MCP vs A2A Explained: AI Agent Communication Protocols & Security Risks
04/18/25 • 54 min
Dive deep into the world of AI agent communication with this episode. Join hosts Caleb Sima and Ashish Rajan as they break down the crucial protocols enabling AI agents to interact and perform tasks: Model Context Protocol (MCP) and Agent-to-Agent (A2A).
Discover what MCP and A2A are, why they're essential for unlocking AI's potential beyond simple chatbots, and how they allow AI to gain "hands and feet" to interact with systems like your desktop, browsers, or enterprise tools like Jira. The hosts explore practical use cases, the underlying technical architecture involving clients and servers, and the significant security implications, including remote execution risks, authentication challenges, and the need for robust authorization and privilege management.
The discussion also covers Google's entry with the A2A protocol, comparing and contrasting it with Anthropic's MCP, and debating whether they are complementary or competing standards. Learn about the potential "AI-ification" of services, the likely emergence of MCP firewalls, and predictions for the future of AI interaction, such as AI DNS.
If you're working with AI, managing cybersecurity in the age of AI, or simply curious about how AI agents communicate and the associated security considerations, this episode provides critical insights and context.
Questions asked:
(00:00) Introduction: AI Agents & Communication Protocols
(02:06) What is MCP (Model Context Protocol)? Defining AI Agent Communication
(05:54) MCP & Agentic Workflows: Enabling AI Actions & Use Cases
(09:14) Why MCP Matters: Use Cases & The Need for AI Integration
(14:27) MCP Security Risks: Remote Execution, Authentication & Vulnerabilities
(19:01) Google's A2A vs Anthropic's MCP: Protocol Comparison & Debate
(31:37) Future-Proofing Security: MCP & A2A Impact on Security Roadmaps
(38:00) - MCP vs A2A: Predicting the Dominant AI Protocol
(44:36) - The Future of AI Communication: MCP Firewalls, AI DNS & Beyond
(47:45) - Real-World MCP/A2A: Adoption Hurdles & Practical Examples
Dive deep into the world of AI agent communication with this episode. Join hosts Caleb Sima and Ashish Rajan as they break down the crucial protocols enabling AI agents to interact and perform tasks: Model Context Protocol (MCP) and Agent-to-Agent (A2A).
Discover what MCP and A2A are, why they're essential for unlocking AI's potential beyond simple chatbots, and how they allow AI to gain "hands and feet" to interact with systems like your desktop, browsers, or enterprise tools like Jira. The hosts explore practical use cases, the underlying technical architecture involving clients and servers, and the significant security implications, including remote execution risks, authentication challenges, and the need for robust authorization and privilege management.
The discussion also covers Google's entry with the A2A protocol, comparing and contrasting it with Anthropic's MCP, and debating whether they are complementary or competing standards. Learn about the potential "AI-ification" of services, the likely emergence of MCP firewalls, and predictions for the future of AI interaction, such as AI DNS.
If you're working with AI, managing cybersecurity in the age of AI, or simply curious about how AI agents communicate and the associated security considerations, this episode provides critical insights and context.
Questions asked:
(00:00) Introduction: AI Agents & Communication Protocols
(02:06) What is MCP (Model Context Protocol)? Defining AI Agent Communication
(05:54) MCP & Agentic Workflows: Enabling AI Actions & Use Cases
(09:14) Why MCP Matters: Use Cases & The Need for AI Integration
(14:27) MCP Security Risks: Remote Execution, Authentication & Vulnerabilities
(19:01) Google's A2A vs Anthropic's MCP: Protocol Comparison & Debate
(31:37) Future-Proofing Security: MCP & A2A Impact on Security Roadmaps
(38:00) - MCP vs A2A: Predicting the Dominant AI Protocol
(44:36) - The Future of AI Communication: MCP Firewalls, AI DNS & Beyond
(47:45) - Real-World MCP/A2A: Adoption Hurdles & Practical Examples
Previous Episode
How to Hack AI Applications: Real-World Bug Bounty Insights
In this episode, we sit down with Joseph Thacker, a bug bounty hunter and AI security researcher, to uncover the evolving threat landscape of AI-powered applications and agents. Joseph shares battle-tested insights from real-world AI bug bounty programs, breaks down why AI AppSec is different from traditional AppSec, and reveals common vulnerabilities most companies miss, like markdown image exfiltration, XSS from LLM responses, and CSRF in chatbots.
He also discusses the rise of AI-driven pentesting agents ("hack bots"), their current limitations, and how augmented human hackers will likely outperform them, at least for now. If you're wondering whether AI can really secure or attack itself, or how AI is quietly reshaping the bug bounty and AppSec landscape, this episode is a must-listen.
Questions asked:
(00:00) Introduction
(02:14) A bit about Joseph
(03:57) What is AI AppSec?
(05:11) Components of AI AppSec
(08:20) Bug Bounty for AI Systems
(10:48) Common AI security issues
(15:09) How will AI change pentesting?
(20:23) How is the attacker landscape changing?
(22:33) Where would autimation add the most value?
(27:03) Is code being deployed less securely?
(32:56) AI Red Teaming
(39:21) MCP Security
(42:13) Evolution of pentest with AI
Resources shared during the interview:
How to Hack AI Agents and Applications
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