jiwoochris/artex-global

选用指南

AI 根据 README 整理 · 2026/10/20 23:46:38

适用场景

适用于在本地隔离环境（如OWASP Juice Shop或DVWA）中进行学习、研究和验证，或对拥有明确书面授权的目标进行测试。

查看 README 依据

> - **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](docs/defense-en.md)** (also in [Korean](docs/defense-ko.md)) >

- 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.

目标用户

面向希望了解自主AI攻击并构建检测和防御能力的防御者。

查看 README 依据

> 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

> - **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](docs/defense-en.md)** (also in [Korean](docs/defense-ko.md)) >

使用方式

通过Docker Compose快速启动，或使用安装脚本、预编译二进制、从源码构建单二进制等方式运行。启动后通过浏览器访问Web界面。

查看 README 依据

_{<b>Chat</b> — a human steps into the autonomous run to inject hints while the agent summarizes the attack chain in English.} </p> The original (Chinese UI) screens are available in [`README.zh.md`](README.zh.md#截图预览) (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_KEY` or `OPENAI_API_KEY`; can also be set in the UI).

Upstream provides several methods: an install script (`./install.sh`), precompiled binaries (Releases), and a single-binary build from source. The commands and full procedure are collected in the "安装" (Installation) section of [`README.zh.md`](README.zh.md#安装) (in Chinese); the essentials are reproduced below.

rm -rf server/webui/dist && mkdir -p server/webui/dist && cp -a web/out/. server/webui/dist/ # 2) sync into the embed directory (avoids nesting on rebuild) CGO_ENABLED=0 go build -tags embedui -o artex ./cmd/artex # 3) compile with the frontend embedded ./start.sh # → http://localhost:8787 ```

部署要求

需要Docker和Docker Compose，数据库使用PostgreSQL（由compose启动），探索需要LLM API密钥（ANTHROPIC_API_KEY或OPENAI_API_KEY）。

查看 README 依据

_{<b>Chat</b> — a human steps into the autonomous run to inject hints while the agent summarizes the attack chain in English.} </p> The original (Chinese UI) screens are available in [`README.zh.md`](README.zh.md#截图预览) (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_KEY` or `OPENAI_API_KEY`; can also be set in the UI).

已知限制

输出语言一致性取决于模型能力，较小模型可能导致报告回退到原始语言；OpenAI兼容路径下max_tokens需设置为16384或更低，否则调用会失败。

查看 README 依据

- **Use a capable frontier model.** In a short validation run that applied only the production directive against a local isolated sandbox, the default model `claude-opus-4-8` kept user-facing output in Korean across all four roles — planner, worker, reporter, and an authorized-sandbox planning request — with no refusals. `gpt-4o` also stayed in Korean on the same scenarios. A cheaper, smaller model (for example `gpt-4o-mini`), by contrast, let the report fall back to the original language. Output-language quality tracks model capability directly, so use a capable model wherever a human reads

> **Token-cap pitfall on the OpenAI-compatible path.** OpenAI-family models such as `gpt-4o` cap response tokens at 16,384. OpenAI-compatible requests, however, carry a larger default output cap (32,768), so leaving it unchanged makes every call fail with `400 (max_tokens is too large)`. In that case, **set that profile's `max_tokens` to 16,384 or lower on the LLM settings page.** Anthropic-family models (including the default `claude-opus-4-8`) allow 32,768 and do not hit this pitfall. ### First-run protection (setup token and bind address)