MCP Safety Audit: LLMs with the Model Context Protocol Allow Major
  Security Exploits
To reduce development overhead and enable seamless integration between
potential components comprising any given generative AI application, the Model
Context Protocol (MCP) (Anthropic, 2024) has recently been released and
subsequently widely adopted. The MCP is an open protocol that standardizes API
calls to large language models (LLMs), data sources, and agentic tools. By
connecting multiple MCP servers, each defined with a set of tools, resources,
and prompts, users are able to define automated workflows fully driven by LLMs.
However, we show that the current MCP design carries a wide range of security
risks for end users. In particular, we demonstrate that industry-leading LLMs
may be coerced into using MCP tools to compromise an AI developer's system
through various attacks, such as malicious code execution, remote access
control, and credential theft. To proactively mitigate these and related
attacks, we introduce a safety auditing tool, MCPSafetyScanner, the first
agentic tool to assess the security of an arbitrary MCP server. MCPScanner uses
several agents to (a) automatically determine adversarial samples given an MCP
server's tools and resources; (b) search for related vulnerabilities and
remediations based on those samples; and (c) generate a security report
detailing all findings. Our work highlights serious security issues with
general-purpose agentic workflows while also providing a proactive tool to
audit MCP server safety and address detected vulnerabilities before deployment.
  The described MCP server auditing tool, MCPSafetyScanner, is freely available
at: https://github.com/johnhalloran321/mcpSafetyScanner
67fee58201428cf3b8408447
Brandon Radosevich
67fee58201428cf3b8408448
John Halloran
claimed_verified
2025-04-16T08:51:13.631Z
658749a2d861072dc5de6f76
/avatars/88c73e5044eb73c0102fc4eb343589ac.svg
John Halloran
user
johnhalloran
MCP Safety Audit: LLMs with the Model Context Protocol Allow Major
  Security Exploits
To reduce development overhead and enable seamless integration between
potential components comprising any given generative AI application, the Model
Context Protocol (MCP) (Anthropic, 2024) has recently been released and
subsequently widely adopted. The MCP is an open protocol that standardizes API
calls to large language models (LLMs), data sources, and agentic tools. By
connecting multiple MCP servers, each defined with a set of tools, resources,
and prompts, users are able to define automated workflows fully driven by LLMs.
However, we show that the current MCP design carries a wide range of security
risks for end users. In particular, we demonstrate that industry-leading LLMs
may be coerced into using MCP tools to compromise an AI developer's system
through various attacks, such as malicious code execution, remote access
control, and credential theft. To proactively mitigate these and related
attacks, we introduce a safety auditing tool, MCPSafetyScanner, the first
agentic tool to assess the security of an arbitrary MCP server. MCPScanner uses
several agents to (a) automatically determine adversarial samples given an MCP
server's tools and resources; (b) search for related vulnerabilities and
remediations based on those samples; and (c) generate a security report
detailing all findings. Our work highlights serious security issues with
general-purpose agentic workflows while also providing a proactive tool to
audit MCP server safety and address detected vulnerabilities before deployment.
  The described MCP server auditing tool, MCPSafetyScanner, is freely available
at: https://github.com/johnhalloran321/mcpSafetyScanner
The Model Context Protocol (MCP) has security vulnerabilities that can be exploited through various attacks, which are mitigated by the introduced agentic tool, MCPSafetyScanner, for assessing MCP server security.
Model Context Protocol MCP large language models LLMs agentic tools adversarial samples security auditing MCPSafetyScanner