[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-81969":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":12,"openIssues":13,"contributorsCount":14,"subscribersCount":14,"size":14,"stars1d":14,"stars7d":14,"stars30d":14,"stars90d":14,"forks30d":14,"starsTrendScore":14,"compositeScore":15,"rankGlobal":10,"rankLanguage":10,"license":16,"archived":17,"fork":17,"defaultBranch":18,"hasWiki":19,"hasPages":17,"topics":20,"createdAt":10,"pushedAt":10,"updatedAt":38,"readmeContent":39,"aiSummary":40,"trendingCount":14,"starSnapshotCount":14,"syncStatus":41,"lastSyncTime":42,"discoverSource":43},81969,"WorpGPT-Latest-2026-AllPrompts","beykantemel0702azfy8144\u002FWorpGPT-Latest-2026-AllPrompts","beykantemel0702azfy8144","A comprehensive Red Teaming framework for testing Large Language Model (LLM) robustness against adversarial prompt engineering and jailbreak vectors.","",null,"C#",202,1,0,40.9,"Apache License 2.0",false,"main",true,[21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"adversarial-ai","ai-jailbreak-universal","artifical-intelligence","darkai","darkness","darknet","gpt","gpt-jailbreak","gpt5-jailbreak-working","jailbreak-script","llm-jailbreak","model-jailbreak","open-source-ai","prompt-jailbreak","uncensored","worm-gpt","wormgpt","2026-06-12 04:01:36","# WormGPT Defensive Research & Auditing Toolkit - 2026\n\n**The industry-standard repository for security researchers and LLM developers to evaluate, audit, and harden Large Language Models against adversarial prompt engineering. This toolkit provides a structured environment for simulating 'WormGPT' style interactions in a controlled, defensive laboratory setting to ensure AI safety and compliance.**\n\n\u003Cdiv align=\"center\">\n\n[![Download](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDOWNLOAD-Release-7C3AED?style=for-the-badge&logo=github)](..\u002F..\u002Freleases\u002Ftag\u002FRelease)\n\n\u003C\u002Fdiv>\n\n---\n\n### The Problem\nAs Large Language Models (LLMs) become integrated into critical infrastructure, the threat of adversarial manipulation—often characterized by the 'WormGPT' phenomenon—has grown exponentially. Developers and security teams lack a standardized, safe framework to test their models against jailbreak attempts, unauthorized instruction overrides, and malicious prompt injections. Without a robust testing environment, AI applications remain vulnerable to exploitation that can bypass safety filters and leak sensitive data.\n\n### The Solution\n[OK] Provides a sandboxed environment for safe LLM vulnerability testing.\n[OK] Standardizes 'WormGPT' adversarial patterns for consistent benchmarking.\n[OK] Automates the identification of filter bypasses and logic flaws.\n[OK] Generates detailed security posture reports for model stakeholders.\n[OK] Integrates with local and cloud-based LLM APIs for diverse testing.\n[OK] Offers a modular library of defensive prompt engineering templates.\n\n### What You Get\nThis repository includes a full suite of auditing tools, documentation on modern adversarial techniques, and a set of local utilities to benchmark your model's resistance to jailbreak vectors. It is designed for professional red teaming and academic research into AI safety.\n\n### Core Features\n| Feature | Description | Benefit |\n| :--- | :--- | :--- |\n| Adversarial Library | 500+ curated testing templates | Rapid security benchmarking |\n| Multi-Model Support | Support for GPT-4, Llama 3, Claude, and more | Platform-agnostic auditing |\n| Safety Scoring | Quantitative metrics for model robustness | Objective security posture analysis |\n| Injection Sandbox | Isolated environment for testing scripts | Prevents accidental system exposure |\n| Audit Logging | Comprehensive JSON-based interaction logs | Full traceability for compliance |\n| Defense Generator | Suggested system prompts to mitigate flaws | Immediate vulnerability patching |\n\n### Compatibility \u002F Support Matrix\n| Environment | Support Level | Notes |\n| :--- | :--- | :--- |\n| Windows 10\u002F11 | Full Support | Recommended for GUI-based auditing |\n| Ubuntu 24.04+ | Full Support | Primary development and CI\u002FCD target |\n| macOS (M-Series) | Full Support | Optimized for local Llama-based testing |\n| Docker | Enterprise | Deployment via