llm-attacks/llm-attacks

Features

• Adversarial Input Generation - Generates gradient-based adversarial inputs to stress-test AI model safety alignments.
• Model Experiment Execution - Implements a system for running harmful prompts across multiple models to compare safety robustness.
• LLM Evaluation Frameworks - Provides a testing environment to quantify how often harmful prompts bypass safety filters.
• Adversarial Robustness Testing - Quantifies the success rate of jailbreak attacks through batch experiments to evaluate model stability.
• Model Benchmarking Suites - Runs batch experiments across multiple harmful behaviors to measure model vulnerability to attacks.
• Model Red-Teaming - Implements automated adversarial testing to detect safety failures and bypass safety filters in LLMs.
• Adversarial Suffix Optimization - Uses coordinate gradient descent to iteratively refine token sequences that maximize adversarial response probability.
• Safety and Alignment Frameworks - Provides a framework for identifying alignment failures to ensure models do not generate harmful content.
• Adversarial Attacks - Provides a comprehensive toolkit for generating gradient-based adversarial prompts to evaluate model robustness.
• Discrete Token Search - Searches the model vocabulary for replacement tokens that align with calculated gradient vectors.
• Gradient-Based Jailbreak Generators - Automatically optimizes string sequences using model gradients to trigger restricted responses from language models.
• Adversarial Suffix Generation - Generates specific string sequences to append to queries that bypass safety filters.
• Transferability Testing - Validates if adversarial suffixes created on open-source models can bypass filters on closed-source models.
• Evaluation Benchmarks - Universal and transferable adversarial attacks on aligned models.
• White Box Attacks - Provides universal and transferable adversarial attacks on aligned models.