As AI is integrated into enterprise technology stacks, AI applications are becoming prime targets for cyberattacks. In response, many cybersecurity teams are adapting existing cybersecurity practices to mitigate these emerging threats. One such practice is red teaming. This is an effort to expose weaknesses in systems by playing the role of the adversary and develop countermeasures to the threats found.
While this exercise is certainly an important one, recent reports and anecdotal evidence indicate that red teaming is not that simple when it comes to AI application security.
To effectively protect these new environments, cybersecurity teams must understand the changing nuances of red teaming in the context of AI. Understanding what has changed (and what hasn’t) with AI is a critical starting point for guiding red team efforts in the coming years.
Why AI flips the red team's script
In the pre-AI era, red teaming was the practice of finding and exploiting vulnerabilities with a specific goal in mind (such as gaining access to servers critical to business operations), usually without alerting security teams. It meant taking covert steps to But with the advent of AI, the red team process will change. This process will become much more frequent and widespread than his one-time trials with a single goal.
Unlike previous types of software, AI models become more intelligent over time. This constant change means new risks can emerge at any time, making predictions very difficult. A one-and-done approach to red teaming just doesn't work. As the capabilities of these models improve over time, cyber teams no longer red-team static models.
Another change: When you start using a third-party LLM, all you see is the model itself, not the data or code behind it. This is similar to assessing car trouble without being able to see under the hood, and is a clear contrast to what we're used to with traditional software.
Red teaming AI applications is no longer a simple process of having a checklist of things to keep in mind and then following through. To identify vulnerabilities, cyber teams must constantly come up with creative ways to poke holes in models and carefully monitor model behavior and output.
Additionally, teams should think carefully when red-teaming LLMs using external plugins. Interconnecting LLMs requires red-teaming the entire system, starting with a very clear objective. For example, let's say you want your LLM to disclose confidential information. Once you successfully generate that vulnerability, you need to identify not only weaknesses in your model, but also system-wide safeguards to reduce the downstream impact of that type of attack.
When working with AI, red teaming your model isn't the only thing that's important. Red teaming interconnected applications is also important. Only by broadening the scope of red team efforts can we fully identify potential vulnerabilities and proactively build operational protections around them.
Cybersecurity 101 also applies to AI
As with any other type of software, red teaming alone is never enough. Especially in his LLM, operational protection is essential to prevent attacks. New threats exist every day, so you need structures and procedures to keep your applications protected. Bottom line: AI security requires leaving no stone unturned, and all traditional cybersecurity practices must continue to be implemented.
For example, you need to sanitize your database. If you have an LLM with access to an internal database, make sure the data is scrubbed before entering it into the model. Additionally, always check your access controls. LLMs should be granted only minimal privileges to minimize damage in the event of a breach.
Securing AI models is an entirely new challenge that nearly every cybersecurity company will eventually have to grapple with. Red teaming is a great place to start, but you should also challenge yourself to understand the red teaming process and complement your efforts with tried and true security strategies. The more cybersecurity professionals who can understand these nuances, the closer we will be to realizing the promise of AI.
