能力标签
ERR0RS-Ultimate
🛠
AI工具

ERR0RS-Ultimate

基于 Python · 开源免费,本地部署,数据完全自主可控
⭐ 21 Stars 🍴 2 Forks 💻 Python 📄 MIT 🏷 AI 7.5分
7.5AI 综合评分
tag1tag2tag3
✦ AI Skill Hub 推荐

ERR0RS-Ultimate 是 AI Skill Hub 本期精选AI工具之一。综合评分 7.5 分,整体质量较高。我们推荐使用将其纳入你的 AI 工具库,帮助提升工作效率。

📚 深度解析

ERR0RS-Ultimate 是一款基于 Python 的开源工具,在 GitHub 上收获 0k+ Star,是tag1、tag2、tag3领域中的优质开源项目。开源工具的最大优势在于代码完全透明,你可以审计每一行代码的安全性,也可以根据自身需求进行二次开发和定制。

**为什么要使用开源工具而非商业 SaaS?**
对于个人开发者和有隐私需求的用户,本地部署的开源工具意味着数据不离本机,不受第三方服务商的数据政策约束。同时,开源工具通常没有使用次数限制和月度费用,一次安装即可长期使用,对于高频使用场景的总拥有成本(TCO)远低于订阅制商业工具。

**安装与环境准备**
ERR0RS-Ultimate 依赖 Python 运行环境。建议通过 pyenv(Python)或 nvm(Node.js)管理 Python 版本,避免全局环境污染。对于新手用户,推荐先创建虚拟环境(python -m venv venv && source venv/bin/activate),再安装依赖,这样即使出现问题也可以随时删除虚拟环境重新开始,不影响系统稳定性。

**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues(已关闭的问题),大多数常见问题都已有解答。遇到 Bug 时,提供 pip list 的输出、完整错误堆栈和最小可复现示例,能显著提高开发者响应速度。AI Skill Hub 将持续追踪 ERR0RS-Ultimate 的版本更新,及时通知重要功能变化。

📋 工具概览

ERR0RS-Ultimate 是一款基于 Python 开发的开源工具,专注于 tag1、tag2、tag3 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。

GitHub Stars
⭐ 21
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
MIT
AI 综合评分
7.5 分
工具类型
AI工具
Forks
2

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

ERR0RS-Ultimate 是一款基于 Python 开发的开源工具,专注于 tag1、tag2、tag3 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。

📌 核心特色
  • 开源免费,支持本地部署,数据完全自主可控
  • 活跃的 GitHub 开源社区,持续迭代更新
  • 提供详细文档和使用示例,新手友好
  • 支持自定义配置,灵活适配不同使用环境
  • 可作为基础组件集成进现有技术栈或进行二次开发
🎯 主要使用场景
  • 本地部署运行,保护数据隐私,满足合规要求
  • 自定义集成到现有系统,扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:pip 安装(推荐)
pip install err0rs-ultimate

# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install err0rs-ultimate

# 方式三:从源码安装(获取最新功能)
git clone https://github.com/Gnosisone/ERR0RS-Ultimate
cd ERR0RS-Ultimate
pip install -e .

# 验证安装
python -c "import err0rs_ultimate; print('安装成功')"
📋 安装步骤说明
  1. 访问 GitHub 仓库页面
  2. 按照 README 文档完成依赖安装
  3. 根据系统环境完成初始化配置
  4. 参考官方示例或文档开始使用
  5. 遇到问题可在 GitHub Issues 中查找解答
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
err0rs-ultimate --help

# 基本用法
err0rs-ultimate input_file -o output_file

# Python 代码中调用
import err0rs_ultimate

# 示例
result = err0rs_ultimate.process("input")
print(result)
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# err0rs-ultimate 配置文件示例(config.yml)
app:
  name: "err0rs-ultimate"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
err0rs-ultimate --config config.yml

