What is Prompt Leaking?
Prompt Leaking is a security vulnerability where attackers extract hidden system instructions, proprietary prompts, or confidential configuration details from an AI system by crafting specific inputs designed to make the AI reveal its underlying instructions.
What is Prompt Leaking?
Prompt Leaking is a type of attack against AI systems, particularly large language models, where an adversary manipulates the system into revealing its hidden instructions, system prompts, or confidential configuration information. These system prompts typically contain instructions that define the AI's behaviour, persona, capabilities, limitations, and sometimes sensitive business logic or proprietary information.
When organisations deploy AI chatbots, customer service agents, or other AI-powered interfaces, they often include system prompts that guide the AI's behaviour. These prompts might contain brand voice guidelines, restricted topics, pricing logic, internal policies, or other information the organisation does not intend to share publicly. Prompt leaking attacks attempt to expose this hidden information.
How Prompt Leaking Works
Direct Extraction Attempts
The simplest form of prompt leaking involves directly asking the AI to reveal its instructions. While most AI systems are configured to refuse such requests, attackers use various techniques to bypass these protections:
- Role-playing requests: Asking the AI to pretend it is a different system that would reveal its instructions
- Hypothetical framing: Requesting the AI to describe what its instructions might look like if it had any
- Translation tricks: Asking the AI to translate its instructions into another language
- Encoding requests: Asking the AI to encode its instructions in base64, reverse text, or other formats
Indirect Extraction
More sophisticated attacks involve gradually extracting information about system prompts through indirect questioning:
- Boundary testing: Asking questions designed to reveal the boundaries and rules encoded in the system prompt
- Behavioural analysis: Observing how the AI responds to different inputs to infer what instructions it has received
- Comparison attacks: Asking the AI to compare its behaviour to hypothetical instructions, revealing details through its responses
Multi-Turn Exploitation
Attackers may use extended conversations to gradually extract prompt information, with each question building on knowledge gained from previous responses. Over multiple interactions, enough fragments can be gathered to reconstruct substantial portions of the system prompt.
Risks of Prompt Leaking
Competitive Intelligence Exposure
System prompts often contain proprietary business logic, competitive strategies, or unique approaches that differentiate a product. If exposed, competitors can replicate your AI's behaviour without investing in their own development.
Security Vulnerability Discovery
System prompts may reference security measures, content filters, or restricted topics. Exposing these details gives attackers a roadmap for circumventing your AI's safety measures through more targeted prompt injection attacks.
Confidential Information Disclosure
In some cases, system prompts contain references to internal systems, API endpoints, database structures, pricing tiers, or other operational details that should not be publicly accessible.
Brand and Reputation Risk
System prompts sometimes contain instructions that, while operationally necessary, could appear problematic if taken out of context. Leaked prompts have generated negative publicity when internal guidelines about content restrictions or response strategies were made public.
Defending Against Prompt Leaking
Principle of Least Information
Include only the minimum necessary information in system prompts. Avoid embedding sensitive business logic, API keys, internal system references, or confidential policies directly in prompts. Instead, use external systems and APIs to provide this information dynamically.
Layered Defence
Do not rely solely on instructing the AI to refuse prompt disclosure requests. Implement multiple layers of protection:
- Input filtering: Detect and block common prompt extraction patterns before they reach the AI model
- Output filtering: Scan AI responses for content that resembles system prompt text before delivering to users
- Instruction separation: Keep the system prompt distinct from user-facing content in ways that make extraction harder
Regular Testing
Conduct regular security testing specifically targeting prompt leaking. Assign internal security teams or external specialists to attempt prompt extraction using known techniques and creative approaches. Use the findings to strengthen your defences.
Monitoring and Alerting
Implement monitoring that detects patterns of behaviour associated with prompt extraction attempts. Multiple users asking similar probing questions, unusual input patterns, or conversations that follow known extraction methodologies should trigger alerts for security review.
