Sensitive Data Disclosure over Leveraging LLM

Background: When leveraging agentic and non-agentic AI capabilities, especially when your solution is connected to your database, your organization can face another threat besides prompt injection: sensitive information disclosure. This attack is similar to prompt injection, but the key concern is the type of information the attacker is trying to expose. This could include trade secrets, business processes, PCI DSS–related data, or other sensitive information.

Attack Flow: Before implementing an LLM, you need to consider the following important points: all outbound communication should include data-masking capabilities to prevent the exposure of sensitive information. An attacker may use specially crafted prompts to try to retrieve this data. Then, the data passes through your security layers. If those layers determine that the request is not harmful, they may allow it to reach the centralized data store and retrieve the data. Example of Sensitive data exposure

And to prevent such data exposure just you need to put guardrail in your infrastructure

But if we have the data-masking layer, as we discussed, even if an attacker abuses prompts and successfully exfiltrates data, they will not be able to see the sensitive information because the data-masking layer worked in time.

Conclusion: Only leveraging LLMs is not enough to secure your organization. Even if you have done a great deal of hardening beforehand, once you start implementing them, your threat landscape changes because some traditional guardrails no longer work in the same way.

To reduce risks, always leverage DLP, provide clear instructions to the LLM, and follow data-masking requirements to prevent sensitive information disclosure.