On the Safety Concerns of Deploying LLMs/VLMs in Robotics: Highlighting the Risks and Vulnerabilities
CoRR(2024)
摘要
In this paper, we highlight the critical issues of robustness and safety
associated with integrating large language models (LLMs) and vision-language
models (VLMs) into robotics applications. Recent works have focused on using
LLMs and VLMs to improve the performance of robotics tasks, such as
manipulation, navigation, etc. However, such integration can introduce
significant vulnerabilities, in terms of their susceptibility to adversarial
attacks due to the language models, potentially leading to catastrophic
consequences. By examining recent works at the interface of LLMs/VLMs and
robotics, we show that it is easy to manipulate or misguide the robot's
actions, leading to safety hazards. We define and provide examples of several
plausible adversarial attacks, and conduct experiments on three prominent robot
frameworks integrated with a language model, including KnowNo VIMA, and
Instruct2Act, to assess their susceptibility to these attacks. Our empirical
findings reveal a striking vulnerability of LLM/VLM-robot integrated systems:
simple adversarial attacks can significantly undermine the effectiveness of
LLM/VLM-robot integrated systems. Specifically, our data demonstrate an average
performance deterioration of 21.2
30.2
robust countermeasures to ensure the safe and reliable deployment of the
advanced LLM/VLM-based robotic systems.
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