Red Teaming for Generative AI

Red Teaming can be described as a technique that can effectively remove vulnerabilities and enhance resiliency of Generative AI models. It is undoubtedly a great method for discovering and managing risks associated with Generative AI. The implementation of red teaming involves overcoming of several obstacles listed below.

1. Clarification regarding what constitutes a red team
2. Standardization of the team’s working during the testing of the model
3. Specification regarding the codification of findings along with dissemination once the testing ends.

Every model comprises of a distinct attack surface, vulnerability, and deployment environment. Hence, no two red teaming efforts are going to be identical. For the exact same reason, red teaming that’s consistent and transparent is the main challenge when it comes to deployment of Generative AI for vendors developing foundational models and companies fine-tuning and putting the models to use.

So, let’s now quickly dive in to explore various key components of red teaming.

Guidelines for Creating Red Teams for Generative AI

Given the scale of AI systems adoped by several companies, red teaming each of these systems is impossible. For this very reason, triaging each system’s risk is the key to effective red teaming. Varying levels of risk can be utilized for guiding the intensity of each red teaming effort like the extent to which the system is tested or not tested. By leveraging this approach, lower risk models must be subjected to less-thorough testing. External reviews may show a reasonable standard of care and limit liability too through documentation which external parties have signed off on the generative AI system.

Red Teaming for Generative AI versus Red Teaming for Other Software Systems

Interaction with Generative AI system results in creation of vast volumes of text, images or audio which differ from other forms of AI in scope and scale. It is designed with a specific goal of generating content that’s harmful with no clear analogue in traditional software systems ranging from generation of demeaning stereotypes and graphical images to outright lies.

Red teams interact with Generative AI systems in a unique manner. It involves them focusing on generating prompts that are malicious or inputs into the model along with tests using a more traditional code to test the system’s capability in producing harmful or inappropriate behavior. There are many ways in which malicious prompts can be generated. These range from making subtle changes to prompts to pressing the model to generate outputs that are problematical.

Advantages of Red Teaming

Red Teaming offers a great way to gauge your organization’s cyber security performance and offers you and your security leaders a real life assessment regarding the security level of your organization.
Red Teaming assists businesses in:

• Identifying and assessing vulnerabilities
• Evaluating security investments
• Testing threat detection and response capabilities
• Boosting a continuous improvement culture
• Preparing for unknown security risks
• Staying a step ahead of attackers

Summary

Red teaming is a complex process comprising of various teams, timelines, and diverse expertise levels. However, the problems that are encountered by companies are not just limited to putting together the red team, aligning on key liabilities, coming up with clear degradation objectives, and implementing the right attack strategies.