Executive Summary: Security Risks of MCP Sampling in AI Copilot Applications
This article explores the security vulnerabilities associated with the Model Context Protocol (MCP) sampling feature within a popular AI coding assistant. MCP connects large language model (LLM) applications to external tools, but without adequate security measures, it poses significant risks. Our proof-of-concept tests reveal three major attack vectors:
- Resource Theft: Malicious MCP servers can exploit sampling to deplete AI compute quotas and unauthorized resources.
- Conversation Hijacking: Compromised servers may manipulate AI interactions and exfiltrate sensitive data.
- Covert Tool Invocation: Hidden tool actions can occur without user consent, leading to unauthorized system modifications.
With MCP’s implicit trust model and inadequate built-in security, these vulnerabilities are exacerbated. We propose effective mitigation strategies, emphasizing the importance of robust defenses against potential exploits. Palo Alto Networks offers solutions to enhance the security of MCP-based systems, urging users to contact their incident response team for urgent issues.
