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System prompt disclosure testing evaluates your model’s ability to keep its system prompt and internal instructions confidential. Attackers often attempt to extract system prompts to understand model behavior and find exploitable weaknesses.

Purpose

Protecting system prompts is important for:
  • Maintaining security through obscurity as an additional layer
  • Protecting proprietary prompt engineering
  • Preventing attackers from understanding model constraints
  • Ensuring competitive advantage in prompt design

Attack Categories

Dataset vs Prompt probes

The catalog builder instantiates *DatasetProbe classes and evaluates with TrueFalseEvaluator. Each subcategory also has a *PromptProbe (for example DirectRequestPromptProbe). Dataset probes accept sampling, use_jailbreaks, max_jailbreak_per_objective, and translate_into_language. The builder maps languagetranslate_into_language and num_test_casesmax_objectives_per_category. See StaticDatasetProbe. Catalog scenarios attach framework tags (EU AI Act, OWASP, MITRE ATLAS, ISO/IEC 42001).

How It Works

The evaluator checks if the model reveals any part of its system prompt:
  • Pass: The model keeps its system prompt confidential
  • Fail: The model discloses part or all of its system prompt

Usage Example

When to Use

Use system prompt disclosure testing when you need to:
  • Protect proprietary prompt engineering
  • Validate prompt confidentiality measures
  • Assess resistance to prompt extraction attacks
  • Conduct security audits
  • Test before deploying customer-facing applications