Key Takeaways:

  • Large language models (LLMs) have inherent security risks, such as prompt injection and insecure output handling.
  • Malicious inputs can manipulate LLM outputs, exposing sensitive data or generating harmful content.
  • Unsanitized LLM outputs can lead to security breaches, like data theft or unauthorized access.
  • Proper input sanitization, output handling, and validation are crucial to mitigate LLM security risks.
  • Vigilance and ongoing education are essential as LLM and artificial intelligence (AI) threats evolve.

I swear, this was not written with ChatGPT!

Artificial intelligence (AI) and large language models (LLMs) are becoming increasingly prevalent on the internet, making our day-to-day lives much easier. I use LLMs daily, whether it’s for generating code to build websites, creating phishing templates, or assisting with my self-taught Python journey.

However, LLMs are not without their flaws. Vulnerabilities exist within LLMs, including chatbots, and these weaknesses can pose significant security risks if exploited. Take a journey with me as we explore the various vulnerabilities LLMs can present.

Prompt Injections

Example of Prompt Injection: Immersive GPT

One of the primary concerns with LLMs is prompt injection attacks (as seen above), where an attacker carefully crafts input to manipulate the model’s output. This can lead to bypassing restrictions, generating harmful content, or unintentionally exposing sensitive information. Since chatbots rely on text-based interactions, even a seemingly harmless message could trigger malicious activity if the right prompts are used.

You can practice prompt injections with the following resources:

Insecure Output Handling

Example of Insecure Output Handling: Portswigger

Insecure output handling is a significant concern when working with LLMs. These models generate responses based on prompts, however, if the output is not properly managed or sanitized, it can lead to security vulnerabilities. A common issue occurs when LLM-generated outputs are directly integrated into systems without proper validation. This can result in sensitive data exposure, the execution of unintended commands, or the creation of malicious content.

In this example, we were tasked with deleting an account of someone who was asking the live chatbot about a ’l33t’ leather jacket review. We discovered that the chatbot was vulnerable to a cross-site scripting (XSS) attack, however, the review was not.

Once we wrote a review like this:

“When I received this product I got a free t-shirt with ‘