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Glossary

AI Penetration Testing

What is AI Penetration Testing?

An AI penetration test is a commissioned, controlled security assessment in which specialists deliberately probe an AI system for weaknesses, such as prompt injection, leakage of confidential data or an agent holding excessive permissions. Unlike broader red teaming, it focuses on a clearly defined scope with documented findings and remedies. It is worth carrying out before a chatbot goes live and after major changes.

How KI.ordo helps

KI.ordo's “Chatbot security audit” template helps you review an agent's instructions against the OWASP risk areas beforehand; it does not replace a professional penetration test.

Learn more →: AI Penetration Testing

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Topics

  • Security
  • Assessment
  • Vulnerabilities

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Related terms

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.

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Chatbot security audit

A chatbot security audit is a structured review of whether an AI chatbot adequately protects confidential data, its internal instructions and its permissions to act. It examines, among other things, susceptibility to prompt injection, unintended disclosure of information and overly broad access to tools – often following the OWASP Top 10 for LLM Applications. The result is a report with a rating per risk area and concrete measures.

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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.

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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.

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Template