# 或通过环境变量配置
export ERR0RS_ULTIMATE_API_KEY="your-key"
export ERR0RS_ULTIMATE_OUTPUT_DIR="./output"
📑 README 深度解析 真实文档 完整度 75/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

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  █████╗  ██████╔╝██████╔╝██║██╔██║██████╔╝███████╗
  ██╔══╝  ██╔══██╗██╔══██╗████╔╝██║██╔══██╗╚════██║
  ███████╗██║  ██║██║  ██║╚██████╔╝██║  ██║███████║
  ╚══════╝╚═╝  ╚═╝╚═╝  ╚═╝ ╚═════╝ ╚═╝  ╚═╝╚══════╝
                        U L T I M A T E

The open-source AI security platform built for everyone who can't afford the enterprise tools.

Python Platform License Version Tool Registry [Arsenal]() Backends [Pi5]() Stars Juice Shop Coverage-22c55e?style=flat-square) Contributors Welcome

Runtime is 100% local · zero data leaves the machine · built for red teams, blue teams, and students who are becoming both

Install · Quick Start · Backends · Architecture · Philosophy · Research · Portfolio · Contribute

</div>

---

What's New in v3.7.0 — "SOC Mentor"

This release lands the operator-progression layer that turns ERR0RS from a tool dictionary into a guided learning environment. The headline feature: every lesson now teaches the strategic and OPSEC dimensions of a tool, not just its flags.

  • 🥷 SOC Mentor lesson layer — 23/23 topics covered. Every teach <topic> now ends with a noise-rated coaching block: TL;DR strategic value, 🟢/🟡/🔴 noise level with explanation, ordered next-best-steps (quietest first), and 4 concrete OPSEC tips per tool. The constitution this implements: "ERR0RS is the ultimate SOC mentor. He should teach his SOC apprentice how to be as stealthy and quiet as possible, as to not expose the test until the operator is ready for the client to know that they are in." Lives in src/core/soc_mentor.py.
  • Persistent server-authoritative mission state. ~/.err0rs/mission_state.json is the single source of truth. Survives reboots, browser refreshes, and tab close. Auto-clears on completion with a one-shot just_completed flag so the celebration card fires exactly once.
  • Mission 01: Your First Recon — fully playable. 3-step nmap → nikto → gobuster walkthrough against OWASP Juice Shop. Each step has rich coaching fields (instruction, what_it_does, what_to_look_for, xp_reward). ▶ RUN STEP button fires the command verbatim, bypassing the intent parser so the mission's exact args reach the tool.
  • Mission 02: SQL Injection Fundamentals — shipped. 4 steps, +175 XP. Manual curl → classic ' OR 1=1-- payload → JWT decode → sqlmap automation. Teaches the SOC-mentor approach: harvest server error messages before guessing payloads; prefer offline cracking over online brute.
  • Per-launch ethics gate. Every launcher boot re-fires the 5-clause ethical use agreement (red-bordered fullscreen modal, checkbox + I AGREE). PID-based invalidation: relaunch = new PID = gate re-fires. No bypasses.
  • Operator Profile panel. Extends the existing skill panel with three new sections: OPERATOR (name, skill, sessions, achievements), MODES (Teach Mode / Auto-Coach / Mentor Context toggles), ACTIONS (Continue Lessons, Restart Mission, Reset Profile). Reset is two-click with automatic backup of ~/.err0rs/ before wipe.
  • Welcome-back greeting card on every launch after ethics-gate, with tone calibrated to skill level (Welcome back, NAME. for guided / NAME. for pro), and a "next action" button — Continue Mission if active, Next Lesson if not, just a greeting if both are clear.
  • XP awards on EVERY tool execution path. Brain _run_tool, LiveProcess terminal-box runs, and AutoKillChain phase loops all now fire award_xp('run_<tool>') and found_vuln events. Skill domains and achievements finally populate from real activity.
  • Lesson completion tracking. Each teach <topic> automatically marks the topic completed, awards 30 XP via complete_lesson event, and advances the lesson counter on the skill panel. Continue Lessons now actually progresses through all 23 topics instead of re-serving the first one.
  • Architectural xterm typing fix. xset r off disables X11 keyboard auto-repeat during synthetic typing so the X server can't emit stuck-key repeats, then xset r on restores in a finally block. Combined with a goldilocks 50ms delay, gobuster commands type as wordlists (correctly) instead of worrrrdllllists or wordlist.
  • Phoenix Arsenal page linked in the topbar. arsenal.html (789-line tool grid, 92 curated + 2000+ BlackArch) is now reachable via a cyan 🔱 ARSENAL pill — was previously a finished feature with zero links to it from anywhere.
  • Payload Studio: ATTACKER_IP auto-substitution. When listener spins up, backend resolves the Pi's outbound interface IP via UDP-socket trick (no nmap needed), and the editor replaces ATTACKER_IP placeholders with the real value. Snippet library (21 BadUSB/BadKB payloads across 4 platforms) restored after fixing same string-literal truncation bug class as v3.6.

