Llm Attacks | Awesome Repository

This repository provides tools and methodologies for studying adversarial attacks on large language models. It focuses on understanding how carefully crafted inputs can manipulate or bypass the safety mechanisms of LLMs, enabling researchers to probe model vulnerabilities and improve their robustness. The project covers techniques for generating adversarial prompts, evaluating model responses under attack conditions, and analyzing the effectiveness of different attack strategies.

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.

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.