Protecting Your Generative AI: Evaluating Security Risks

Table of Contents

1. Introduction
2. Understanding the Application
3. Threat Modeling: Identifying Risks
4. Threat Statement: Malicious Prompt
5. Attack Steps: Exploiting the Application
6. Mitigations: Protecting the System
7. Validating the Threat Model
8. Resources and Next Steps

Introduction

🔹 Building a Secure Generative AI: Evaluating the Security Risk

In this article, we will explore the process of building a secure generative AI system and evaluating the associated security risks. We will delve into the concept of threat modeling, identify potential risks, and discuss how to mitigate them effectively. Whether you are already building a generative AI application or planning to do so in the near future, this article will provide valuable insights to help you ensure the security of your system.

Understanding the Application

🔹 The Healthcare Chatbot: Revolutionizing Customer Interactions

Imagine working for a healthcare company where you and your team have developed a groundbreaking generative AI use case - a chatbot that enables customers to Inquire about their medical information. As you are eager to start building and launch the application, questions about security risks begin to arise. What are the potential risks associated with generative AI? How can these risks be prioritized and mitigated? This article will guide you through the process of answering these critical questions using threat modeling.

Threat Modeling: Identifying Risks

🔹 The Essence of Threat Modeling

Threat modeling is a systematic approach to identify, communicate, and understand potential threats and mitigations within the context of a valuable asset - in this case, your generative AI application and the customer's data. The process involves answering four fundamental questions:

1. What are we working on?
   • Understanding the application and its architecture, such as data flow and system design decisions.
2. What can go wrong?
   • Identifying the potential threats and risks associated with the application.
3. What are we going to do about those threats?
   • Designing and implementing mitigations and controls to address the identified threats.
4. Did we do a good job of threat modeling?
   • Evaluating the effectiveness of the threat model and the implemented mitigations.

Threat Statement: Malicious Prompt

🔹 The Threat: Compromising Healthcare Data

One of the key threats to consider in the context of our application is the use of a malicious prompt, leading to the exposure of healthcare data belonging to other patients. This threat can arise when the system accepts any prompt from the user without sufficient validation and allows the querying of data beyond the user's own information.

Attack Steps:

1. Reconnaissance: The malicious actor explores the application to Gather information about its vulnerabilities and weaknesses.
2. Weakness Identification: The actor identifies the specific weaknesses in the system, such as the absence of prompt validation.
3. Exploitation: The actor crafts a malicious prompt that instructs the system to retrieve data related to other users, bypassing the intended access restrictions.
4. Data Exposure: The system responds to the malicious prompt, providing unauthorized access to sensitive healthcare data.

Attack Steps: Exploiting the Application

🔹 Mimicking the Attacker's Process

To better understand the steps an attacker might take to compromise the system, we simulated a conversation between a builder, a security guardian, and a security engineer. By analyzing the behavior of the application, we identified the loopholes an attacker could exploit, such as unrestricted prompt acceptance, unverified queries, and unchecked data responses. This exercise allowed us to uncover weaknesses and potential attack vectors, paving the way for effective mitigations.

Mitigations: Protecting the System

🔹 Strengthening the Application's Security

To mitigate the identified threats and vulnerabilities, we propose the following mitigations:

1. Define Acceptable Use: Specify the permitted interactions between the logic, LLM, and database using the system prompt. This provides a clear set of rules for the application.
2. Sanitize Prompt Parameters: Implement a mechanism to validate and sanitize user prompts, dropping any unauthorized or potentially exploitative parameters.
3. Pre-define Query Structures: Establish predefined query structures to ensure that only permitted and authorized queries are generated by the LLM.
4. Validate Query Results: Check the response data against the original database to ensure it belongs to the authorized user and is accurate.
5. Implement Robust Authentication and Authorization: Leverage a trusted service like Amazon Cognito to handle authentication and authorization, verifying user identity and access rights.

Validating the Threat Model

🔹 Continuous Evaluation and Improvement

The effectiveness of the threat model and the implemented mitigations can be validated through various means:

1. Penetration testing: Conduct regular penetration tests, ideally after each release, to identify potential weaknesses and vulnerabilities beyond what the threat model initially uncovered.
2. Automated Testing: Develop automated tests for authentication and authorization mechanisms to ensure they function as intended and provide robust protection.
3. Review with Stakeholders: Share the threat model with stakeholders and incorporate their feedback to ensure accuracy, completeness, and Clarity.

Resources and Next Steps

🔹 Unlocking a Wealth of Knowledge

To enhance your understanding and continue improving your generative AI threat modeling, we recommend exploring the following resources:

1. Threat Modeling Workshop: Attend a workshop designed to help developers understand and apply threat modeling techniques effectively.
2. Threat Composer: Utilize this tool to build threat models efficiently, creating consistent and concise threat statements and categorizing them for analysis.
3. Example Generative AI Chatbot Threat Model: Study a comprehensive threat model example that provides insights into securing generative AI applications, specifically in the healthcare domain.
4. OWASP Top 10 for LLMs and MITRE Atlas: Familiarize yourself with these frameworks, which offer valuable guidance for building secure generative AI applications.

Threat modeling is an ongoing process that requires continuous evaluation and improvement. By incorporating threat modeling practices into your development lifecycle, you can build secure generative AI applications and protect sensitive data.

Thank you for joining us on this journey to understand and mitigate the security risks associated with building generative AI systems. Your commitment to security ensures the confidentiality, integrity, and availability of your applications and the trust of your customers.