When AI Talks Too Much: Preventing Data Leaks from LLMs

Large Language Models (LLMs) like ChatGPT, Claude, and others are trained to respond helpfully, informatively, and conversationally. But sometimes, that helpfulness backfires—badly.

If you ask the right question in the right way, an LLM might reveal sensitive data or private system prompts. It may also disclose internal documents or secrets it was never meant to disclose. This is called sensitive information disclosure. It is one of the most dangerous risks identified in the OWASP Top 10 for LLM Applications.

AI is becoming more embedded into enterprise systems, chatbots, and SaaS tools. This kind of "AI oversharing" isn’t just embarrassing. It represents a potential data breach.

What Is Sensitive Information Disclosure in LLMs?

At its core, this risk refers to situations where the AI model outputs private, restricted, or internal information unintentionally. This can happen because the model:

• Was trained on sensitive data
• Has access to internal documents or systems
• Is operating without proper output filtering
• Is responding to cleverly crafted inputs (aka prompt injection)

The model may have been trained with the best intentions. However, it can still leak information. This happens if it hasn’t been properly controlled.

Real-World Examples of AI Data Leaks

1. Training Data Leakage
   Some LLMs trained on internet data have inadvertently revealed snippets of personal info. They have disclosed passwords or internal Slack messages. In one case, a researcher asked an LLM for example SSH keys. The researcher received a real one that had been in the training data.
2. System Prompt Exposure
   Attackers have tricked AI systems into revealing the hidden system prompt. These are essentially the instructions the model uses to stay safe. They also help it remain focused. Once that’s exposed, attackers can better craft jailbreak prompts.
3. Internal Document Retrieval
   Attackers can sometimes manipulate LLMs when they are connected to company data or APIs. This manipulation can lead to the leaking of content from internal reports, emails, or ticketing systems.
4. Conversational Memory Abuse
   If an AI remembers past interactions, it might “accidentally” leak something from one user to another. This is especially true if the memory isn’t user-specific.

Why This Is So Dangerous

Sensitive information disclosure isn’t just an AI bug—it’s a compliance and privacy risk. Depending on what’s exposed, you could violate:

• GDPR (if personal data is involved)
• HIPAA (if health data leaks)
• IP and trade secret policies
• Customer trust and confidentiality agreements

Worse, these leaks can happen silently. Unless you're actively monitoring outputs, you may never know your AI revealed something sensitive until it's too late.

How to Prevent LLMs from Leaking Data

Here’s what developers and security teams can do to stop these kinds of leaks:

1. Filter Outputs

Use strong content moderation and output filtering. Check for keywords and sensitive data patterns like credit cards or passwords. Ensure unexpected internal responses are evaluated before showing them to users.

2. Limit Training Data Exposure

Never train an LLM on confidential or regulated data unless you can guarantee it won’t be reproduced. Use anonymization, redaction, and data minimization best practices.

3. Don’t Trust Plugins and Connectors

If your LLM has access to APIs, databases, or tools, restrict what it can retrieve and display. Log all access and responses from third-party sources.

4. Use Role-Based Separation

Don’t let the LLM access information that the user shouldn’t see. Implement strict access controls between data sources and the AI.

5. Red Team for Disclosure

Test your chatbot or AI system like an attacker would. Can it be tricked into revealing private info? Are system prompts or internal variables easy to extract?

Conclusion

Large language models are good at talking—but that’s exactly why they can get into trouble. A simple request or clever prompt might expose sensitive business logic, internal knowledge, or even user data.

As AI continues to move from lab experiments to real-world apps, preventing AI data leaks must become a top priority. Don’t let your chatbot become your biggest compliance risk. Teach your AI to talk carefully—before it says too much.