Recently, autonomous agents built on large language models (LLMs) have experienced significant development and are being deployed in real-world applications. Through the usage of tools, these systems can perform actions in the real world. Given the agents' practical applications and ability to execute consequential actions, such autonomous systems can cause more severe damage than a standalone LLM if compromised. While some existing research has explored harmful actions by LLM agents, our study approaches the vulnerability from a different perspective. We introduce a new type of attack that causes malfunctions by misleading the agent into executing repetitive or irrelevant actions. Our experiments reveal that these attacks can induce failure rates exceeding 80% in multiple scenarios. Through attacks on implemented and deployable agents in multi-agent scenarios, we accentuate the realistic risks associated with these vulnerabilities. To mitigate such attacks, we propose self-examination defense methods. Our findings indicate these attacks are more difficult to detect compared to previous overtly harmful attacks, highlighting the substantial risks associated with this vulnerability.