Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions

1 INTRODUCTION

• MCP emerged amid growing tool use but persistent fragmentation, with developers manually managing interfaces, authentication, and execution across platforms.Existing approaches also rely on predefined workflows, limiting dynamic tool discovery and orchestration.Source: §1 · p. 2
• MCP enables agents to discover, select, and orchestrate tools dynamically, while supporting human approval or data input.The paper describes adoption by clients and a growing community-server ecosystem.Source: §1 · p. 2
• The paper presents the first MCP ecosystem analysis, covering architecture, components, and workflow.It also examines adoption, diversity, and use cases across industries and platforms.Source: §1 · p. 2
• It defines the server lifecycle as creation, operation, and update, and identifies security risks at each phase.The paper also outlines ways to safeguard AI-to-tool interactions.Source: §1 · p. 2
• The paper discusses challenges for stakeholders and future research on security, scalability, and governance to support sustainable growth.Its roadmap covers the MCP landscape, security and privacy risks, future challenges, and prior work.Source: §1 · pp. 2–3

2.1 AI Tooling

• MCP provides a standardized protocol for interaction with multiple tools, in contrast to approaches that integrate each service through a specific API.Figure 1 compares tool invocation with and without MCP; the text identifies complexity and limited scalability as challenges of service-specific integration.Source: §2.1 · p. 3, Figure 1 · p. 3

2.1.1 Manual API Wiring.

• Manual API wiring requires custom authentication, data transformation, and error handling for each integration, creating maintenance burdens and fragile systems as APIs grow.MCP offers a unified interface for dynamic connections without custom API wiring.Source: §2.1.1 · p. 3

2.1.2 Standardized Plugin Interfaces.

• Plugin interfaces standardized schemas but often lacked two-way, stateful multi-step interaction, while platform-specific plugin stores limited cross-platform compatibility.MCP is described as open-source and platform-agnostic, supporting richer two-way interactions and complex workflows.Source: §2.1.2 · p. 3

2.1.3 AI Agent Tool Integration.

• Agent frameworks still required largely manual integration and maintenance, while MCP unifies invocation, interaction, and chaining of tools.Unlike passive retrieval methods such as RAG, MCP also enables active operations on external systems.Source: §2.1.3 · pp. 3–4, §2.1.4 · p. 4

2.2 Motivation

• MCP adoption is rapid, but its ecosystem remains incomplete in areas including security and tool discoverability, and it remains largely unexplored in academia.The paper responds with an analysis of the landscape and emerging ecosystem, identification of potential security risks, and a vision for future development.Source: §2.2 · p. 4

3.1 Core Components

• The architecture comprises a host, client, and server; in the typical workflow, the client analyzes a user prompt, selects server tools, invokes external APIs, and returns results.Figure 2 depicts the MCP workflow.Source: §3.1 · p. 4, Figure 2 · p. 5

3.1.1 MCP Host.

• The MCP host is the AI application that provides the task-execution environment and runs the MCP client.Examples include Claude Desktop, Cursor, and autonomous AI agents.Source: §3.1.1 · p. 4

3.1.2 MCP Client.

• The MCP client mediates host-server communication by querying available functions, processing responses and notifications, and communicating over the transport layer.The client also samples tool usage and performance data.Source: §3.1.2 · p. 5

3.1.3 MCP Server.

• MCP servers expose tools for external operations, resources for structured or unstructured data, and prompts as reusable workflow templates.The server identifies and invokes requested tools, retrieves requested data, and maintains prompts and workflows.Source: §3.1.3 · pp. 5–6

3.2 Transport Layer and Communication

• The transport layer carries client requests, server capability listings, responses, and continuing notifications between client and server.Notifications communicate server-status changes and updates to the client in real time.Source: §3.2 · p. 6

3.3 MCP Server Lifecycle

• The lifecycle comprises creation, operation, and update, with distinct activities in each phase.Addressing phase-specific challenges supports server security, efficiency, and adaptability.Source: §3.3 · p. 6, §3.3.4 · p. 7

