---
reference_key: owasp-llm02
title: "OWASP LLM02 --- Sensitive Information Disclosure"
url: "https://genai.owasp.org/llmrisk/llm022025-sensitive-information-disclosure/"
availability: available
capture_method: primary
captured_at: 2026-06-23T03:32:09Z
document_id: REF-owasp-llm02
---

# OWASP LLM02 --- Sensitive Information Disclosure

| Field | Value |
|-------|-------|
| **Reference key** | `owasp-llm02` |
| **Availability** | AVAILABLE |
| **Capture method** | `primary` |
| **Source type** | remote_url |
| **URL / path** | https://genai.owasp.org/llmrisk/llm022025-sensitive-information-disclosure/ |
| **Captured (UTC)** | 2026-06-23T03:32:09Z |
| **Content type** | text/html; charset=UTF-8 |
| **HTTP status** | 200 |

## Boardroom Citation Context

Sensitive information disclosure in LLM outputs.

## Source Location

https://genai.owasp.org/llmrisk/llm022025-sensitive-information-disclosure/





## Captured Content

```text
LLM02:2025 Sensitive Information Disclosure - OWASP Gen AI Security Project Skip to content Join us in London, 6/2  6/4 InfoSecurity Europe  OWASP GenAI and Agentic Security Summit | Register Now! GETTING STARTED Introduction MEETINGS CONTRIBUTING EVENTS GLOSSARY RESOURCES All LLM TOP 10 LLM TOP 10 FOR 2025 LLM TOP 10 FOR 2023/24 CHEAT SHEETS WHITEPAPERS TOOLS LEARNING VIDEOS SOLUTIONS DIRECTORY ROADMAP NEWSLETTER PROJECT INITIATIVES AI Security Landscape AIBOM Generator GOVERNANCE CHECKLIST Threat Intelligence AGENTIC APP SECURITY Secure AI Adoption AI Red Teaming Data Security BLOG ABOUT Mission and Charter Governance LEADERSHIP INDUSTRY RECOGNITION CONTRIBUTORS SPONSORS SUPPORTERS SPONSORSHIP NEWSROOM CONTACT BRANDING LLM02:2025 Sensitive Information Disclosure Sensitive information can affect both the LLM and its application context. This includes personal identifiable information (PII), financial details, health records, confidential business data, security credentials, and legal documents. Proprietary models may also have unique training methods and source code considered sensitive, especially in closed or foundation models. LLMs, especially when embedded in applications, risk exposing sensitive data, proprietary algorithms, or confidential details through their output. This can result in unauthorized data access, privacy violations, and intellectual property breaches. Consumers should be aware of how to interact safely with LLMs. They need to understand the risks of unintentionally providing sensitive data, which may later be disclosed in the models output. To reduce this risk, LLM applications should perform adequate data sanitization to prevent user data from entering the training model. Application owners should also provide clear Terms of Use policies, allowing users to opt out of having their data included in the training model. Adding restrictions within the system prompt about data types that the LLM should return can provide mitigation against sensitive information disclosure. However, such restrictions may not always be honored and could be bypassed via prompt injection or other methods. Common Examples of Vulnerability 1. PII Leakage Personal identifiable information (PII) may be disclosed during interactions with the LLM. 2. Proprietary Algorithm Exposure Poorly configured model outputs can reveal proprietary algorithms or data. Revealing training data can expose models to inversion attacks, where attackers extract sensitive information or reconstruct inputs. For instance, as demonstrated in the Proof Pudding attack (CVE-2019-20634), disclosed training data facilitated model extraction and inversion, allowing attackers to circumvent security controls in machine learning algorithms and bypass email filters. 3. Sensitive Business Data Disclosure Generated responses might inadvertently include confidential business information. Prevention and Mitigation Strategies ###@ Sanitization: 1. Integrate Data Sanitization Techniques Implement data sanitization to prevent user data from entering the training model. This includes scrubbing or masking sensitive content before it is used in training. 2. Robust Input Validation Apply strict input validation methods to detect and filter out potentially harmful or sensitive data inputs, ensuring they do not compromise the model. ###@ Access Controls: 1. Enforce Strict Access Controls Limit access to sensitive data based on the principle of least privilege. Only grant access to data that is necessary for the specific user or process. 2. Restrict Data Sources Limit model access to external data sources, and ensure runtime data orchestration is securely managed to avoid unintended data leakage. ###@ Federated Learning and Privacy Techniques: 1. Utilize Federated Learning Train models using decentralized data stored across multiple servers or devices. This approach minimizes the need for centralized data collection and reduces exposure risks. 