LLM02:2025 – Sensitive Information Disclosure and How to Prevent It

As LLMs are increasingly embedded into applications, the risk of sensitive information disclosure rises. Dynamically generated responses can expose internal data, user context, or retrieved records when proper controls are missing.

In 2025, thousands of private Grok AI conversations were unintentionally indexed by Google Search due to a configuration vulnerability, making sensitive queries publicly accessible until the vulnerability was fixed.

LLM02:2025 – Sensitive Information Disclosure in OWASP Top 10 LLM, highlights how LLM-based systems can unintentionally leak confidential or regulated data, leading to privacy violations, compliance failures, and security risks.

LLM02:2025 Sensitive Information Disclosure

Sensitive Information Disclosure refers to the risk of large language models unintentionally exposing confidential, personal, or proprietary data through their outputs. This can occur when prompts, retrieved content, logs, or internal context are processed without strict access controls, redaction, or output filtering. Because LLM responses are often trusted and consumed by downstream systems, even a single disclosure can lead to privacy violations, regulatory non-compliance, intellectual property leakage, or broader security compromise.

Why LLM02:2025 Sensitive Information Disclosure Matters

Sensitive information disclosure is a critical risk in real-world LLM deployments, where dynamically generated outputs can amplify even small control gaps into significant security, compliance, and operational issues.

• LLM outputs are difficult to fully control: Responses are generated by combining training data, runtime context, and user input. This makes sensitive information disclosure harder to predict and harder to prevent using traditional application security controls.
• Sensitive data can originate from both users and systems: Personal identifiable information, financial data, health records, legal documents, credentials, and confidential business data may enter LLM workflows through normal usage, increasing the risk of later exposure through outputs.
• Exposed data spreads beyond the initial interaction: Once sensitive information appears in an output, it may be stored, forwarded, logged, or reused by downstream systems and users, making containment and remediation significantly more complex.
• Regulatory and compliance risks escalate quickly: Even limited disclosure of regulated or confidential data can trigger violations of data protection laws, contractual obligations, and internal governance policies.
• Disclosure weakens security posture: Leaked information can expose internal system behavior, proprietary algorithms, or credentials, enabling attackers to conduct follow-on attacks or bypass existing controls.
• Most disclosures occur silently: Sensitive information often appears in valid-looking responses without generating alerts or errors, allowing exposure to persist unnoticed in production environments.

How Sensitive Information Disclosure Occurs in LLM-Based Systems

Sensitive information disclosure in LLM systems typically results from gaps in how data is handled across training, inference, and configuration. These vulnerabilities often remain invisible until information appears in model responses.

1. Training Data Memorization: When sensitive records are present in training data, the model may retain and later reproduce fragments of that information. This recall can be triggered by specific prompts and is difficult to predict, since it depends on how patterns were learned during training rather than on direct data access.
2. Unsafe Use of Runtime Context: LLM applications often pass live context from databases, documents, or APIs into prompts. If this data is not properly filtered, the model can include confidential or regulated information in its response. Because this context is treated as input, disclosure may occur without clear system errors.
3. Prompt Manipulation: System prompts and safety instructions guide model behavior. They do not enforce hard limits Carefully crafted inputs can weaken these controls, allowing the model to return information it was meant to withhold, even in otherwise well-configured systems.
4. Exposure of Proprietary Details: Poorly constrained outputs can reveal internal model logic, training artifacts, or proprietary algorithms. Over time, this information can be used to infer model behavior or extract sensitive intellectual property.
5. Configuration Weaknesses: Misconfigurations such as exposed system prompts, verbose error messages, or broad internal access frequently enable disclosure. These vulnerabilities often persist in production, leading to repeated exposure without deliberate exploitation.

How to Prevent or Mitigate LLM02:2025 Sensitive Information Disclosure

Preventing sensitive information disclosure in LLM applications requires consistent controls across data, access, configuration, and monitoring, applied before data reaches the model and maintained throughout its operation.

1. Data Sanitization and Input Validation: Sensitive information should be removed, masked, tokenized, or redacted before it is used for training or passed into the model during inference. Strong input validation must also be applied to detect and block sensitive or harmful content before it reaches the model, reducing the risk of confidential data being learned or disclosed through prompts or contextual inputs.
2. Access Control and Data Source Restriction: Model access to data should follow strict least-privilege principles, limiting interaction to only what is necessary for the intended function. External APIs, document repositories, and runtime data sources must be tightly controlled to prevent unintended leakage through loosely managed or overly broad integrations.
3. Privacy-Preserving Learning Techniques: Federated learning can reduce centralized data exposure by keeping training data distributed across locations, lowering the impact of large-scale leaks. Differential privacy further limits disclosure by adding controlled noise to data or outputs, making it difficult to reconstruct individual records from model responses.
4. Secure System Configuration: System prompts, preambles, and internal instructions must be protected from user access or override. Configuration hardening is equally important, including suppressing verbose error messages and avoiding exposed settings by following established security misconfiguration best practices such as OWASP API security guidance.
5. User Education and Transparency: Users should be clearly guided on safe interaction practices, including avoiding the submission of sensitive information. At the same time, organizations must maintain transparency around data usage, retention, and deletion, and provide opt-out mechanisms for including user data in training processes.
6. Output Monitoring and Detection: Model outputs should be continuously monitored for unexpected sensitive data, abnormal response patterns, or signs of prompt-based extraction. Early detection and response help contain isolated disclosures before they escalate into broader security or compliance incidents.

Securing with AppTrana AI Shield

AppTrana AI Shield helps reduce the risk of sensitive information disclosure by adding enforcement and visibility at the interaction layer where LLM risks surface. It continuously inspects inputs, contextual data, and generated responses for exposure patterns, applies policy-based controls to limit unsafe behavior, and detects prompt-driven attempts to extract protected information. By combining real-time monitoring with configuration hardening and managed oversight, AppTrana AI Shield helps ensure that sensitive data remains contained even as LLM-powered features are deployed at scale.