LLM02:2025 Sensitive Information Disclosure
LLMs risk exposing sensitive data, proprietary algorithms, or confidential details through their output, resulting in unauthorized data access, privacy violations, and intellectual property breaches.
Sensitive information can affect both the LLM and its application context, including PII, financial details, health records, confidential business data, security credentials, and legal documents.
Impact Level
HighData at Risk
PII, Business Data, Algorithms
Attack Vector
Model Output
1. PII Leakage
Personal identifiable information (PII) may be disclosed during interactions with the LLM, exposing names, addresses, social security numbers, or other sensitive personal data.
Example: User asks for help with a document, and the LLM accidentally reveals another user's personal information that was in its training data.
2. Proprietary Algorithm Exposure
Poorly configured model outputs can reveal proprietary algorithms or data. This can expose models to inversion attacks where attackers extract sensitive information.
Example: The 'Proof Pudding' attack (CVE-2019-20634) demonstrated how disclosed training data facilitated model extraction and inversion attacks.
3. Sensitive Business Data Disclosure
Generated responses might inadvertently include confidential business information, trade secrets, or internal processes.
Example: A customer service chatbot accidentally reveals internal pricing strategies or unreleased product information.