OWASP Top 10 for Large Language Model (LLM) Security - A Web Application Penetration Tester's Guide - Part 1

Language models have become an integral part of various applications, ranging from chatbots to content generation. However, with their increasing adoption comes a set of security challenges that need to be addressed. The OWASP Top 10 for Large Language Models (LLM) focuses on the most critical security risks associated with large language models. As a Web Application Penetration Tester, understanding these risks is crucial for ensuring the security of applications that leverage these models. In this blog, we will explore each of the OWASP Top 10 for LLM and their impact on the role of a penetration tester. 

A couple of things to note:  

Keep in mind that many of these attacks occur in concert - a prompt injection attack may be executed through an insecure plug-in. 

Also, penetration testing a large language model will involve skills, techniques, and goals that are not always part of the pentester’s job role. For example, some LLM vulnerabilities require secure code reviews, which do not always fall under a pentester’s responsibilities. This article assumes a broad role definition for a pentester.

LLM01: Prompt Injection

Prompt injection involves the unauthorized injection of malicious prompts or input into a language model. Prompts can be injected through a variety of means, such as a compromised plug-in or inadequate input control. Prompt injection can lead to a variety of compromises, including remote code execution and SQL injection. 

Penetration testers need to assess the application's input validation mechanisms and the overall system supply chain to ensure that malicious prompts cannot be injected, thereby safeguarding against this type of attack.

LLM02: Insecure Output Handling

LLMs generate data based on prompts, and part or all of the prompt is often included in the model’s responses. This can lead to compromising downstream systems through cross-site scripting, remote code execution, or other common exploits.  

Penetration testers need to verify that downstream systems cannot be compromised by using malicious inputs to the LLM.

LLM03: Training Data Poisoning

Language models are only as good as the data they are trained on. Training data poisoning involves manipulating the model's behavior by injecting malicious data during the training phase. This can lead to a variety of compromises, including introducing vulnerabilities into the system, creating biases in the model output that result in bad decisions or public embarrassment, or degrading the performance and accuracy of the model. 

Penetration testers must evaluate the robustness of the training data and verify the source of any external data sources used to train the model.

LLM04: Model Denial of Service

In a model denial-of-service attack, malicious inputs are crafted to overload or exhaust the language model's resources, rendering it unresponsive. Denial of service can be achieved by submitting resource exhaustive prompts or by flooding the system with requests. Systems should have capacity usage limits such as the number of requests that can be submitted over a specified time and input validation to avoid resource-exhaustive prompts. 

Penetration testers must assess the application's ability to handle resource-intensive inputs and recommend measures to mitigate such attacks, ensuring consistent and reliable service.

LLM05: Supply Chain Vulnerabilities

Supply chain vulnerabilities are a well-known threat in cybersecurity. LLMs are vulnerable to the same threats as other software - vulnerabilities in libraries, platforms, and systems used to develop the model and the tools that use the model. LLMs are subject to an additional supply chain risk - external data used to train the model. 

Penetration testers will thoroughly analyze the supply chain of the application, verifying that only trusted and well-maintained resources are used.

Don’t miss Part Two of this blog where we cover the second half of the OWASP Top 10 for LLM Security!

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