30-60s with API)
./redamon.sh kb build standard      # Add NVD CVEs
./redamon.sh kb update nvd          # Incremental NVD refresh
./redamon.sh kb stats               # Show index statistics
./redamon.sh kb rebuild lite        # Wipe and rebuild from scratch

For full technical documentation – query pipeline, data sources, ingestion profiles, scoring, security model – see the Knowledge Base Technical Reference or the Wiki: Knowledge Base & Web Search.

Table of Contents

• Full Wiki Documentation
• Overview
• Built for Enterprise, Secure by Design
• Security & Threat Model
• Feature Highlights
• System Architecture
• Components
• Documentation
• Troubleshooting
• RedAmon HackLab
• Community Showcase
• Legal

Overview

RedAmon is a modular, containerized penetration testing framework that chains automated reconnaissance, AI-driven exploitation, and graph-powered intelligence into a single, end-to-end offensive security pipeline. Every component runs inside Docker (no tools installed on your host) and communicates through well-defined APIs so each layer can evolve independently.

The platform is built around six pillars:

Built for Enterprise, Secure by Design

RedAmon is engineered to drop into a real security organization, not just a researcher’s laptop.

• Secure by design, STRIDE threat-modeled. The whole platform is assessed end to end under STRIDE and hardened in independently verified waves: code-level privilege separation, a filtering Docker broker in front of the host socket, fail-closed auth, SSRF egress guards, secret redaction, append-only audit trails, and a non-disableable guardrail that blocks government, military, and intergovernmental targets. See the Security Posture and Threat Model.
• Fully isolated and containerized. Every tool, scanner, and agent runs in its own container with no host-level execution. Scan jobs are spawned as ephemeral sibling containers with their own filesystem and network namespace, then torn down on completion, keeping the host clean and containing the blast radius of any exploit.
• Governed and multi-tenant. Multi-user / multi-project tenancy with admin and standard roles, DB-level user_id + project_id scoping on every node, Rules-of-Engagement enforcement, human-in-the-loop approval gates, and hundreds of per-project settings give teams the control an enterprise engagement requires.

Deploy to a server in one command. Beyond local localhost, RedAmon ships a hardened single-host deploy that stands up a shared, internet-reachable instance and wraps the stack in nginx + TLS (Let’s Encrypt), a host firewall, SSH hardening, and fail2ban, exposing a single public HTTPS origin:

cd tooling/deploy/single-host
cp .env.example .env      # set DOMAIN, HOST_IP, SSH_KEY_PATH, ADMIN_* ...
./deploy.sh init          # builds the full stack and brings it up over SSH

Full walkthrough: Wiki: Deploying to a Server.

Security & Threat Model

RedAmon is offensive software, so we hold it to the standard it tests others by. It is engineered secure by design and has been assessed end to end under the STRIDE methodology (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege), with findings remediated in sequenced, independently verified waves.

This is not a marketing claim. It is grounded in a published, evidence-based analysis you can read:

• Security Posture - the full defense-in-depth control catalog: every layer implemented, mapped to the code.
• Threat Model - the assets, trust boundaries, data flows, entry points, and network surface the analysis was built on.

Highlights of what is implemented:

• Code-level privilege separation. The target-facing worker is the least trusted component and holds no secrets; a filtering docker-broker stands between the orchestrator and the host Docker socket (image allowlist, denies --privileged, host-root binds, dangerous capabilities).
• Two deployment postures, one trust model. Local (backends bound to loopback) and a hardened public-internet deploy that adds nginx + TLS, an operator gate, login rate-limiting, and a single public origin.
• Fail-closed by default. Unset auth tokens reject rather than serve, default database credentials are refused and rotated, WebSockets require a signed ticket plus a server-side same-origin check, and every request is scoped to its authenticated owner (per-user access control with anti-enumeration 404s).
• Defense in depth. SSRF egress guards, secret redaction in logs, append-only audit trails, container-escape hardening, decompression and concurrency caps, and supply-chain pinning (sha256 + commit pins).
• A non-disableable hard guardrail blocks government, military, and intergovernmental targets regardless of configuration.
• Third-party plugins are contained, not trusted. The plugin platform installs only from an Ed25519-signed catalog, runs each plugin under its own UID in a runner with no internet and no database or LLM credentials, reaches the stack only through a two-route proxy that re-authorizes every call, and lets a plugin change the agent’s instructions only through text a person approves.
• Verified, release-gated remediation. Fixes ship in sequenced waves, each independently verified with live exploit reproduction before and after the patch. See the Changelog security entries.

Found a vulnerability? See our Security Policy for private disclosure.

Feature Highlights

Reconnaissance Pipeline

A fully automated, end-to-end external attack-surface mapper running inside a Kali Linux container. Give it one input (a root domain, a subdomain list, IP/CIDR ranges, or a Domain batch: a list of hostnames grouped by domain and scanned one group after another into a single graph) and the pipeline returns a complete, structured picture of the target: every subdomain, every live host, every open port, every HTTP service with its technology stack, every crawled endpoint and discovered parameter, every CVE the target is likely vulnerable to, plus dedicated scanners for GraphQL APIs, subdomain takeovers, hidden virtual hosts behind reverse proxies, web cache poisoning & deception, and an Adversarial AI Surface layer that tags AI runtimes / vector DBs / LLM frontends / proxies / MCP servers / chat / completion / embedding / RAG endpoints and prompt-injectable parameters using header / favicon / title / path / parameter-name signatures, then actively confirms them with a dedicated AI Surface Recon module (benign chat-shape probes, MCP handshake + tool-poisoning scan, vector-DB reads — no LLM calls).

