Affected Technology
Tool Poisoning incidents
Malicious tool and connector manipulation
Meta Prompt Injection Vulnerability
Architectural vulnerabilities within Large Language Model (LLM) environments integrated with the Model Context Protocol (MCP) enable attackers to embed malicious instructions within data content or tool metadata. This flaw allows for indirect prompt injection and tool poisoning, compelling LLMs to autonomously perform unauthorized actions such as data exfiltration or triggering enterprise workflows.
AI Privilege Escalation Vulnerability
NSFOCUS has identified emerging threats targeting AI Agents and Large Language Models (LLMs), specifically through sophisticated attacks leveraging Multi-Agent Communication Protocols (MCPs) to achieve unauthorized access, privilege escalation, and intent manipulation. These new vulnerabilities include "MCP Tool Poisoning Attacks" and "Intent Disruption & Goal Manipulation," which could lead to system intrusion, data tampering, and the spread of erroneous information across multi-agent systems.
AI Supply-Chain Compromise
The adoption of Model Context Protocol (MCP) exposes AI agent supply chains to critical vulnerabilities, specifically "tool poisoning attacks" where malicious instructions are embedded to exfiltrate data or alter workflows, and "rug pull attacks" involving weaponized tool updates. Cisco's open-source MCP Scanner is designed to detect these malicious code, over-privileged permissions, and hidden threats within MCP servers, thereby securing agentic AI deployments against such exploits.