Researchers propose two universal tool-based defenses (Attacker Tool Filtering and Normal Tool Recalling) and prompt-based defenses (Chain-of-Thought prompting and self-reflection techniques) to mitigate adversarial attacks on tool-integrated LLM agents. Experimental results show significant reduction in Attack Success Rates while preserving or improving task success rates across various LLMs. The code is available on GitHub.