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。
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。
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。
# 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')"
# 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)
# 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"
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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.
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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
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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.
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.~/.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.' 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.~/.err0rs/ before wipe.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._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.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.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.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.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.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.
tools/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.tools/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%.tools/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.tools/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.
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| Requirement | Notes |
|---|---|
| **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.
---
sudo bash install.sh
```bash
sudo bash scripts/pi5_first_boot.sh
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.
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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.
---
```bash pip install -r requirements-kali.txt --break-system-packages
The reference implementation runs on a Raspberry Pi 5 Cyberdeck:
| Component | Spec |
|---|---|
| **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.
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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.
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.
Once running at http://127.0.0.1:8765, try these in the terminal:
```bash
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.
---
nano .env
sudo bash scripts/install_hailo_h10.sh
cp .env.example .env nano .env ```
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Native support for the field operator's stack:
| Hardware | Capability |
|---|---|
| **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) |
ERR0RS-Ultimate 是一个面向所有人开发的开源 AI 安全平台的简介。
ERR0RS-Ultimate 的新功能包括 operator 进度层、SOC Mentor 课程层和 Teach Knowledge Drop 等。
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。
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。
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。
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。
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。
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
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。
✅ 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。
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。
| 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 |
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。