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Glossary

LLM security

What is LLM security?

LLM security covers all measures that protect applications built on large language models from misuse, data leakage and unwanted behaviour. Typical risks include prompt injection, disclosure of confidential instructions or data, and agents with excessive permissions. Protection comes from several layers: clear instructions, restricted rights, checks on inputs and outputs, AI firewalls, red teaming and regular audits.

How KI.ordo helps

In KI.ordo, roles from viewer to owner determine who may change agents and knowledge, a change log keeps edits traceable, and a chatbot security audit template supports reviews.

Learn more →: LLM security

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Topics

  • security
  • language models
  • risk management

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Related terms

AI Firewall

An AI firewall is a protective layer between users, data sources and an AI model. It screens inputs for manipulation attempts such as prompt injection or jailbreaks and checks outputs, for example for confidential data or unwanted content, before passing them on. It may use rules, classification models or a second language model. An AI firewall reduces risk but does not offer complete protection and replaces neither limited permissions nor testing.

  • Security
  • Prompt injection
  • +1
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AI red teaming

AI red teaming is the structured examination of an AI system from the perspective of potential attackers or misuse. Specialists test in a controlled way whether a chatbot reveals confidential instructions, can be led into unwanted behaviour, provides false facts or exposes data. The findings feed into better instructions, filters and permissions. A chatbot security audit is a lighter form: it assesses configuration and sample conversations against known risk areas such as the OWASP Top 10 for LLM Applications.

  • AI security
  • Security testing
  • +1
Template

Indirect Prompt Injection

In an indirect prompt injection, manipulative instructions are not in the user's input but hidden in content an AI system processes along the way, such as web pages, emails or documents. When an agent reads such content, it may mistake hidden commands for genuine tasks and, for example, disclose data. Safeguards include treating external content strictly as data, limiting permissions, screening inputs and requiring human approval for critical actions.

  • Security
  • Prompt injection
  • +1
Template

OWASP LLM Top 10

The OWASP Top 10 for LLM Applications is a list of the most important security risks in applications built on large language models, published by the OWASP GenAI Security Project. The current 2026 edition appeared in August 2026 (as of October 2026) and covers: prompt injection, sensitive information disclosure, excessive agency, supply chain, data and model poisoning, unbounded consumption, misinformation, hidden context exposure, vector and embedding weaknesses, and improper output handling. It serves as a checklist for development and audits.

  • security
  • standard
  • +1
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