title: MCP Safety Audit: LLMs with the Model Context Protocol Allow Major
    Security Exploits
summary: 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
paper_authors._id: 67fee58201428cf3b8408447
paper_authors.name: Brandon Radosevich
paper_authors._id: 67fee58201428cf3b8408448
paper_authors.name: John Halloran
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T08:51:13.631Z
paper_authors._id: 658749a2d861072dc5de6f76
paper_authors.avatarUrl: /avatars/88c73e5044eb73c0102fc4eb343589ac.svg
paper_authors.fullname: John Halloran
paper_authors.type: user
paper_authors.user: johnhalloran
paper_title: MCP Safety Audit: LLMs with the Model Context Protocol Allow Major
    Security Exploits
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: Model Context Protocol MCP large language models LLMs agentic tools adversarial samples security auditing MCPSafetyScanner