justinkyuQA/llm-prompt-injection-suite

LLM Prompt Injection Suite A modular toolkit for evaluating prompt injection vulnerabilities, adversarial behaviors, and instruction bypass techniques in Large Language Models (LLMs). Overview The LLM Prompt Injection Suite is a research-oriented project focused on testing the resilience and safety boundaries of modern AI systems. This toolkit is designed to help researchers, developers, and security practitioners: - evaluate prompt injection resistance - analyze jailbreak effectiveness - test instruction hierarchy handling - measure safety policy robustness - build datasets for adversarial AI testing - automate evaluation workflows The project is intended for defensive security research, model evaluation, and AI alignment experimentation. Features Current Features - Prompt injection testing - Basic evaluator framework - Prompt corpus support - Result logging - Modular Python structure - Dataset experimentation Planned Features - Automated fuzzing engine - Payload mutation system - Scoring and ranking metrics - Batch testing pipelines - Local LLM integration - Docker deployment support - Reporting dashboard - JSON/CSV export support - Multi-model comparison testing Project Structure llm-prompt-injection-suite/ ├── prompts/ ├── datasets/ ├── outputs/ ├── src/ │ ├── evaluator.py │ ├── scoring.py │ └── utils.py ├── tests/ ├── requirements.txt └── README.md Installation Clone the repository: git clone https://github.com/YOUR_USERNAME/llm-prompt-injection-suite.git cd llm-prompt-injection-suite Install dependencies: pip install -r requirements.txt Usage Run the evaluator: python src/evaluator.py - configurable payload sets - API integrations - local model testing - automated fuzzing workflows Research Goals This project explores: - adversarial prompting - prompt injection vectors - instruction override attacks - context poisoning - prompt leakage - evaluation methodologies for AI systems Ethical Use This repository is intended strictly for: - defensive security research - educational purposes - AI safety evaluation - authorized testing environments Users are responsible for complying with all applicable laws, platform policies, and responsible disclosure practices. Roadmap v0.1 - Initial evaluator - Basic prompt datasets - Logging support v0.2 - Mutation engine - Payload generation - Improved reporting v0.3 - LLM fuzzing integration - Multi-model benchmarking - Dockerized deployment v1.0 - Full modular framework - Plugin architecture - Automated research workflows Technologies - Python - Git - GitHub - Linux / Termux - AI Safety Research - LLM Evaluation Techniques Contributions, ideas, and research discussions are welcome. License MIT License