How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
This project is no longer actively maintained. You are welcome to fork and continue its development on your own. Thank you for your interest and support.
The main features of mnns/llmfuzzer are: AI Application Security, AI Security, AI Security and Red Teaming, Detection and Monitoring.
Open-source alternatives to mnns/llmfuzzer include: leondz/garak — Garak is a suite of tools for measuring AI reliability, scanning for vulnerabilities, and automating security… protectai/rebuff — LLM Prompt Injection Detector. promptfoo/promptfoo — Promptfoo is an evaluation framework designed for testing, benchmarking, and red-teaming language models and agentic… cleverhans-lab/cleverhans — Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples,… azure/pyrit — PyRIT is an AI vulnerability assessment tool and security scanner designed to detect risks in large language model… protectai/ai-exploits — A collection of real world AI/ML exploits for responsibly disclosed vulnerabilities.
Garak is a suite of tools for measuring AI reliability, scanning for vulnerabilities, and automating security assessments through adaptive probing. It functions as a generative AI vulnerability scanner and evaluation tool designed to identify security gaps, hallucinations, and failure modes in language models. The framework provides a toolkit for red-teaming and safety assessments, utilizing a structured system of probes and detectors to calculate failure rates. It specifically scans for risks such as data leakage and prompt injection by recording model responses to adversarial inputs. The p
PyRIT is an AI vulnerability assessment tool and security scanner designed to detect risks in large language model applications. It functions as a generative AI red teaming framework used to simulate adversarial attacks and identify weaknesses in system guardrails. The tool automates AI risk assessment by scanning generative AI components for security vulnerabilities. It utilizes automated testing and analysis to identify security gaps and prevent potential exploits through a consistent, repeatable process. The system incorporates asynchronous model orchestration to compare security postures
Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples, incorporate them into training loops, and benchmark the resilience of machine learning models. It provides a gradient-based attack framework for constructing both white-box and black-box attacks to identify model misclassifications. The project includes capabilities for model robustness benchmarking, allowing users to evaluate and verify how models resist evasion attacks and malicious input perturbations. It also facilitates adversarial training to increase a model's resistance to pert