What's New in v3.6.0 — "Teach Knowledge Drop"

This release lands the first major payload of operator-grade teach content into the canonical registry and ships the infrastructure that made it sustainable to produce.

  • 67 tools fully taught at operator depth — every one now carries 6 opsec notes (current to 2025–2026 EDR/AMSI/ETW-TI tradecraft), 2 sample command outputs (beginner + advanced operator scenario), 3 legal notes (CFAA / ROE / cloud automated-response considerations), 5 false-positive traps, and full MITRE ATT&CK technique mappings. Total: 1,328 distinct pieces of red-team teach content across the registry.
  • RAG knowledge base onlinetools/ingest_teach_to_rag.py embeds all 67 teach cards into a local ChromaDB collection (err0rs_teach_v1) using all-MiniLM-L6-v2. Semantic queries like "kerberoasting active directory" surface the right card (Rubeus) every time. Pi 5 CPU-resident, no GPU needed.
  • err0rs-qwen model baked — the ERR0RS soul (src/ai/system_prompt.md) is now embedded directly into a customized qwen2.5-coder:7b via Modelfile. Tertiary-tier offline inference inherits the teacher voice without prompt-injection.
  • Build-time teach generator hardenedtools/generate_teach.py cost-tracking rewrite: real per-million-token billing from msg.usage (not flat per-call guesses), per-model rate overrides (Sonnet vs Opus vs Haiku), and a cap loop that fires on actual spend. Projection accuracy improved from −56% to −7%.
  • Human-in-the-loop merge tooltools/merge_generated.py with interactive per-card review (approve/skip/edit/diff/quit), atomic writes, git tags + backup files + session logs, and a non-interactive --from-decisions batch path for trusted bulk merges.
  • Quality gatestools/quality_gates.py runs 12 categorical checks (schema completeness, character ranges, MITRE format, duplicate detection, JSON safety, command-binary cross-references) before merge to catch generator misfires.

See CHANGELOG.md for the complete changelist.

---

Core Capabilities

Prerequisites

RequirementNotes
**Kali Linux, Parrot OS, or Ubuntu/Debian**x86_64 or ARM64
**Python 3.10+**python3 --version to verify
**git**sudo apt install git
**Ollama**Installed automatically by install.sh
**~4 GB disk free**For Ollama model + Python deps
💡 Phoenix Arsenal users: Install Phoenix-OS before ERR0RS to unlock the 2,172-tool grid. ERR0RS auto-detects Phoenix at /home/kali/Phoenix-OS. ERR0RS works fully without Phoenix.

