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    0din by Mozilla

    Defense

    A runtime security classifier that evaluates incoming prompts for attack risk before they reach your LLM application.

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    Picari insights

    Engineering and security teams seeking a low-latency, privacy-preserving input guardrail to intercept prompt injections and jailbreaks before queries reach their LLMs.

    Best for
    • Privacy-conscious architectures requiring on-prem or local prompt screening
    • Real-time prompt injection and jailbreak prevention
    • Python-native and API-driven GenAI application security
    May not be ideal if
    • Comprehensive LLM observability and post-generation output governance
    • Teams seeking a fully managed all-in-one API gateway with routing and billing controls
    • Environments with zero tolerance for pre-inference classification latency

    Core capabilities

    Fine-tuned classifier trained on real-world attack data
    Local inference detection running entirely in-process by default
    Real-time prompt scoring for jailbreak and prompt injection detection
    Tunable sensitivity thresholds across application paths

    Common use cases

    01

    Gate or reject suspicious prompts before LLM processing

    02

    Log attack attempts for security monitoring

    03

    Protect critical application paths with strict thresholds

    Things to consider

    Every product has trade-offs, and which of them matter depends on your environment, your team and your timeline.

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    0din by Mozilla Defense pricing and integrations

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    Profile last updated on 6 September 2026 by Picari.