Azure/PyRIT
PyRIT
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 across multiple models and uses heuristic-based risk scoring to quantify attack success. It supports prompt-based adversarial generation, template-based payload injection, and stateful interaction loops for multi-turn simulations. A plugin-driven test suite allows for the integration of modular security checkers to target specific AI vulnerabilities.
Features
- Security Vulnerability Scanning - Provides an automated security scanner to detect vulnerabilities and potential exploits in generative AI systems.
- Multi-turn Interaction Managers - Manages stateful, multi-turn interactions to simulate complex social engineering attacks.
- LLM Vulnerability Scanners - Probes large language models for behavioral security flaws such as prompt injection and data leakage.
- Model Red-Teaming - Provides a framework for adversarial testing and vulnerability scanning to detect safety failures in AI models.
- AI Vulnerability Scanning - Employs automated scanners to identify security weaknesses in generative AI components.
- Adversarial Payloads - Utilizes template-based payload injection to evaluate the robustness of generative AI system prompts.
- Adversarial Input Generation - Generates adversarial inputs through iterative prompt refinement to bypass safety filters.
- AI Risk Assessments - Implements consistent and repeatable automated analysis to evaluate the security posture of generative AI systems.
- AI Security Assessment - Acts as a security analyzer for detecting vulnerabilities and risks specifically within AI infrastructure.
- Adversarial Red Teaming Toolkits - Functions as a red teaming toolkit to test model robustness against safety guardrails using adversarial techniques.
- AI Model Vulnerabilities - Identifies and manages security flaws and manipulation techniques specific to Large Language Models.
- Model Request Orchestrators - Implements an orchestrator to manage concurrent requests across multiple LLMs for security posture comparison.
- Criteria-Based Scoring Engines - Provides a scoring engine that evaluates model outputs against security criteria to quantify attack success.
- Modular Security Checkers - Ships a plugin-driven test suite for integrating modular security checkers that target specific AI vulnerabilities.
- AI Security - Orchestration tool for multi-turn AI red teaming.
- AI Security and Red Teaming - Python Risk Identification Toolkit for generative AI by Microsoft.
- Red Teaming and Security - Python-based tool for automating red-teaming and risk identification.
- Security & Privacy - Red-teaming framework for identifying risks in generative AI systems.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