3.3.1 MCP Server Components.

• Metadata, configuration, tool and resource lists, and prompts and templates make up the MCP server’s core components.Together, they support tool integration, data retrieval, and task orchestration.Source: §3.3.1 · pp. 6–7

3.3.2 Creation Phase.

• Creation registers and configures the server, deploys its installer, and verifies code integrity before operation.Registration gives the server a unique identity for client discovery; integrity verification aims to prevent unauthorized code changes.Source: §3.3.2 · p. 7

3.3.3 Operation Phase.

• During operation, the server processes requests and tool invocations, handles commands, and enforces sandbox isolation.Command handling manages potential overlaps, while sandboxing helps prevent unauthorized access.Source: §3.3.3 · p. 7

3.3.4 Update Phase.

• Updates require valid access permissions, version consistency, and deactivation or removal of outdated server versions.The paper identifies old versions as a potential means for attackers to exploit known vulnerabilities.Source: §3.3.4 · p. 7

4.1.1 Key Adopters.

• MCP adoption is reported among AI companies and developer tools, while Cloudflare, Block (Square), and Stripe are exploring it for security, scalability, and governance in multi-tenant environments.Source: §4.1.1 · pp. 7–8

4.1.2 Community-Driven MCP Servers.

• Community platforms host server collections, while desktop solutions support local MCP deployment.MCP.so, Glama, and PulseMCP host thousands of servers; Dockmaster and Toolbase support local deployment.Source: §4.1.2 · p. 8

4.1.3 SDKs and Tools.

• Official SDKs and community tools offer multiple ways to build, expose, host, and manage MCP servers.SDKs include TypeScript, Python, Java, Kotlin, and C#; tools include FastMCP, FastAPI to MCP Auto Generator, and server-generation platforms.Source: §4.1.3 · pp. 8–9

4.2 Use Cases

• The use-case examples examine OpenAI, Cursor, and Cloudflare integrations.They cover AI agents and SDKs, development workflows, and remote hosting.Source: §4.2 · p. 9

4.2.1 OpenAI: MCP Integration in AI Agents and SDKs.

• OpenAI’s Agent SDK routes requests requiring external tools through an MCP server to return responses to the agent.The passage also describes plans to extend MCP support to the Responses API and ChatGPT desktop applications.Source: §4.2.1 · p. 9

4.2.2 Cursor: Enhancing Software Development with MCP-Powered Code Assistants.

• Cursor’s code assistants use MCP to access external APIs and repositories and automate development tasks within the IDE.Examples include running API tests, modifying files, and analyzing code.Source: §4.2.2 · pp. 9–10

4.2.3 Cloudflare: Remote MCP Server Hosting and Scalability.

• Cloudflare hosts remote MCP servers that agents access through authenticated API calls, including OAuth-based authentication.Its multi-tenant architecture provides separate instances for users; the paper describes this as supporting isolation and preventing data leakage.Source: §4.2.3 · p. 10

5 SECURITY AND PRIVACY ANALYSIS

• MCP servers face security risks throughout their lifecycle because they are open and extensible platforms.The analysis considers creation, operation, and update.Source: §5 · p. 10
• Each lifecycle phase presents distinct challenges that require mitigation.The paper states that unaddressed challenges can compromise system integrity, data security, and user privacy.Source: §5 · p. 10
• The analysis organizes its security threats around the creation, operation, and update phases.This lifecycle framing connects security risks to the phases of MCP server management.Source: §5 · p. 10

5.1.1 Name Collision.

• Name collision can let a deceptive server identity mislead clients into installing a malicious server.The paper proposes namespace policies, cryptographic verification, and reputation-based trust systems as possible safeguards.Source: §5.1.1 · pp. 10–11

5.1.2 Installer Spoofing.

• Unofficial auto-installers simplify setup but can distribute tampered servers or misconfigured environments.Smithery-CLI, mcp-get, and mcp-installer are examples; the paper calls for secure installation standards, package-integrity checks, and reputation mechanisms.Source: §5.1.2 · p. 11, Table 3 · p. 11

