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.
As 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.
[OK] Provides a sandboxed environment for safe LLM vulnerability testing. [OK] Standardizes 'WormGPT' adversarial patterns for consistent benchmarking. [OK] Automates the identification of filter bypasses and logic flaws. [OK] Generates detailed security posture reports for model stakeholders. [OK] Integrates with local and cloud-based LLM APIs for diverse testing. [OK] Offers a modular library of defensive prompt engineering templates.
This 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.
| Feature | Description | Benefit |
|---|---|---|
| Adversarial Library | 500+ curated testing templates | Rapid security benchmarking |
| Multi-Model Support | Support for GPT-4, Llama 3, Claude, and more | Platform-agnostic auditing |
| Safety Scoring | Quantitative metrics for model robustness | Objective security posture analysis |
| Injection Sandbox | Isolated environment for testing scripts | Prevents accidental system exposure |
| Audit Logging | Comprehensive JSON-based interaction logs | Full traceability for compliance |
| Defense Generator | Suggested system prompts to mitigate flaws | Immediate vulnerability patching |
| Environment | Support Level | Notes |
|---|---|---|
| Windows 10/11 | Full Support | Recommended for GUI-based auditing |
| Ubuntu 24.04+ | Full Support | Primary development and CI/CD target |
| macOS (M-Series) | Full Support | Optimized for local Llama-based testing |
| Docker | Enterprise | Deployment via containerized workers |
| AWS / Azure | Integrated | Native API support for cloud endpoints |
| Signal | Status | Details |
|---|---|---|
| Code Audit | Passed | Third-party security review 2026 |
| Dependency Scan | Clean | Zero critical vulnerabilities in tree |
| Academic Peer Review | Validated | Methods based on AI Safety papers |
| Community Support | Active | Daily updates from security researchers |
| Documentation | 100% | Full API and usage guides included |
| Feature | Without This Toolkit | With This Toolkit |
|---|---|---|
| Testing Speed | Manual, inconsistent testing | Automated, repeatable audits |
| Vulnerability Coverage | High risk of missed injection vectors | Systematic coverage of known bypasses |
| Safety Proof | Anecdotal evidence of safety | Verified robustness scorecards |
| Response Quality | Vulnerable to 'WormGPT' style bypass | Hardened against adversarial logic |
| Compliance | Difficult to prove AI safety | Standardized logs for audit trails |
- Download the latest release from the repository releases page.
- Extract the toolkit to a secure, isolated local directory.
- Install the required Python dependencies via
pip install -r requirements.txt. - Configure your target model API keys in the
config.yamlfile. - Run the primary auditing console using
python main.py --target [model_id]. - Review the generated security report in the
/audits/output folder.
+-------------------------------------------------------------+
| WORM-GPT AUDIT CONSOLE v4.2 (STABLE-2026) |
+-------------------------------------------------------------+
| [SYSTEM] Target Model: LLM-v4-PRODUCTION |
| [SYSTEM] Loading Adversarial Vectors... DONE (542 loaded) |
| |
| [AUDIT] Running Vector: PROMPT_INJECTION_088... [FAILED] |
| [AUDIT] Running Vector: LOGIC_BYPASS_112... [PASSED] |
| [AUDIT] Running Vector: ROLEPLAY_JAILBREAK... [FAILED] |
| |
| [RESULT] Current Robustness Score: 78/100 |
| [ALERT] Critical Vulnerability Detected in Logic Layer |
+-------------------------------------------------------------+
| Component | Requirement |
|---|---|
| OS | Windows 10+, Ubuntu 22.04+, or macOS 14+ |
| CPU | Quad-core 3.0GHz or higher (Intel/AMD/Apple) |
| RAM | 16GB Minimum (32GB recommended for local LLMs) |
| Storage | 5GB available space for datasets and logs |
| Internet | Required for cloud API testing and updates |
| Dependencies | Python 3.11+, OpenSSL 3.0+ |
| Permissions | Local user admin for environment setup |
Package: wormgpt-auditing-toolkit
Version: 4.2.0-STABLE
Build: 2026.05.12-RELEASE
Checksum Type: SHA-256
Checksum: 8f3e2d1c9a0b4f5e6d7c8b9a0f1e2d3c4b5a69788d9e0f1a2b3c4d5e6f7a8b9c
Release Channel: Stable Production
Publisher: AI Safety Research Collective
This 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.
wormgpt-auditing-toolkit-stable-build-2026
Contributions are welcome! Please submit a Pull Request with a clear description of your changes and ensure all tests pass.
Distributed under the MIT License. See LICENSE for more information.