arXiv:2405.19802v1 Announce Type: new
Abstract: Embodied intelligence empowers agents with a profound sense of perception, enabling them to respond in a manner closely aligned with real-world situations. Large Language Models (LLMs) delve into language instructions with depth, serving a crucial role in generating plans for intricate tasks. Thus, LLM-based embodied models further enhance the agent’s capacity to comprehend and process information. However, this amalgamation also ushers in new challenges in the pursuit of heightened intelligence. Specifically, attackers can manipulate LLMs to produce irrelevant or even malicious outputs by altering their prompts. Confronted with this challenge, we observe a notable absence of multi-modal datasets essential for comprehensively evaluating the robustness of LLM-based embodied models. Consequently, we construct the Embodied Intelligent Robot Attack Dataset (EIRAD), tailored specifically for robustness evaluation. Additionally, two attack strategies are devised, including untargeted attacks and targeted attacks, to effectively simulate a range of diverse attack scenarios. At the same time, during the attack process, to more accurately ascertain whether our method is successful in attacking the LLM-based embodied model, we devise a new attack success evaluation method utilizing the BLIP2 model. Recognizing the time and cost-intensive nature of the GCG algorithm in attacks, we devise a scheme for prompt suffix initialization based on various target tasks, thus expediting the convergence process. Experimental results demonstrate that our method exhibits a superior attack success rate when targeting LLM-based embodied models, indicating a lower level of decision-level robustness in these models.
The Significance of Embodied Intelligence in Multimedia Information Systems
Embodied intelligence is a concept that empowers agents with a profound sense of perception, enabling them to respond in a manner closely aligned with real-world situations. This concept has far-reaching implications in the field of multimedia information systems, where the fusion of various technologies such as animations, artificial reality, augmented reality, and virtual realities converge.
Large Language Models (LLMs) play a crucial role in generating plans for intricate tasks by delving into language instructions with depth. The integration of LLM-based embodied models further enhances the agent’s capacity to comprehend and process information. This multi-disciplinary approach brings together the power of language understanding, perception, and decision-making, creating a system that can seamlessly interact with the physical and virtual world.
The Challenges in Securing LLM-based Embodied Models
However, with the integration of LLMs into embodied models, new challenges arise in securing these systems against potential attacks. Specifically, attackers can manipulate LLMs by altering their prompts, resulting in the production of irrelevant or even malicious outputs. This poses a threat to the overall robustness and reliability of LLM-based embodied models.
Addressing this challenge, the researchers behind the article have identified a notable absence of multi-modal datasets essential for evaluating the robustness of LLM-based embodied models comprehensively. To fill this gap, they have constructed the Embodied Intelligent Robot Attack Dataset (EIRAD), specifically tailored for robustness evaluation. This dataset will enable researchers to test and enhance the security of LLM-based embodied models across a wide range of attack scenarios.
Innovative Attack Strategies and Evaluation Methods
The article outlines two attack strategies devised to simulate a range of diverse attack scenarios: untargeted attacks and targeted attacks. These strategies enable researchers to understand the vulnerabilities and potential loopholes in LLM-based embodied models, helping to develop effective defense mechanisms.
Furthermore, in order to accurately evaluate the success of these attack strategies, the researchers have devised a new attack success evaluation method using the BLIP2 model. This evaluation method ensures that the attack is not only successful in manipulating the LLM-based embodied model but also provides a measure of the effectiveness of the attack.
Optimizing the Attack Process
The researchers acknowledge the time and cost-intensive nature of the attack process, particularly the GCG algorithm utilized in the attacks. To address this, they propose a scheme for prompt suffix initialization based on various target tasks. This scheme expedites the convergence process, making it more efficient and less resource-intensive.
Implications for Decision-level Robustness in LLM-based Embodied Models
Experimental results presented in the article demonstrate that the proposed method exhibits a superior attack success rate when targeting LLM-based embodied models. This indicates a lower level of decision-level robustness in these models. Understanding and addressing these vulnerabilities is crucial for enhancing the security and reliability of LLM-based embodied models in the field of multimedia information systems.
In conclusion, this article highlights the multi-disciplinary nature of the concepts discussed, bridging the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By exploring the challenges and presenting innovative attack strategies and evaluation methods, the researchers contribute to the ongoing efforts to secure and enhance the robustness of LLM-based embodied models.