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Prompt Leak

What Is Prompt Leak?

Prompt leak is a specific outcome of prompt injection where a model is manipulated into revealing its system prompt or internal instructions. As AI applications and agents rely on increasingly elaborate system prompts, tool definitions, and orchestration logic to function, any unintentional disclosure of that configuration exposes what is effectively proprietary IP and gives an attacker a blueprint for more targeted attacks. A leaked prompt can also be embarrassing on its own if it reveals instructions the organization would rather not have public, making this as much a reputational risk as a technical one.

Key Concerns

  • Intellectual Property Disclosure: preventing the unauthorized revelation of proprietary system prompts and configuration.
  • Reconnaissance for Further Attacks: a leaked prompt gives attackers a blueprint for more damaging, targeted injections.
  • Brand Reputation Damage: protecting against fallout from an exposed prompt that reveals embarrassing or sensitive instructions.

FAQ

Credentials, API keys, or anything else that would cause real harm if exposed. Assume the system prompt is readable under the right conditions.


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Related Terms


Prompt Injection

Prompt injection is an attack where crafted input causes a large language model to deviate from its intended instructions and follow the attacker's instead.

Jailbreak

Jailbreaking is a category of prompt injection focused on getting a model to ignore its safety training and guardrails rather than hijacking it for a specific downstream action.

Related Resources

OWASP LLM Top 10: Key Security Risks for GenAI and LLM Apps

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Nov 10th, 2024

Review the OWASP LLM Top 10 list to understand the top security risks for GenAI and LLM applications. Learn key threats, examples & mitigation strategies.

LLM Jailbreak: Understanding Many-Shot Jailbreaking Vulnerability

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Apr 3rd, 2024

LLM jailbreak attacks like many-shot jailbreaking exploit large language models. Prompt explains risks, examples, and defenses against these vulnerabilities.

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