OWASP LLM Top 10: SensitiveInformationDisclosure Feedback


# LLM06_SensitiveInformationDisclosure_Feedback.md

## Strengths

1. **Clarity of the message**: The description of the vulnerability is well-explained and effectively highlights the risks associated with sensitive information disclosure in Large Language Models (LLMs). The author does a good job emphasizing the different types of sensitive information (PII, financial data, proprietary methods, etc.) and the potential consequences of its disclosure.

2. **Good use of examples**: The entry provides clear and specific examples of sensitive information (PII, proprietary algorithms) that can be disclosed. This helps to illustrate the problem in a way that’s easy for readers to understand.

3. **Concise prevention strategies**: The mitigation strategies outlined (data sanitization, Terms of Use policies, restricting the types of data LLMs return) are practical and actionable. This provides clear guidance to users and developers on how to address the issue.

4. **Relevant external resources**: The inclusion of resource links adds value by directing the reader to additional reading material on related topics, such as federated learning, tokenization, and side-channel attacks.

## Weaknesses & Areas for Improvement

1. **Overlong sentences and wordiness**: Some of the sentences are too long, which could confuse the reader. Breaking down these sentences will improve readability. For instance, the sentence starting with "Additionally, proprietary closed or foundation models..." could be split into shorter, more manageable sections.

2. **Inconsistent use of headings and subheadings**: While the entry does have some headings, it could benefit from clearer sectioning, especially under the **Prevention and Mitigation Strategies** and **Example Attack Scenarios** headings. Subheadings could help the reader better navigate the content.

3. **Unclear explanation of key concepts**: Certain technical phrases, such as “sandboxing” and “system prompt restrictions,” could use further clarification. Not all readers may be familiar with these terms, and a brief explanation or example would improve understanding.

4. **Missing numbered lists**: The entry would benefit from more structured formatting, particularly using numbered lists in the sections on **Common Examples of Vulnerability**, **Prevention and Mitigation Strategies**, and **Example Attack Scenarios**. This will enhance the readability and organization.

5. **Repetitive use of terms**: The phrase “Sensitive Information” is used multiple times in close proximity. Varying the wording (e.g., “confidential data,” “private information”) will improve flow and avoid redundancy.

## Suggestions for Improvement

1. **Sentence rephrasing for clarity**:
   - Original: “Sensitive information is contextually relevant to both the model and its deployment in LLM applications.”
     - Suggested: “Sensitive information can affect both the LLM and its application context.”
     
   - Original: “Both LLMs and when embedded within applications risk the potential to reveal sensitive information, proprietary algorithms, or other confidential details through their output.”
     - Suggested: “LLMs, especially when embedded in applications, risk exposing sensitive data, proprietary algorithms, or confidential details through their output.”

2. **Break down longer sentences**:
   - Original: “It is important for consumers of LLM applications to be aware of how to safely interact with LLMs and identify the risks associated with unintentionally inputting sensitive data that may be subsequently returned by the LLM in output elsewhere.”
     - Suggested: “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 model's output.”

3. **Use subheadings for better structure**:
   - Under the **Prevention and Mitigation Strategies** section, introduce subheadings like **Sanitization**, **Terms of Use Policies**, **System Prompt Restrictions**, etc., to make it easier to follow and to group related strategies together.

4. **Introduce numbered lists for clarity**:
   - Under **Common Examples of Vulnerability**, use a numbered list. For example:
     1. Personal Identifiable Information (PII) leakage during conversational interactions.
     2. Proprietary algorithm exposure due to poorly configured model outputs.
     3. Unintentional disclosure of sensitive business data in generated responses.

   - Similarly, in **Example Attack Scenarios**:
     1. An attacker queries the LLM using prompts designed to extract PII.
     2. The LLM inadvertently leaks confidential information during a public-facing Q&A session.

5. **Reduce redundancy**:
   - The phrase “sensitive information” is overused. Try replacing it with alternatives like “private data,” “confidential details,” or “sensitive content” to make the text more engaging.

## Conclusion

Overall, the entry is well-written and communicates the vulnerability effectively. By breaking down long sentences, using more structured formatting, and varying the vocabulary, the entry will become more accessible to readers. Numbered lists and subheadings will also improve clarity and help users follow the prevention and mitigation strategies more easily. The addition of more examples and explanations for less common terms will make the entry more universally understandable.