WorpGPT: An Adversarial Security Testing Framework for Large Language Models

WorpGPT is a comprehensive red team testing framework designed to systematically evaluate large language models (LLMs) against adversarial manipulations like prompt injection and jailbreak attacks. It provides over 500 structured test templates, supports multiple mainstream LLMs, offers a quantifiable security scoring system, and operates in an isolated sandbox environment. This tool addresses the industry gap of standardized, efficient LLM security testing.

As LLMs integrate into critical systems, adversarial risks (prompt injection, jailbreak, role-play bypass) grow. However, developers lack standardized, safe testing tools—traditional manual methods are time-consuming and low-coverage. Unverified AI apps may deploy with hidden vulnerabilities, leading to production risks. WorpGPT was created to solve this by enabling controlled, systematic testing without real-world harm.

WorpGPT's design focuses on four goals: standardized test templates, automated vulnerability detection, quantifiable reports, multi-model support. Key features:

• Adversarial test library: 500+ categorized templates (attack type, difficulty, component).
• Multi-model support: Works with GPT-4, Llama3, Claude (local/open-source or cloud API).
• Security scoring: Generates a numerical score (e.g.,78/100) with pass/fail details for objective assessment.
• Isolated sandbox: Ensures tests don't affect production systems, allowing safe radical testing.

WorpGPT's usage is straightforward:

1. Download toolkits from release page, extract to isolated directory.
2. Install Python dependencies and configure target model API keys.
3. Launch audit console via command line, specify model ID—system runs preset tests. It supports Windows, Ubuntu, macOS, and Docker deployment, compatible with cloud APIs and local models. The console provides real-time progress, and post-test reports include interaction logs and vulnerability analysis.

WorpGPT's test library covers key attack types:

• Prompt injection: Tests sensitivity to embedded system instructions in user input.
• Jailbreak vectors: Evaluates resistance to role-play or hypothetical scenario bypasses.
• Logic layer bypass: Checks if complex reasoning (multi-round, nested logic) leads to security boundary breaches.
• Information leakage: Assesses risk of training data/system info exposure under adversarial queries.

Beyond vulnerability detection, WorpGPT offers defense suggestions (system prompt modifications) based on a community-validated template library. It emphasizes compliance: usage is limited to education/research/professional audits (users need legal authorization). The project is MIT-licensed, open to community contributions, with third-party audited code and full documentation.

WorpGPT fills a critical gap in LLM security toolchains. Its future roles:

• Model selection: Compare security of different LLMs for procurement.
• Compliance: Support regulatory requirements with standardized reports.
• Research: Serve as a benchmark for adversarial studies.
• CI/CD integration: Automated regression testing for model updates. Limitations: Test coverage is limited to known attacks; scores aren't absolute safety guarantees; tests may generate harmful content (need controlled environments).

WorpGPT transforms scattered red team testing into repeatable, quantifiable processes. It's an essential tool for responsible AI development, helping organizations deploy LLMs safely. For any entity using LLMs in production, WorpGPT is worth exploring as part of a comprehensive security strategy (combined with code audits, input/output filtering, etc.).