LLM02:2023 - Data Leakage

Description:
Data leakage occurs when an LLM accidentally reveals sensitive information, proprietary algorithms, or other confidential details through its responses. This can result in unauthorized access to sensitive data or intellectual property, privacy violations, and other security breaches.

Common Data Leakage Vulnerabilities:

• Incomplete or improper filtering of sensitive information in the LLM’s responses.
• Overfitting or memorization of sensitive data in the LLM’s training process.
• Unintended disclosure of confidential information due to LLM misinterpretation or errors.

How to Prevent:

• Implement strict output filtering and context-aware mechanisms to prevent the LLM from revealing sensitive information.
• Use differential privacy techniques or other data anonymization methods during the LLM’s training process to reduce the risk of overfitting or memorization.
• Regularly audit and review the LLM’s responses to ensure that sensitive information is not being disclosed inadvertently.
• Monitor and log LLM interactions to detect and analyze potential data leakage incidents.

Example Attack Scenarios: Scenario #1: A user inadvertently asks the LLM a question that could reveal sensitive information. The LLM, lacking proper output filtering, responds with the confidential data, exposing it to the user.

Scenario #2: An attacker deliberately probes the LLM with carefully crafted prompts, attempting to extract sensitive information that the LLM has memorized from its training data.

By understanding and addressing the risks associated with data leakage, developers can better protect their LLM implementations and ensure the safety and security of their systems.