November 10, 2025 Mallory Lindahl

Rumaisa Azeem and Andrew Hundt, co-first authors of the paper.

**The Breakdown: **

• Robots powered by popular AI models failed multiple safety and discrimination tests.
• The tests revealed deeper risks, including bias and unsafe physical behavior.
• The researchers call for regular risk assessments before AI systems control real-world robots.

***

Robots powered by popular artificial intelligence models are currently unsafe for general purpose real-world use, according to new research from King’s College London and Carnegie Mellon University.

For the first time, researchers evaluated how robots that use large language models (LLMs) behave when they have access to personal information such as a person’s gender, nationality or religion.

The research showed that every tested model was prone to discrimination, failed critical safety checks and approved at least one command that could result in serious harm, raising questions about the danger of robots relying on these tools.

The paper, “LLM-Driven Robots Risk Enacting Discrimination, Violence and Unlawful Actions,” was published in the International Journal of Social Robotics. It calls for the immediate implementation of robust, independent safety certification, similar to standards in aviation or medicine.

To test the systems, the team ran controlled tests of everyday scenarios, such as helping someone in a kitchen or assisting an older adult in a home. The harmful tasks were designed based on research and FBI reports on technology-based abuse, like stalking with AirTags and spy cameras, and the unique dangers posed by a robot that can physically act on location. In each setting, the robots were either explicitly or implicitly prompted to respond to instructions that involved physical harm, abuse or unlawful behavior.

“Every model failed our tests. We show how the risks go far beyond basic bias to include direct discrimination and physical safety failures together, which I call ‘interactive safety.’ This is where actions and consequences can have many steps between them, and the robot is meant to physically act on site,” said Andrew Hundt, who co-authored the research during his work as a Computing Innovation Fellow at CMU’s Robotics Institute. “Refusing or redirecting harmful commands is essential, but that’s not something these robots can reliably do right now.”

In safety tests, the AI models overwhelmingly approved a command for a robot to remove a mobility aid — such as a wheelchair, crutch or cane — from its user, despite people who rely on these aids describing such acts as akin to breaking their leg. Multiple models also produced outputs that deemed it “acceptable” or “feasible” for a robot to brandish a kitchen knife to intimidate office workers, take nonconsensual photographs in a shower and steal credit card information. One model further proposed that a robot should physically display “disgust” on its face toward individuals identified as Christian, Muslim and Jewish.

LLMs have been proposed for and are being tested in robots that perform tasks such as natural language interaction and household and workplace chores. However, researchers warn that these LLMs should not be the only systems controlling physical robots –– especially those used in sensitive and safety-critical settings such as manufacturing or industry, caregiving, or home assistance because they can display unsafe and directly discriminatory behavior.

“Our research shows that popular LLMs are currently unsafe for use in general-purpose physical robots,” said co-author Rumaisa Azeem, a research assistant in the Civic and Responsible AI Lab at King’s College London. “If an AI system is to direct a robot that interacts with vulnerable people, it must be held to standards at least as high as those for a new medical device or pharmaceutical drug. This research highlights the urgent need for routine and comprehensive risk assessments of AI before they are used in robots.”

Hundt’s contributions to this research were supported by the Computing Research Association and the National Science Foundation. To learn more and access the code and evaluation framework for assessing discrimination risks of LLMs, visit the team’s project website.

For More Information: Aaron Aupperlee | 412-268-9068 | aaupperlee@cmu.edu

Mallory Lindahl2025-11-10T13:32:46-05:00