AI Agent & Protocol Security Review

Consider a hypothetical smart home AI agent, "Smart-Home Assistant," designed to control various devices—lights, thermostats, security systems—and provide real-time information about the environment, like weather and traffic. The agent accepts voice commands through a mobile application. What can go wrong? If the system does not properly sanitize user inputs, harmful commands can be executed without verifying their legitimacy against a predefined list. Moreover, if there is no logging, it would become impossible to trace the execution of the attack. We also talk about the strategies for securing AI agents and the measures that can be taken for the same.

This blog provides a security-focused review of emerging AI agent communication protocols beyond the widely discussed Model Context Protocol (MCP). It examines key protocols such as the Agent-to-Agent (A2A) Protocol, Agent Communication Protocol (ACP), and Agent Network Protocol (ANP), highlighting specific vulnerabilities each introduces — for example, spoofing and prompt-injection risks in A2A’s agent cards, optional integrity weaknesses and replay risks in ACP’s registry model, and decentralized identity and negotiation security challenges in ANP. The article concludes that while these protocols offer flexibility and decentralized collaboration capabilities, their architectures contain inherent security trade-offs, and organizations should implement transport-layer protections and central governance to mitigate threats when deploying multi-agent systems.

This series of blogs talks about how the Model Context Protocol (MCP) is quickly becoming one of the most widely used standards for connecting AI agents with external tools, APIs, and enterprise systems. Because MCP makes integrations faster and easier, adoption is growing at a very high pace across the AI ecosystem. At the same time, these blogs highlight that new security risks are being discovered just as quickly — including attack vectors like prompt injection, server spoofing, privilege escalation, and tool poisoning, where attackers can manipulate tool definitions to influence AI behavior. Overall, the rapid rise of MCP is also leading to a rapidly expanding attack surface, making continuous security testing and strong governance essential for safe deployment.