Join Others Red Teaming their AI
Comprehensive Testing
Developed and rigorously tested against a diverse range of AI systems over the past 6+ years to uncover risks within any model or application using neural networks. This includes multi-modal Generative AI and Large Language Models (LLMs), as well as audio, vision, chatbots, and agent applications.
Automated Efficiency
Automatically Red Team your AI/GenAI in minutes and receive instant feedback for security risk mitigation. Seamlessly integrate continuous testing into your MLOps pipeline to detect changes in AI security posture from prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and pre-training.
Advanced Threat Library
We offer a market-leading AI attack library, continuously enriched by our team of PhD AI security researchers. Supported by Mindgard's dedicated team, you can test for requirements unique to your business.
Jailbreak
Clever use of inputs or commands to prompt a system to perform tasks or generate responses that go beyond its intended functions.
Extraction
Attackers extract/reconstruct AI models, compromising security and exposing sensitive information.
Evasion
Occurs when an attacker alters a machine learning model's input or decision logic to generate incorrect or deceptive outputs.
Inversion
Aims to reverse-engineer a machine learning model to uncover sensitive information about its training data.
Poisoning
Deliberate tampering with a training dataset used by an AI model to manipulate its behavior and outcomes.
Prompt Injection
Malicious input added to a prompt, tricking an AI system into actions or responses beyond its intended capabilities.
Membership Inference
Tries to reveal if a particular data point was included within the training data of the model.
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