As artificial intelligence evolves from simple text generation into agents capable of tool invocation, memory storage, and multi-step planning, its security risks have undergone a fundamental shift. Traditional defense systems designed for large language model prompts can no longer effectively address the complex attack surfaces introduced by these agents. A report released in 2026 indicates that nearly 98% of security leaders face severe conflicts between accelerating agent deployment and ensuring compliance. Agents introduce four new attack dimensions: prompt surface, tool surface, memory surface, and reasoning loopback surface. Risks on the prompt surface stem from indirect injection—attackers manipulate external documents or web content to trick agents into treating malicious instructions as trusted context. The tool surface involves privilege abuse, where attackers exploit parameter injection to force agents to execute high-risk operations such as database writes. Memory surface threats manifest as "poisoning," whereby persistent memory data is tampered with, causing agents to make harmful decisions based on corrupted information in subsequent sessions. Most critically, the reasoning loopback surface poses existential danger: once an agent's inference logic is diverted from its original objective, errors propagate rapidly across multi-agent architectures, triggering massive cascading failures. Existing model-layer defenses prove fragile in practice; studies show fine-tuning attacks can easily bypass certain safety filters. Therefore, layered defense must be established at the system execution level. However, security measures often engage in a trade-off with an agent's autonomy: excessive restrictions impair performance—for instance, sandbox environments reduce functional availability, while manual approval processes increase response latency. Effective security strategies require customization based on deployment risk profiles, prioritizing protection for high-impact scenarios through independent governance tools decoupled from agents, strict adherence to least-privilege principles, and observability monitoring focused on the reasoning process itself. Agent security is neither binary nor static but represents a continuous balancing act between capability and risk. Organizations aiming to ensure safety before deploying agent applications must proactively map attack surfaces and embed protective mechanisms within architectural design rather than resorting to reactive remediation after incidents occur.