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Ensure the robustness and safety of your models. We streamline your AI model evaluation by providing pre-categorized datasets that directly map to crucial AI safety concerns. Our collections are built by meticulously leveraging a wide array of reliable public open-source resources.

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Datasets

Below are the public open-source datasets we use within the platform:
  • Do Not Answer — License: Apache-2.0
    A curated set of prompts that responsible models should refuse to answer. Useful for evaluating refusal and filtering behavior.
  • Aegis / Nemotron Content Safety Dataset V2 — License: CC-BY-4.0
    33,416 annotated human–LLM interactions (30,007 train / 1,445 validation / 1,964 test). Contains diverse safety labels for fine-grained content-safety evaluation.
  • HarmBench — License: MIT
    Collection of benchmarks and datasets for measuring model harms. This project’s provided dataset is used in our tests.
  • CySecBench — License: MIT
    A cybersecurity-focused prompt dataset for benchmarking LLMs. ~12,662 prompts covering generative attack/defense scenarios and other cyber threats.

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Mapping

Below are the high‑level domains and the tag categories that map into each one. Use these domain names with mindgard (for example, mindgard test --domain toxicity or mindgard test --domain toxicity.harassment).
  • harmful - Harmful content
    • violence — physical harm, threats, or glorification of violence
    • dangerous_content — instructions or encouragement of hazardous activities
    • sexually_explicit — explicit sexual content
    • self_harm — instructions or encouragement of self-injury/suicide
    • illegal — content facilitating illegal acts
  • toxicity - Toxicity
    • hate_speech — targeted hostility
    • profanity — vulgar or offensive language
    • harassment — personal attacks, bullying, or abuse
    • discrimination — biased or exclusionary content
  • business_risk - Business risk
    • copyright — IP infringement, piracy, or unlicensed content
    • pii — exposure or request for personally identifiable information
  • cybersecurity - Malicious Planning & Code Generation
    • cyber_crime
    • cloud_attacks
    • control_system_attacks
    • cryptographic_attacks
    • evasion_techniques
    • hardware_attacks
    • intrusion_techniques
    • iot_attacks
    • malware_attacks
    • network_attacks
    • web_application_attacks
  • information_disorder - Information disorder
    • misinformation — false or misleading content shared without clear intent to deceive
    • disinformation — false content created or propagated with intent to deceive