ARTEX — Global Edition
An autonomous penetration-testing system driven by LLM multi-agents (Go backend + Next.js frontend)
The UI and generated output default to English, and can be switched to Korean, Chinese or Spanish in Settings (System → Settings → Display language). Changing the language also changes the language of agent-generated reports, summaries and chat replies.
🚨 Security & misuse warning — read this first
This repository is published for use only within authorized environments, and only to build defensive and detection capabilities.
ARTEX is an autonomous offensive tool powerful enough to carry an attack from reconnaissance through intrusion to data exfiltration with little human involvement, so the harm from misuse is correspondingly large. In October 2026, several Korean news outlets reported that investigators had found indications the upstream ARTEX was used in personal-data breaches targeting Korean financial institutions; the related investigation is ongoing. This global edition is not published to help attackers. Its purpose is to help defenders understand how such autonomous AI attacks work and build the capability to detect and block them.
- Unauthorized use is a crime in itself. Do not run any scanning, probing, or exploitation against systems you do not own or for which you lack explicit written authorization. In the Republic of Korea, unauthorized intrusion into an information and communications network violates the Network Act, and the Personal Information Protection Act also applies where personal data is involved.
- Do not target live services or other parties' assets. Verify only in learning, research, and locally isolated environments you own (deliberately vulnerable targets such as OWASP Juice Shop or DVWA).
- Read it from a defender's point of view. This repository also compiles defensive and detection material, such as detection signatures and hardening checklists, for autonomous AI attacks. → Defense & Detection Guide (also in Korean)
If you do not agree to this warning and to the usage restrictions and disclaimer below, do not download or use this repository.
This repository is a localized edition of the Chinese open-source project Autumn-27/ARTEX (AGPL-3.0 · upstream repository currently unreachable, checked 2026-10), adapted so that users and teams worldwide can adopt it as-is. To preserve the agents' decision-making performance, the internal reasoning prompts are kept in the original language, and only the user-facing output (findings, summaries, reports, chat replies) is forced into the selected language (English by default). See "Why a global edition" below for the rationale.
ARTEX is a system in which several LLM-driven agents autonomously run a penetration test: they break goals down on their own, execute real tools, and accumulate discovered assets and vulnerabilities into a graph as they go. A single Go binary ships with the Next.js frontend embedded, and all data is stored in PostgreSQL.
Note on this edition's language. This fork makes the product usable in four languages — English (default), Korean, Chinese, and Spanish — selectable in Settings. The user-facing output is forced into the selected language, while the agents' internal reasoning prompts are kept in the original language to preserve benchmarked performance. See Configuration for how the output language is enforced by a small code-fixed directive.
⚠️ Read first — authorized use and legal notice
ARTEX may be used only against targets you own or for which you have explicit written authorization. Any scanning, probing, or exploitation beyond the authorized scope may itself be illegal.
- In the Republic of Korea, intruding into or disrupting another party's information and communications network without authorization violates the Act on Promotion of Information and Communications Network Utilization and Information Protection (정보통신망법).
- Personal data collected or exposed during a penetration test is subject to the Korean Personal Information Protection Act (개인정보보호법). Even with authorization, handle the access, retention, and deletion of personal data with care.
- Wherever you are, comply with your own jurisdiction's laws on network security, data protection, and computer crime.
- Use this for learning, research, and verification in locally isolated environments. Before targeting any live external system, secure written authorization and an agreed scope and time window.
Full license terms, usage restrictions, and the disclaimer are in the License and disclaimer section below. Using this tool constitutes your agreement to those terms.
Why a global edition
Upstream ARTEX has its prompts, UI, and documentation entirely in Chinese, which made it cumbersome for anyone who does not read Chinese to read the results and share them with a team. This edition aims to:
- Localize the output — the findings, fact summaries, final reports, and chat replies that agents surface to a human are forced into the selected language (English by default; Korean, Chinese, and Spanish selectable). Commands, payloads, code, URLs, and raw logs are needed for analysis and are left in their original form.
- Preserve performance — the internal reasoning prompts (the behavioral instruction body) that drive the agents' judgment are not translated. Behavior benchmarked in the original language is kept intact, and only the output language is changed, avoiding the quality drift that translation introduces.
- State the legal boundaries — the "authorized scope only" warning is stated clearly, together with Korea-specific notices on network and privacy law (the maintainers are based in Korea); the law of wherever you operate applies as well.
- Support diverse LLM stacks — the LLM provider can be swapped from a frontier model to any OpenAI-compatible endpoint (domestic or open models). See Configuration.
The boundaries and design policy of this multilingual edition are documented in more detail in the repository's working notes. To make it easy to diff against the upstream repository, the original Chinese document is preserved as
README.zh.md.
Screenshots
The three screens below show the default English UI (switchable to Korean, Chinese, and Spanish in Settings). The data comes from a local, isolated sandbox: every target is the fictional acme.com and private address ranges.
The original (Chinese UI) screens are available in README.zh.md (Chinese UI).
Quick start (Docker Compose)
Prerequisites: Docker and Docker Compose. The database is PostgreSQL, brought up by compose. Exploration requires an LLM (
ANTHROPIC_API_KEYorOPENAI_API_KEY; can also be set in the UI).
Image: the
artexservice indocker-compose.ymlpullsghcr.io/jiwoochris/artex-global, the global-edition image this repository publishes to GitHub Container Registry on every release tag (v*). The default tag islatest(the most recent release); setARTEX_TAGin.envto pin a specific release. Upstream's original imageautumn27/artexhas been taken down from Docker Hub and is no longer used.
git clone https://github.com/jiwoochris/artex-global.git
cd artex-global
cp .env.example .env # set POSTGRES_PASSWORD; ANTHROPIC_API_KEY is optional
docker compose up -d # brings up the artex image + postgres together
# → open http://localhost:8787 (on first visit, set the admin password at /setup)
# /setup asks for the setup token printed in the startup log: docker compose logs artex | grep '설정 토큰'
The image bundles common tools (ripgrep, curl, vim, npm, nmap, and more), and when brought up with compose, ./skills and ./data are bind-mounted to the host and survive container recreation.
Other installation methods
Upstream provides several methods: an install script (./install.sh), precompiled binaries (Releases), and a si