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Home»Research»Professor UAB builds user-friendly tools to find hidden AI security threats
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Professor UAB builds user-friendly tools to find hidden AI security threats

versatileaiBy versatileaiJune 26, 2025No Comments2 Mins Read
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xi li, doctorChatbots and other artificial intelligence tools are becoming more common in everyday life. They answer questions, provide advice, and help in areas such as finance and healthcare. But what if these systems secretly hide dangerous messages?

Xi Li, PhD, a professor of computer science at the University of Alabama, Birmingham, leads the way in which AI tools focus on ethical, safety and public goods. Her new research provides an easy way for everyday users to find hidden risks with AI-powered tools, even if they don’t have technical training.

Her work, entitled “Chain-of-Scrutiny: Detecting Backdoor Attacks of Large-Scale Language Models,” was recently recognized by the 2025 Association of Computational Linguistics, one of the global conferences of linguistic technology and artificial intelligence.

Li’s method uses simple, natural language to guide the inference process to AI models, similar to the types used in text and email. If the model provides a strange or dangerous answer that does not match that reasoning, the tool can find the problem.

“It’s like guiding chatbots to think step by step, just like we humans,” Lee said. “It’s easier to find the problem after taking each step in the inference process. The same goes for AI.”

Many commonly used AI systems are only available through application programming interfaces. This means that users have no access to how the system is built. This makes it difficult to check for security issues. However, Li’s method works directly within user-AI conversations, providing an easy way to ensure trust without the need for special access or expertise.

Her approach makes AI safer and easier to use. Instead of relying on large tech companies to solve problems, everyday people can help capture the problems themselves.

“This is a paradigm shift in AI safety,” Li said. “We don’t just protect our models. We’re making sure our users can detect the threat itself without technical training.”

The project is a collaboration between UAB’s Li lab and researchers at Auburn University in Auburn, Alabama. Pennsylvania State University, University Park, Pennsylvania. And Meta.

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