  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.