Everything runs on a fan-out / fan-in architecture: each phase fires as many tools in parallel as the work allows, then converges before the next phase begins. 40+ industry tools integrate into one coordinated workflow, wildcard DNS poisoning is filtered out automatically with puredns, and stealth mode keeps the entire pipeline running on passive sources only when active probing is off-limits. Findings stream into the Neo4j knowledge graph on a background thread so the scan never blocks on database writes, and the raw JSON is preserved for download. The detailed tool-by-tool breakdown is in the matrix below.

Wiki: Running Reconnaissance | Technical: README.RECON.md

Recon Pipeline Tool Matrix

_{*Amass can run in active mode when configured. Knockpy performs active DNS probing.}

Partial Recon

Run any single tool from the pipeline independently without re-running the entire scan. Up to 12 partial recons can run in parallel per project, each with independent logs, stop controls, and status badges visible in both the Graph toolbar and Project Settings header. Each tool section has a play button that opens a modal where you can review existing graph data, add custom targets (subdomains, IPs, ports, or URLs), and launch the tool in isolation. Results are merged back into the Neo4j graph using MERGE operations – duplicates are updated, not recreated. The tool runs with all project settings (timeouts, wordlists, API keys, proxy) applied automatically. All pipeline tools support partial recon.

Wiki: Recon Pipeline Workflow – Partial Recon

AI in Pipeline

Optional LLM-augmented decision points wired inside the recon pipeline where static look-ups historically drift – Nuclei prunes its tag list to the detected tech stack, the WAF classifier catches header-stripped Cloudflare/AWS WAF/Imperva, and many others across FFuf, Nuclei false-positive filtering, and subdomain-takeover disambiguation. Each hook is a cascade fallback after the static path with a deterministic safe fallback, so an LLM outage cannot break a scan. One master toggle in the Target tab governs them all.

The recon pipeline uses TypeSafe Jev, a typed decision model, for nine decisions: what kind of page each URL is, which host Hakrawler crawls first, which directories and extensions FFuf fuzzes, whether a tool failed or just found nothing, which Nuclei tags to run, whether a WAF is in front, whether a takeover is real, and which serialization format a flagged blob is. Three more Jev hooks steer the Route Exposure Scan: which routes fill its request budget, whether a lead’s page is a disguised login wall, and how sensitive each lead is, so the agent confirms the most sensitive first. They only rank and order: no lead is dropped, and without a Jev answer the scan is the deterministic one. Jev needs a token on the project owner’s account; see how it works in the Jev Meets the Recon Pipeline walkthrough on redamon.org.

Wiki: TypeSafe Jev

Wiki: AI in Pipeline

AI Gauntlet: Offensive AI/LLM Testing

Where the recon pipeline finds the AI surface, the AI Gauntlet attacks it. Pick the discovered LLM endpoints (or type a custom URL), choose a tool, confirm the Rules of Engagement, and launch: a short-lived container runs the target through four industry red-team tools – garak (broad single-shot scanner, 40 probe families), PyRIT (bounded multi-turn jailbreaks), Giskard (LLM-assisted safety scan), and promptfoo (dataset red-team eval) – measuring an Attack Success Rate per attack and writing the confirmed weaknesses back onto the graph as OWASP-LLM / MITRE-ATLAS-mapped Vulnerability findings with evidence transcripts.

It is deterministic (seeds, fixed bounds, pinned tool versions) and runs with zero external egress: every judge / grader / embedding call is forced to a local Ollama model, so no payload or transcript ever leaves your machine. Off by default and RoE-gated, with live phase-by-phase progress, and findings surfaced in the Red Zone AI Gauntlet Vulnerabilities table and the pentest report.

Wiki: AI Gauntlet | Technical: AI_GAUNTLET_TECH_DOC.md

GVM Vulnerability Scanner

GVM/OpenVAS performs deep network-level vulnerability assessment with 170,000+ NVTs, probing services at the protocol layer for misconfigurations, outdated software, default credentials, and known CVEs. Complements Nuclei’s web-layer findings. Seven pre-configured scan profiles from quick host discovery (~2 min) to exhaustive deep scanning (~8 hours). Findings are stored as Vulnerability nodes in Neo4j alongside the recon graph.

Wiki: GVM Vulnerability Scanning | Technical: README.GVM.md

AI Agent Orchestrator

A LangGraph-based autonomous agent implementing the ReAct pattern. It progresses through three phases: Informational (intelligence gathering, graph queries, Shodan, Google dorking), Exploitation (Metasploit, Hydra credential testing, social engineering simulation), and Post-Exploitation (enumeration, lateral movement). The agent executes 22 security tools via MCP servers inside a Kali sandbox, supports parallel tool execution via Wave Runner, and provides real-time chat interaction with guidance, stop/resume, and approval workflows. Deep Think mode enables structured strategic analysis before acting.

Wiki: AI Agent Guide | Technical: README.AGENTIC_SYSTEM.md

Agent Tool Arsenal

README 内容过长，已截取前 64 KB。完整内容请前往 GitHub 仓库查看。