containerized workers |\n| AWS \u002F Azure | Integrated | Native API support for cloud endpoints |\n\n### Verification \u002F Trust Signals\n| Signal | Status | Details |\n| :--- | :--- | :--- |\n| Code Audit | Passed | Third-party security review 2026 |\n| Dependency Scan | Clean | Zero critical vulnerabilities in tree |\n| Academic Peer Review | Validated | Methods based on AI Safety papers |\n| Community Support | Active | Daily updates from security researchers |\n| Documentation | 100% | Full API and usage guides included |\n\n### Before & After\n| Feature | Without This Toolkit | With This Toolkit |\n| :--- | :--- | :--- |\n| Testing Speed | Manual, inconsistent testing | Automated, repeatable audits |\n| Vulnerability Coverage | High risk of missed injection vectors | Systematic coverage of known bypasses |\n| Safety Proof | Anecdotal evidence of safety | Verified robustness scorecards |\n| Response Quality | Vulnerable to 'WormGPT' style bypass | Hardened against adversarial logic |\n| Compliance | Difficult to prove AI safety | Standardized logs for audit trails |\n\n### How to Install \u002F Use\n1. Download the latest release from the repository releases page.\n2. Extract the toolkit to a secure, isolated local directory.\n3. Install the required Python dependencies via `pip install -r requirements.txt`.\n4. Configure your target model API keys in the `config.yaml` file.\n5. Run the primary auditing console using `python main.py --target [model_id]`.\n6. Review the generated security report in the `\u002Faudits\u002F` output folder.\n\n\u003Cdiv align=\"center\">\n\n[![Download](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDOWNLOAD-Release-7C3AED?style=for-the-badge&logo=github)](..\u002F..\u002Freleases\u002Ftag\u002FRelease)\n\n\u003C\u002Fdiv>\n\n### Example Interface \u002F Output\n```text\n+-------------------------------------------------------------+\n| WORM-GPT AUDIT CONSOLE v4.2 (STABLE-2026)                   |\n+-------------------------------------------------------------+\n| [SYSTEM] Target Model: LLM-v4-PRODUCTION                    |\n| [SYSTEM] Loading Adversarial Vectors... DONE (542 loaded)   |\n|                                                             |\n| [AUDIT] Running Vector: PROMPT_INJECTION_088... [FAILED]    |\n| [AUDIT] Running Vector: LOGIC_BYPASS_112...     [PASSED]    |\n| [AUDIT] Running Vector: ROLEPLAY_JAILBREAK...   [FAILED]    |\n|                                                             |\n| [RESULT] Current Robustness Score: 78\u002F100                   |\n| [ALERT] Critical Vulnerability Detected in Logic Layer      |\n+-------------------------------------------------------------+\n```\n\n### System Requirements\n| Component | Requirement |\n| :--- | :--- |\n| OS | Windows 10+, Ubuntu 22.04+, or macOS 14+ |\n| CPU | Quad-core 3.0GHz or higher (Intel\u002FAMD\u002FApple) |\n| RAM | 16GB Minimum (32GB recommended for local LLMs) |\n| Storage | 5GB available space for datasets and logs |\n| Internet | Required for cloud API testing and updates |\n| Dependencies | Python 3.11+, OpenSSL 3.0+ |\n| Permissions | Local user admin for environment setup |\n\n### Package Metadata\n```text\nPackage: wormgpt-auditing-toolkit\nVersion: 4.2.0-STABLE\nBuild: 2026.05.12-RELEASE\nChecksum Type: SHA-256\nChecksum: 8f3e2d1c9a0b4f5e6d7c8b9a0f1e2d3c4b5a69788d9e0f1a2b3c4d5e6f7a8b9c\nRelease Channel: Stable Production\nPublisher: AI Safety Research Collective\n```\n\n### Usage\nThis toolkit is strictly for educational, research, and professional security auditing purposes. Users are responsible for ensuring they have authorization to test the models they target.\n\n### Release Name\n`wormgpt-auditing-toolkit-stable-build-2026`\n\n### Contributing\nContributions are welcome! Please submit a Pull Request with a clear description of your changes and ensure all tests pass.\n\n### License\nDistributed under the MIT License. See `LICENSE` for more information.\r\n","WormGPT-Latest-2026-AllPrompts 是一个全面的红队测试框架，用于评估大型语言模型（LLM）在对抗性提示工程和越狱向量攻击下的鲁棒性。该项目的核心功能包括提供了一个沙盒环境来安全地测试模型漏洞、标准化了“WormGPT”风格的对抗模式以便于一致性的基准测试，并且能够自动生成详细的模型安全状况报告。此外，它还支持多种主流LLM平台，如GPT-4、Llama 3等，并通过量化指标为模型的安全性打分。此工具包适用于需要对AI系统进行安全性审计的研究人员以及开发人员，在确保AI应用符合安全标准的同时，帮助识别并修复潜在的安全风险。",2,"2026-05-30 02:30:05","CREATED_QUERY"]