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Glossary

System Prompt Extraction

What is System Prompt Extraction?

System prompt extraction describes attempts to make a chatbot or AI agent reveal its hidden system instructions. If such an attempt succeeds, the result is called prompt leaking: extraction is the attack, the leak is the consequence. Effective protection starts with keeping passwords, keys and confidential rules out of instructions and assuming they may become public; input checks and regular testing add further safeguards.

How KI.ordo helps

The KI.ordo template “Chatbot security audit” reviews an agent's instructions for risks such as disclosed system instructions and provides harmless self-test questions.

Learn more →: System Prompt Extraction

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Topics

  • Security
  • Prompt leaking
  • Chatbots

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Related terms

Prompt leaking

Prompt leaking refers to the unintended disclosure of an AI system's internal instructions, for instance when a chatbot reveals its system prompt in response to a cleverly worded request. This becomes a problem if those instructions contain business logic, internal information or even credentials. The OWASP list for LLM applications names this risk explicitly. The most effective safeguard is to write instructions as if they could become public, never store secrets in them and check outputs as well.

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Jailbreaking

Jailbreaking refers to attempts to bypass an AI model's safety rules through cleverly worded inputs, so that it produces content or takes actions it is meant to refuse. For organisations this is a risk, because a chatbot could, for example, reveal confidential instructions or respond inappropriately. Protective measures include clear system instructions, input and output screening, limited permissions, logging and regular security testing (red teaming).

  • Security
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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.

  • security
  • audit
  • +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
Template