---

Installation

2. Install (handles deps, Ollama, .env, desktop icon, 35+ tools)

sudo bash install.sh

Raspberry Pi 5 (field deployment)

```bash

Pi 5 first-boot setup (sets up ARM64 deps, GPU memory split)

sudo bash scripts/pi5_first_boot.sh

Standard install

sudo bash install.sh ```

⚠️ Hailo + Ollama: The Hailo-10H NPU is online and identifies via hailortcli, but Ollama doesn't yet use it for LLM acceleration. See docs/HAILO_PHASE3_STATUS.md for current status and the four paths forward.

---

First-Run Setup Wizard

For interactive .env setup:

python3 main.py --setup

Walks you through: backend selection, model choice, web UI bind/port, security key generation, engagement defaults, and teach mode default.

---

Manual / Advanced Install

```bash pip install -r requirements-kali.txt --break-system-packages

Hardware Stack (Cyberdeck Build)

The reference implementation runs on a Raspberry Pi 5 Cyberdeck:

ComponentSpec
**SBC**Raspberry Pi 5 8GB or 16GB
**AI Accelerator**Hailo-10H NPU (26 TOPS) via AI HAT+
**Storage**NVMe SSD (Geekworm X1004)
**WiFi Adapters**Alfa AWUS036ACM (5GHz) + built-in Pi WiFi
**RF Tools**Flipper Zero (RogueMaster) + CC1101
**Wireless Attack**WiFi Pineapple Nano
**HID Attack**ESP32 with Marauder firmware
**Total Cost**~$400-500 USD

Running Kali Linux ARM64. Full ERR0RS deployment with Phoenix Arsenal, local LLM inference, and hardware control — field-portable in a 3D-printed case.

---

🤝 Help Build This — Call for Contributors

ERR0RS is built by one person right now. It shouldn't be.

This project exists because the security industry has an access problem — and no single student can solve that alone. The codebase, the curriculum, the tool coverage, the language support, the hardware integrations — all of it gets better when more people care. If you read this far and any part of the mission resonated, there's a contribution shape for you.

🎓 Guided Onboarding for Every Skill Level

First-time users get a 4-screen wizard: who ERR0RS is, an ethical use agreement (required, not optional), a skill self-assessment that sets the mode, and a guided first mission against OWASP Juice Shop with step-by-step coaching. Experienced operators can skip straight to expert mode via python3 main.py --setup.

