awesome-repositories.com
Blog
MCP
awesome-repositories.com

Descubre los mejores repositorios open-source con nuestra búsqueda potenciada por IA.

ExplorarBúsquedas curadasAlternativas open-sourceSoftware autohospedableBlogMapa del sitio
ProyectoServidor MCPAcerca deCómo clasificamosPrensa
Aviso legalPrivacidadTérminos
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
mnns avatar

mnns/LLMFuzzer

0
View on GitHub↗
353 estrellas·60 forks·Python·MIT·5 vistas

LLMFuzzer

This project is no longer actively maintained.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.

Features

  • AI Application Security - Fuzzing framework specifically designed for testing large language models.
  • AI Security - Fuzzing framework designed for LLM integrations.
  • AI Security and Red Teaming - Fuzzing framework for finding vulnerabilities in LLMs.
  • Detection and Monitoring - Fuzzing framework specifically designed for testing LLM integrations.

Historial de estrellas

Gráfico del historial de estrellas de mnns/llmfuzzerGráfico del historial de estrellas de mnns/llmfuzzer

Búsqueda con IA

Explora más repositorios increíbles

Describe lo que necesitas en lenguaje sencillo: la IA clasifica miles de proyectos open-source curados por relevancia.

Start searching with AI

Alternativas open-source a LLMFuzzer

Proyectos open-source similares, clasificados según cuántas características comparten con LLMFuzzer.
  • protectai/rebuffAvatar de protectai

    protectai/rebuff

    1,501Ver en GitHub↗

    LLM Prompt Injection Detector

    TypeScript
    Ver en GitHub↗1,501
  • leondz/garakAvatar de leondz

    leondz/garak

    8,227Ver en GitHub↗

    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

    Python
    Ver en GitHub↗8,227
  • azure/pyritAvatar de Azure

    Azure/PyRIT

    3,444Ver en GitHub↗

    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

    Pythonai-red-teamgenerative-aired-team-tools
    Ver en GitHub↗3,444
  • cleverhans-lab/cleverhansAvatar de cleverhans-lab

    cleverhans-lab/cleverhans

    6,443Ver en GitHub↗

    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

    Jupyter Notebookbenchmarkingmachine-learningsecurity
    Ver en GitHub↗6,443
Ver las 30 alternativas a LLMFuzzer→

Preguntas frecuentes

¿Qué hace mnns/llmfuzzer?

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.

¿Cuáles son las características principales de mnns/llmfuzzer?

Las características principales de mnns/llmfuzzer son: AI Application Security, AI Security, AI Security and Red Teaming, Detection and Monitoring.

¿Qué alternativas de código abierto existen para mnns/llmfuzzer?

Las alternativas de código abierto para mnns/llmfuzzer incluyen: 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.