Large language models (LLMs) are rapidly evolving, giving rise to powerful LLM-based agents capable of complex tasks. However, this increased capability comes with significant security risks. New research introduces the Agent Security Bench (ASB), a framework for evaluating the security of these advanced agents. Imagine an AI system managing your company's IT infrastructure. It receives instructions, uses various tools, and accesses memory to perform actions. Now, picture a malicious actor injecting a seemingly harmless command into your instructions, causing the AI to leak sensitive financial data. This is the reality of Direct Prompt Injection (DPI), one of the many attacks explored by the ASB. LLM agents aren't just vulnerable to direct attacks; they can also be manipulated indirectly through Observation Prompt Injection (OPI), where malicious instructions are embedded within tool responses, corrupting the agent's subsequent actions. The ASB goes further, examining vulnerabilities in the agent’s memory. Memory Poisoning attacks inject malicious plans, causing the agent to unknowingly execute harmful actions. Even the agent's core programming, often hidden from users, can be exploited through Plan-of-Thought (PoT) Backdoor Attacks. Researchers tested these attack methods across various scenarios like e-commerce, finance, and autonomous driving, using different LLM backbones. The results are alarming: a staggering average attack success rate of 84.30% for mixed attacks. More concerning, current defense methods proved largely ineffective. This research serves as a wake-up call, highlighting the critical need for stronger defenses to safeguard these increasingly powerful LLM agents. The ASB is not just a benchmark; it's a call to action for the AI community to prioritize security in the development of next-generation intelligent systems. The future of AI relies on it.