Llama 4 profile

    Llama 4 — security & risk dashboard

    Known weaknesses, safety and security advisories, and documented real-world incidents for Llama 4 — collected automatically and re-checked every few hours, so teams building on it can see what to design around.

    Last checked 20 Oct 2026

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    Known flaws

    5

    Security risks

    4

    Real-world incidents

    0

    Advisories logged

    0

    Security, safety & abuse risks

    • Outputs can be inaccurate or fabricated; consequential factual claims require independent verification.
    • Safety tuning does not guarantee prevention of harmful outputs, jailbreaks or instruction-following failures.
    • Self-hosted deployments require application-specific safeguards, security testing and abuse monitoring.
    • Connecting the model to tools or confidential data requires independent access controls and validation of model-generated actions.

    Known flaws & limitations

    • Performance and safety outside the twelve explicitly supported languages require additional validation.
    • Sparse activation does not eliminate storage and memory requirements for approximately 109B total Scout parameters or 400B Maverick parameters.
    • The custom license restricts use and distribution, including multimodal-model grants to EU-based individuals and companies, with an end-user exception.
    • Generates text rather than native audio, video or image outputs.
    • The documented context window is a capacity specification, not a guarantee of reliable retrieval or reasoning across every token.

    Security advisories & risk updates

    No risk advisories logged since this profile started being tracked. New ones appear here automatically.

    What this means if you are building on it

    • Unsupervised medical, legal, financial or other consequential decision-making.
    • Applications requiring native image, audio or video generation.
    • Deployments requiring guaranteed factual accuracy or reliable reasoning across every token of the maximum context.
    • Resource-constrained deployments that cannot accommodate the full model weights and context-related memory demands.

    Everything here comes from published documentation, advisories and reporting, with links to the original sources. It is not a complete vulnerability database — always check the provider's own security notices before shipping.