2. Incorporate Differential Privacy Apply techniques that add noise to the data or outputs, making it difficult for attackers to reverse-engineer individual data points. ###@ User Education and Transparency: 1. Educate Users on Safe LLM Usage Provide guidance on avoiding the input of sensitive information. Offer training on best practices for interacting with LLMs securely. 2. Ensure Transparency in Data Usage Maintain clear policies about data retention, usage, and deletion. Allow users to opt out of having their data included in training processes. ###@ Secure System Configuration: 1. Conceal System Preamble Limit the ability for users to override or access the systems initial settings, reducing the risk of exposure to internal configurations. 2. Reference Security Misconfiguration Best Practices Follow guidelines like OWASP API8:2023 Security Misconfiguration to prevent leaking sensitive information through error messages or configuration details. (Ref. link: OWASP API8:2023 Security Misconfiguration ) ###@ Advanced Techniques: 1. Homomorphic Encryption Use homomorphic encryption to enable secure data analysis and privacy-preserving machine learning. This ensures data remains confidential while being processed by the model. 2. Tokenization and Redaction Implement tokenization to preprocess and sanitize sensitive information. Techniques like pattern matching can detect and redact confidential content before processing. Example Attack Scenarios Scenario #1: Unintentional Data Exposure A user receives a response containing another users personal data due to inadequate data sanitization. Scenario #2: Targeted Prompt Injection An attacker bypasses input filters to extract sensitive information. Scenario #3: Data Leak via Training Data Negligent data inclusion in training leads to sensitive information disclosure. Reference Links Lessons learned from ChatGPTs Samsung leak : Cybernews AI data leak crisis: New tool prevents company secrets from being fed to ChatGPT : Fox Business ChatGPT Spit Out Sensitive Data When Told to Repeat Poem Forever : Wired Using Differential Privacy to Build Secure Models : Neptune Blog Proof Pudding (CVE-2019-20634) AVID ( moohax & monoxgas ) Related Frameworks and Taxonomies Refer to this section for comprehensive information, scenarios strategies relating to infrastructure deployment, applied environment controls and other best practices. 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 Share this: Share on X (Opens in new window) X Share on Facebook (Opens in new window) Facebook More Print (Opens in new window) Print Email a link to a friend (Opens in new window) Email Share on X (Opens in new window) X LLM Top 10 LLM01:2025 Prompt Injection A Prompt Injection Vulnerability occurs when user prompts alter the LLMs behavior or output in unintended ways. These inputs... LLM02:2025 Sensitive Information Disclosure Sensitive information can affect both the LLM and its application context. This includes personal identifiable information (PII), financial details,... LLM03:2025 Supply Chain LLM supply chains are susceptible to various vulnerabilities, which can affect the integrity of training data, models, and deployment... LLM04:2025 Data and Model Poisoning Data poisoning occurs when pre-training, fine-tuning, or embedding data is manipulated to introduce vulnerabilities, backdoors, or biases. This manipulation... LLM05:2025 Improper Output Handling Improper Output Handling refers specifically to insufficient validation, sanitization, and handling of the outputs generated by large language models... LLM06:2025 Excessive Agency An LLM-based system is often granted a degree of agency by its developer  the ability to call functions... LLM07:2025 System Prompt Leakage The system prompt leakage vulnerability in LLMs refers to the risk that the system prompts or instructions used to... LLM08:2025 Vector and Embedding Weaknesses Vectors and embeddings vulnerabilities present significant security risks in systems utilizing Retrieval Augmented Generation (RAG) with Large Language Models... LLM09:2025 Misinformation Misinformation from LLMs poses a core vulnerability for applications relying on these models. Misinformation occurs when LLMs produce false... LLM10:2025 Unbounded Consumption Unbounded Consumption refers to the process where a Large Language Model (LLM) generates outputs based on input queries or... PRIVACY NEWSLETTER CONTACT Overview LLM Top 10 Initiatives Landscape Glossary Roadmaps Resources Sponsors Sponsorship Contribute Newsroom Events Meetings Leadership Volunteer Jobs OWASP and the OWASP logo are trademarks of the OWASP Foundation, Inc. Unless otherwise specified, all content on the site is Creative Commons Attribution-ShareAlike v4.0 and provided without warranty of service or accuracy. For more information, please refer to our General Disclaimer .  Copyright  2025 OWASP Foundation, Inc. Scroll to Top LLM02:2025 Sensitive Information Disclosure
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*Archived reference article for AICSR-STUDY-2026-001 footnote corpus.*
