Nobody’s Safe from LLM Prompt Injection

Author(s): Tim Cvetko

Originally published on Towards AI.

Here’s how to Fight Back

I’m sure you’ve heard of the SQL injection attack. SQL injection occurs when an attacker injects malicious SQL code into fields or parameters used by a front-facing application.

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For example, the code snippet above can quickly result in data exfiltration, i.e., stealing and extolling the entire SQL database. With the rise of LLMs, a similar type of attack is threatening to rock the revolution. In this article, you will learn:

What LLM Prompt Injection IsWhy It Occursand how YOU can Mitigate its Effect as the Application Owner

Who is this blog post useful for? Is anybody working on implementing LLMs into their applications?

How advanced is this post? Anybody previously acquainted with LLM terms should be able to follow along.

Similar to SQL, LLM prompt injection has emerged as the troublesome capability of LLMs, like GPT-4. This method allows users to inject specific prompts that strategically guide the model to reveal the data it has learned that shouldn’t have been exposed to the user on frontend.

Here’s an example:

Overly Broad Training Data: LLMs are trained on diverse datasets from the internet, and their knowledge spans a wide array of topics. In some cases, the training data may inadvertently include sensitive or confidential information.Lack of… Read the full blog for free on Medium.

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Published via Towards AI