Safety Snowball Agent is an agent-based framework for evaluating how safe visual inputs can combine into unsafe behavior in large vision-language models. The framework accompanies the NeurIPS 2025 paper “Safe + Safe = Unsafe?” and probes a multimodal jailbreak mechanism that differs from traditional adversarial-image attacks.
Dec 2, 2025
MASTERKEY is a research framework for automated jailbreak attack generation and defense evaluation for LLM chatbots. The framework supports systematic analysis of jailbreak strategies across commercial chatbot systems and was published at NDSS 2024.
Feb 26, 2024
An LLM-empowered automatic penetration testing framework with 14k+ GitHub stars and 2.4k+ forks. PentestGPT is designed to automate penetration testing by leveraging the domain knowledge inherent in Large Language Models. It features a three-module architecture (Reasoning, Generation, and Parsing) that emulates human penetration testing workflows. Key Features: Multi-module agent design for reasoning, generation, and parsing Integration with multiple LLM backends and real-world security workflows Evaluation on CTF challenges and practical penetration testing targets 228.6% task completion improvement over baseline GPT models Recognition: Distinguished Artifact Award at USENIX Security 2024 Widely used open-source security research artifact with active community adoption
Aug 1, 2023