Prompt Leakage

Prompt leakage occurs when a large language model (LLM) inadvertently reveals parts of its underlying system prompt, configuration instructions, or internal logic in its output. These prompts often contain critical information that controls model behavior—such as tone, rules, safety guidelines, or role definitions.

System prompts are typically invisible to the end user but are appended behind the scenes to shape how the model responds. If these prompts are leaked, attackers or users may:

• Reverse-engineer the intended restrictions.
• Circumvent safety filters more effectively.
• Copy proprietary formatting, personality instructions, or policies.
• Conduct targeted prompt injection or jailbreak attacks.

Prompt leakage can happen through:

• Direct queries (e.g., “What were your initial instructions?”).
• Indirect prompting (e.g., asking for summaries or completions that reference hidden instructions).
• RAG systems that blend retrieved and system content poorly.
• Prompt chaining errors in multi-agent or orchestration setups.

This vulnerability is particularly risky in customer support bots, enterprise chat systems, and API-exposed LLM services where system prompts encode sensitive business logic or compliance instructions.

Mitigation strategies include:

• Obfuscating or embedding system instructions securely.
• Designing prompts to avoid exposure through language structure.
• Inspecting outputs for leakage patterns.
• Using access policies and output filtering at runtime.

How PointGuard AI Addresses This:PointGuard AI monitors for signs of prompt leakage in real time by analyzing output patterns and response structure. The platform flags any disclosure of hidden instructions or system directives and can automatically block or redact exposed content.

Resources:

OWASP LLM07:2025 System Prompt Leakage