Autonomous Large Language Model (LLM) agents are increasingly vulnerable to indirect prompt injection (IPI) attacks, where adversarial instructions embedded in external data sources manipulate agent behavior without direct user input. These external data can originate from user documents, web retrieval results, or API outputs, leading to unauthorized compliance, sensitive information disclosure, identity spoofing, and cross-agent propagation of unsafe practices. This review synthesizes current research on IPI vulnerabilities and proposed mitigation strategies. Key attack vectors include manipulating webpage HTML via adversarial triggers for web agents, poisoning agent skills with tampered content, and injecting malicious commands into cloud logs processed by debugging agents. Covert attacks, which execute malicious actions without user-perceptible traces, pose a particularly insidious threat. Existing defenses, such as SecAlign's preference optimization, StruQ's structured queries, and multi layered zero-trust architectures, show promise. More advanced frameworks like ClawGuard enforce user-confirmed rule sets at tool-call boundaries, while ZEDD detects semantic shifts in embedding space for zero-shot detection. However, current guardrails often fail against sophisticated, adaptive attacks, and frameworks like AgentVigil and MUZZLE demonstrate high success rates in red-teaming exercises. The UReCoM attack further highlights vulnerabilities where benign users unknowingly relay adversarial content, bypassing existing defenses. The pervasive nature and increasing sophistication of IPI necessitate robust, behavior-grounded evaluation metrics and unified defense mechanisms to secure autonomous LLM agents in complex, untrusted environments.