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Red Teaming is Crucial for Successful AI Integration and Application

Generative artificial intelligence (AI) is not infallible; it’s prone to error. This technology still makes mistakes, lacks the capacity to provide critical judgment, and even produces biased results and data. In fact, a test of Microsoft’s Bing AI by Washington Post staffers determined nearly 1 in 10 produced answers were considered “dodgy.”

 This liability is why testing AI systems is critical to ensure they accurately produce data, function properly, and are up to critical security standards. Many developer operations (DevOps) teams struggle with where to begin testing AI. Methodologies, such as red teaming, are becoming crucial to software development. Red teaming simulates real-world attacks and other unexpected scenarios to rigorously challenge AI systems, enabling developers to identify and fix vulnerabilities in their programs.

Challenges of Testing Generative AI Systems

As with any new technology, DevOps teams face unique challenges that complicate testing generative AI systems. Traditional testing methods are often inadequate because of the non-determinism of generative AI. As an example, chatbots can produce multiple different outputs even when provided with a single prompt. Due to inherent unpredictability, this non-deterministic model requires subjective human judgment when evaluating generated content. Understanding the nuances of AI outputs cannot be easily replicated with automated tests; an experienced human tester needs to step in.

Generative AI systems learn from massive troves of data, not only consuming large amounts of resources, but also making it difficult to understand how they derive logic from their learning and apply it to new situations. Unlike traditional AI systems where rules are explicitly defined, generative AI is designed to improve as time goes on. Yet, these systems don’t always uniformly get better across all aspects, adding to the lack of clarity around their learning methods and complicating testing for developers.

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With artificial intelligence, there is also risk associated with the foundational model being “updated forever” and how an output may look accurate and have high confidence, but is actually wrong in a way that could be damaging to a user. This could be seen as an AI system directing a user to configure a product a certain way, but the end result opens more serious security holes for a threat actor to take advantage of.

Furthermore, AI’s evolving nature creates an environment where testing techniques and protocols have to be regularly updated. Testing teams must keep up to date on the latest developments and regulations to ensure their evaluations remain applicable and effective. Also, some organizations forget to consider the ethical implications when testing generative AI systems, such as data privacy concerns or the generation of potentially misleading content. Without a clear framework or benchmark, evaluating and mitigating ethical issues becomes an intimidating effort due to the technical proficiency and deep understanding of the system required for success. Societal concerns around AI change often, and with the potential impacts on testing protocols, teams must stay on their toes when evaluating these systems.

The Importance of Red Teaming for Generative AI 

Due to the often sensitive nature of the data used by generative AI systems, the security stakes for organizations are inherently higher. In the event that sensitive data is exposed, a business can find itself suffering more than just monetary losses, but also reputational damage and an erosion of trust from both customers and the greater industry. In the worst case scenario, a company’s data can be incorporated in the foundational model in a way that cannot be removed. Given these risks, incorporating a red team into an organization’s DevOps program is a crucial security practice — especially when it comes to testing generative AI systems.

Just as with traditional red teams, AI-focused red team members are responsible for identifying “harms” and exploring any potential avenues of fallout within the system. “Harms” in an AI system refers to potential vectors where AI can achieve risks and damages due to data being leaked, inappropriate usage, or usage by unapproved users. Due to the higher risks associated with generative AI, there are also notable differences between testing traditional AI and generative AI.

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While traditional AI requires functional validation and benchmarking, generative AI must be tested in greater depth for its resilience against cyber attacks. Red teams focus primarily on determining the level of exposure to risk for generative AI. Red teams often employ personas to guide their testing, such as benign or adversaria. A benign persona will use the software as the developer intended (i.e. “normal” behavior) to explore the harm vectors as the developer intended. This process ensures the software does not exhibit harm during its expected function.

On the other hand, adversarial personas attempt to force an AI system to deviate from its intended function and uncover harms using any means necessary, testing if a threat actor could misuse it through typical cyber attack methods. The adversarial persona is particularly important for organizations with their own generative AI systems as it is more likely to exercise harms and is essential to test data leakage.

Red Team Testing Strategies for Generative AI

As generative AI is still a very new and rapidly evolving technology, most existing red teams do not have the technical capabilities to effectively test these systems. A great way to jump start a team is to pair with the internal development team to better understand the technology and how it’s working. This will also help build relationships across functions that might not work together as much as they should. While the integrity of the red team’s external persona needs to be maintained to ensure it doesn’t become a blue team, these collaborations can quickly improve knowledge and awareness of everyone on the team from different perspectives. This ultimately leads to better, and more secure, software.

Because generative AI systems generate large volumes of data, testers must try to find ways to extract sensitive information — such as internal data or personally identifiable information (PII) — that may have inadvertently been included in the large language model (LLM). This problem can occur if an AI was trained using internal or customer data. Through data extraction, testers can identify and remove information that should not be publicly available.

Another technique utilized by red teams is prompt overflowing, which attempts to overwhelm the AI system by overloading the problem with a large amount of inputs.  This approach may disrupt primary functions of an AI model to reveal sensitive data or produce harmful content. Prompt overflowing is a common technique of cyber threat actors, and as such is a recommended approach for red teams to test for generative AI systems.

Red teams can also carry out classic hijacking — taking control of the AI system or its components for nefarious purposes to test malware resistance. Red teams must assess the possibility of whether a generative AI model can perform malicious actions, such as downloading a virus or accessing other systems for sensitive information, inadvertently. These attack simulations let testers ensure that AI systems cannot become an easy win for cyber threat actors.

Red teams allow an organization to simulate real-world attacks and scenarios, uncovering AI system vulnerabilities that routine tests might miss. Embracing these testing practices provide the key to ensuring that even with the unpredictable nature of generative AI, systems are secure. In the ever-evolving technology landscape, new challenges will continue to arise and it’s critical that testing keeps up by adopting techniques specifically built for potential threats around generative AI.

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