Quick Start

Once running at http://127.0.0.1:8765, try these in the terminal:

```bash

Demo Mode

No lab setup needed. ERR0RS includes a built-in demonstration mode that showcases its capabilities against safe local targets:

python3 src/ui/errorz_launcher.py --demo

Demo mode runs against localhost and 127.0.0.1 only. It demonstrates tool execution with live streaming output, Auto Coach analysis blocks, conversation AI coaching with the ERR0RS soul, the progression XP system, and report generation.

---

3. (optional) Add API keys for Claude / DeepSeek

nano .env

Optional: Hailo-10H NPU driver (if you have the AI HAT+)

sudo bash scripts/install_hailo_h10.sh

Copy the env template and fill in your values

cp .env.example .env nano .env ```

---

🔌 Hardware Integration

Native support for the field operator's stack:

HardwareCapability
**Flipper Zero**Full studio + Evolution Engine (10-level XP) + auto-detect
**WiFi Pineapple Nano**PineAP engine, recon modules, client capture
**Alfa AWUS036ACM**Monitor mode, packet injection, 5GHz coverage
**USB Rubber Ducky**Payload library browser, DuckyScript editor
**Bash Bunny**Multi-stage payload management
**Hailo-10H NPU**On-device AI inference on Raspberry Pi 5 (see [docs/HAILO_PHASE3_STATUS.md](docs/HAILO_PHASE3_STATUS.md) for current acceleration status)
🇨🇳 中文文档镜像 AI 翻译 2026-06-10
英文原文章节由系统翻译为中文摘要,便于快速理解。完整原文见上方 "📑 README 深度解析"。
📌 简介

ERR0RS-Ultimate 是一个面向所有人开发的开源 AI 安全平台的简介。

⚡ 功能介绍

ERR0RS-Ultimate 的新功能包括 operator 进度层、SOC Mentor 课程层和 Teach Knowledge Drop 等。

📋 环境依赖

ERR0RS-Ultimate 的环境依赖和系统要求包括 Kali Linux、Parrot OS、Ubuntu/Debian、Python 3.10+、git 和 Ollama 等。

🛠 安装步骤(Docker/pip/源码)

ERR0RS-Ultimate 的安装步骤包括使用 install.sh 脚本、安装依赖项、配置环境变量和创建桌面图标等。

🚀 使用教程

ERR0RS-Ultimate 的使用教程包括首次使用的 4 个屏幕向导、快速启动和 expert 模式等。

⚙️ 配置说明(含 MCP / env)

ERR0RS-Ultimate 的配置说明包括添加 API 密钥、安装 Hailo-10H NPU 驱动和填写环境变量等。

🔄 工作流/模块

ERR0RS-Ultimate 的工作流和模块说明包括硬件集成、Flipper Zero、WiFi Pineapple Nano 和 Alfa AWUS036ACM 等。

🎯 aiskill88 AI 点评 B 级 2026-06-01

该项目提供了一个AI-powered的渗透测试框架,具有150+工具集成和秒级响应,值得关注

📚 实用指南(长尾问题)
适合谁
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
  • 跨境业务、多语言内容运营团队
最佳实践
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • 分块大小建议 256-512 tokens,向量库优选 pgvector 或 Qdrant
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • embedding 模型与查询模型不一致导致检索失效
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
  • Python 依赖冲突:建议用 venv / uv 隔离环境
部署方案
  • Docker:ERR0RS-Ultimate 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 本地部署:CPU 8GB 起,GPU 推荐 16GB+ 显存
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
ERR0RS-Ultimate 中文教程ERR0RS-Ultimate 安装报错怎么办ERR0RS-Ultimate Docker 部署ERR0RS-Ultimate Agent 工作流ERR0RS-Ultimate 与同类工具对比ERR0RS-Ultimate 最佳实践ERR0RS-Ultimate 适合谁用

⚡ 核心功能

👥 适合谁
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
  • 跨境业务、多语言内容运营团队
⭐ 最佳实践
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • 分块大小建议 256-512 tokens,向量库优选 pgvector 或 Qdrant
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • embedding 模型与查询模型不一致导致检索失效
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型

👥 适合人群

AI 技术爱好者研究人员和学生开发者和工程师技术创业者

🎯 使用场景

  • 本地部署运行,保护数据隐私,满足合规要求
  • 自定义集成到现有系统,扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发

⚖️ 优点与不足

✅ 优点
  • +MIT 协议,可免费商用
  • +完全开源免费,无授权费用
  • +本地部署,数据完全自主可控
  • +开发者社区支持,遇问题可查可问
⚠️ 不足
  • 安装和初始配置可能需要一定技术基础
  • 功能完整性通常不如成熟商业产品
  • 技术支持主要依赖开源社区,响应速度不稳定
⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。

🔗 相关工具推荐

📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

ERR0RS-Ultimate 是一款Python开发的AI辅助工具。开源AI工具:AI-Powered Penetration Testing Framework with 150+ Tools Integrations and sec to。⭐21 · Python 主要应用场景包括:用于AI渗透测试框架的核心使用场景。
💡 AI Skill Hub 点评

经综合评估,ERR0RS-Ultimate 在AI工具赛道中表现稳健,质量良好。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。

📚 深入学习 ERR0RS-Ultimate
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 ERR0RS-Ultimate
原始描述 开源AI工具:AI-Powered Penetration Testing Framework with 150+ Tools Integrations and sec to。⭐21 · Python
Topics tag1tag2tag3
GitHub https://github.com/Gnosisone/ERR0RS-Ultimate
License MIT
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/Gnosisone/ERR0RS-Ultimate

收录时间:2026-06-01 · 更新时间:2026-06-02 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。