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LLM02:2025 Sensitive Information Disclosure

Description​

Sensitive Information Disclosure Risk

Sensitive information disclosure in LLM systems represents a critical vulnerability that can affect both the model itself and its application context. The scope of sensitive data encompasses a wide range of information, from personal identifiable information (PII) and financial details to health records, confidential business data, security credentials, legal documents, and proprietary training methods and source code, particularly in closed or foundation models.

When LLMs are integrated into applications, they create potential pathways for exposing sensitive data, proprietary algorithms, or confidential details through their outputs. This exposure can lead to unauthorized data access, privacy violations, and intellectual property breaches. It's crucial for consumers to understand the inherent risks of providing sensitive data that may subsequently appear in model outputs. Application owners must establish clear Terms of Use policies and provide mechanisms for users to opt out of having their data included in training processes.

Vulnerability Patterns​

Personal Information Exposure​

The risk of PII leakage represents one of the most significant concerns in LLM deployments. These systems can inadvertently memorize and later disclose personal information during interactions, potentially violating privacy regulations and compromising individual security.

Algorithm and Training Exposure​

Poorly configured model outputs may reveal proprietary algorithms or training data, making systems vulnerable to inversion attacks. This exposure allows attackers to potentially extract sensitive information or reconstruct inputs. A notable example is the 'Proof Pudding' attack (CVE-2019-20634), where disclosed training data enabled both model extraction and inversion.

Business Intelligence Leakage​

LLM responses might inadvertently include confidential business information, potentially exposing trade secrets, strategic plans, or other sensitive corporate data that could advantage competitors or malicious actors.

Prevention and Mitigation Strategies​

Data Sanitization Framework​

Organizations must implement comprehensive data sanitization techniques to prevent user data from entering training sets and to effectively scrub or mask sensitive content before it reaches the model. This should be coupled with robust input validation methods that filter out potentially harmful or sensitive data inputs before processing.

Access Control Architecture​

A strong access control system should enforce the principle of least privilege, limiting access to only necessary data and functionality. This includes restricting data sources and ensuring secure runtime data orchestration to minimize potential exposure points.

Privacy-Preserving Learning Methods​

Organizations can leverage federated learning to train models using decentralized data, reducing the need for centralized data collection and minimizing exposure risks. Additionally, differential privacy techniques can be employed to add noise to data and outputs, making reverse-engineering attempts more challenging.

User Education and Transparency Program​

A comprehensive approach to user education should provide clear guidance on safe LLM usage and best practices for avoiding sensitive input. This must be paired with transparent data policies and straightforward opt-out mechanisms for training data inclusion.

Secure System Configuration Framework​

System security begins with concealing system preambles and limiting the ability to override settings. Organizations should follow established security best practices, including OWASP API Security guidelines, and implement measures to prevent information leakage through error messages.

Advanced Protection Mechanisms​

Modern security approaches can incorporate homomorphic encryption to enable secure data analysis while maintaining confidentiality during processing. Additionally, implementing sophisticated tokenization and redaction systems helps preprocess and sanitize sensitive information through pattern matching for confidential content.

Example Attack Scenarios​

Unintentional Cross-User Exposure​

In this scenario, a user receives a response containing another user's personal data due to insufficient data sanitization protocols. This type of breach often occurs when models retain information from previous interactions and fail to properly segment user data.

Targeted Information Extraction​

A sophisticated attacker successfully bypasses input filters through carefully crafted prompts, enabling the extraction of sensitive information stored within the model's parameters or accessible through its integrations.

Training Data Compromise​

Through negligent data handling practices during the training process, sensitive information becomes embedded in the model's knowledge base, leading to potential disclosure in future interactions. This scenario often results from inadequate data screening and sanitization procedures during the training phase.

  1. ChatGPT Samsung Leak Lessons
  2. Preventing Company Secrets from ChatGPT
  3. ChatGPT Data Leak via Poem Repetition
  4. Building Secure Models with Differential Privacy
  5. Proof Pudding Attack Details
  • OWASP Top 10 for LLM Applications
  • AML.T0024.000 - Infer Training Data Membership (MITRE ATLAS)
  • AML.T0024.001 - Invert ML Model (MITRE ATLAS)
  • AML.T0024.002 - Extract ML Model (MITRE ATLAS)