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    LLM Security

    Web design & developmentGenAI Security, security of LLM applications
    Reading time: 2 min.Editorially reviewed on:
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    Definition

    LLM Security includes protection of models, prompts, data, tools, and integrations against abuse and misconduct.

    Risks range from prompt injection and data leakage to excessive agent privileges, insecure outputs, and supply chain issues. Security must consider system boundaries, not just the model.

    Simply explained

    How to secure not only the assistant but all data and tool accesses around it.

    Why it matters in practice

    Highly relevant for productive AI systems with enterprise data or actions.

    Example from business

    An agent receives read-only rights, inputs are treated separately from system instructions, and tool calls require approval when risks exist.

    Limits and pitfalls

    • Only use content filters; pass user rights to agents; consider prompt injection solved.

    Origin & history

    With productive LLM applications, dedicated security catalogs have emerged; OWASP maintains a top-10 risk list for GenAI/LLM applications.

    Use in mid-sized companies

    • Possible application in SMEs: combine threat modeling, least privilege, guardrails, logging, and red-teaming.

    Frequently asked questions

    Can a system prompt guarantee security?

    No. Is prompt injection only a text problem? No, it becomes critical especially with data and tool accesses.

    Sources

    Sources verified: 10./12.10.2026.

    Editorially reviewed on:

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