Prompt Design Best Practices
Design system prompts with the assumption that they may eventually be exposed:
- No secrets in prompts: Never include API keys, passwords, or other secrets in system prompts
- No embarrassing content: Ensure all instructions would be defensible if made public
- Compartmentalisation: Separate different aspects of AI behaviour across different system components rather than encoding everything in a single prompt
- Version control: Track changes to system prompts to quickly identify what information may have been exposed
Prompt Leaking in Southeast Asian Business Contexts
For organisations in Southeast Asia deploying customer-facing AI systems:
- Multi-language considerations: Prompt leaking attacks can be conducted in any language the AI supports. Ensure your defences work across all languages your system handles, not just English.
- Regulatory implications: Data protection regulations across ASEAN may apply to information contained in system prompts, particularly if prompts reference personal data handling procedures or contain customer segmentation logic.
- Vendor assessment: When using third-party AI services, understand how the vendor protects system prompts and what liability they accept for prompt leaking incidents.
The Evolving Landscape
Prompt leaking techniques continue to evolve as AI systems become more sophisticated. New attack methods emerge regularly, and what works today may be mitigated tomorrow, only for new vulnerabilities to be discovered. This is an area that requires ongoing attention rather than a one-time fix.
Research into more robust prompt protection methods is active, including approaches that architecturally separate system instructions from user interactions in ways that make extraction fundamentally more difficult. Business leaders should stay informed about developments in this space and plan for regular updates to their AI security posture.
Prompt Leaking represents a direct threat to intellectual property and operational security for any organisation deploying AI systems with proprietary instructions. System prompts often encode competitive advantages, business rules, and operational guidelines that have significant value.
For CEOs and CTOs in Southeast Asia, this risk is particularly relevant as more organisations deploy customer-facing AI systems across multiple markets. A leaked system prompt could expose pricing strategies, content moderation rules, competitive positioning, or customer segmentation logic to competitors or the public.
The reputational risk is equally significant. Leaked prompts have generated negative press coverage for major technology companies when internal instructions were perceived as manipulative or contrary to public statements. Treating prompt security as a genuine information security concern, rather than an afterthought, protects both competitive position and brand reputation.
- Treat system prompts as confidential assets and apply information security principles to their creation, storage, and deployment.
- Never include sensitive information such as API keys, internal system references, or confidential business logic directly in system prompts.
- Implement layered defences including input filtering, output scanning, and architectural separation of system instructions from user interactions.
- Conduct regular prompt leaking assessments as part of your AI security testing programme, covering all languages your system supports.
- Design system prompts with the assumption that they may eventually be exposed, ensuring all content would be defensible if made public.
- Monitor AI interactions for patterns that indicate prompt extraction attempts and implement alerting mechanisms for security teams.
- Review third-party AI vendor contracts to understand their obligations regarding prompt protection and liability for leaking incidents.
Frequently Asked Questions
How is prompt leaking different from prompt injection?
Prompt injection involves inserting malicious instructions into an AI system to alter its behaviour, such as making it bypass safety rules or perform unintended actions. Prompt leaking is about extracting information from the AI system, specifically its hidden system instructions. While related, they have different objectives: injection aims to control the AI, while leaking aims to reveal its configuration. Both are important security concerns, and defences against one do not necessarily protect against the other.
Can prompt leaking be completely prevented?
Complete prevention is extremely difficult with current technology. Even sophisticated defences can be circumvented by novel attack techniques. The practical approach is defence in depth: combine multiple protective measures, minimise the sensitivity of information in prompts, design prompts to be defensible if exposed, and monitor for extraction attempts. Organisations should operate under the assumption that determined attackers may eventually extract some prompt information and design their systems accordingly.
More Questions
Treat it as a security incident. First, assess what information was exposed and its sensitivity. If the prompt contained credentials or secrets, rotate them immediately. Evaluate whether the leaked information enables further attacks against your AI system or other infrastructure. Update the system prompt to remove or change any compromised elements. Review and strengthen your prompt leaking defences. Finally, communicate with affected stakeholders as appropriate and document the incident for future reference.
Need help implementing Prompt Leaking?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how prompt leaking fits into your AI roadmap.