5.1.3 Code Injection/Backdoor.

• Code injection can embed persistent backdoors through compromised dependencies, vulnerable build pipelines, or unauthorized source changes.The paper recommends code-integrity verification, dependency management, security audits, reproducible builds, and checksum validation.Source: §5.1.3 · pp. 11–12

5.2.1 Tool Name Conflicts.

• Deceptive tool names or descriptions can misdirect selection, hijack workflows, and expose sensitive information.The authors observed that descriptions urging clients to prioritize a tool made selection more likely, even when its functionality was inferior or potentially harmful.Source: §5.2.1 · p. 12

5.2.2 Slash Command Overlap.

• Overlapping slash commands can trigger unintended actions, from data loss to system instability.The paper recommends context-aware resolution, disambiguation, and prioritization based on verified tool metadata.Source: §5.2.2 · p. 12

5.2.3 Sandbox Escape.

• Sandbox flaws can let malicious tools escape confinement, access the host, and execute arbitrary code.Potential vectors include system-call weaknesses, improper exception handling, third-party library vulnerabilities, and side channels.Source: §5.2.3 · pp. 12–13

5.3 Security Risks in the Update Phase

• Updates also require managing server versions, modifying configurations, and adjusting access controls.The paper identifies privilege persistence, vulnerable-version redeployment, and configuration drift as the phase’s three critical risks.Source: §5.3 · p. 13

5.3.1 Post-Update Privilege Persistence.

• Revoked or changed privileges may remain active after updates when API keys, tokens, or permissions are not synchronized or invalidated.The paper recommends consistent propagation of revocations, automatic credential expiration, and logging and auditing privilege changes.Source: §5.3.1 · p. 13

5.3.2 Re-deployment of Vulnerable Versions.

• Decentralized distribution and community-driven patches make vulnerable-version redeployment more likely through rollbacks, delayed updates, and unverified sources.Unofficial installers may use cached versions or lack version verification; the paper calls for a standardized package system and centralized registry.Source: §5.3.2 · p. 13

5.3 Security Risks in the Update Phase

• Configuration drift accumulates deviations from security baselines through manual changes, missed updates, or conflicting modifications.In remote or cloud multi-tenant servers, misconfiguration can affect multiple users or organizations; automated validation and consistency checks are proposed.Source: §5.3.3 · p. 14

6.1 Implications

• MCP reduces integration work and supports complex workflows, while deeper access to sensitive data and critical operations raises risks from unverified tools and misconfiguration.The paper also describes benefits for maintainers and the broader AI community, including cross-system coordination, alongside concerns about privacy, fair tool selection, and misuse.Source: §6.1 · pp. 14–15

6.2 Challenges

• Challenges span decentralized oversight, inconsistent authentication, weak monitoring, workflow state and error recovery, multi-tenant isolation, and smart-environment responsiveness and interoperability.In smart environments, heterogeneous devices can require custom adaptations, and compromised servers may enable unauthorized control of critical systems.Source: §6.2 · p. 15

6.3 Recommendations for MCP stakeholders

• Recommendations include verified packages and registries, secure coding and configuration management, security research on vulnerabilities and orchestration, and vigilant server use.Researchers are urged to investigate state management, error recovery, and workflow validation; end-users should favor verified servers and regularly update them.Source: §6.3 · p. 16

7.1 Tool Integration in LLM Applications

• Tool-integration research ranges from surveys and documentation automation to benchmarks, selection methods, tool creation, and agent taxonomies.Examples include Shen et al.[44], AutoTools[45, 46], EasyTool [59], ToolSandbox [30], UltraTool [23], AvaTaR [54], Toolken+[56], ToolMaker[53], and Li.Source: §7.1 · p. 17
• The paper presents MCP as a potential unifying context protocol and argues that its new attack surfaces merit deeper security investigation.The motivation is a perceived lack of a standardized, secure, and extensible context protocol for tool-augmented LLMs.Source: §7.1 · p. 17, §7.2 · p. 17

7.2 Security Risks in LLM-Tool Interactions

• Security work studies tool misuse, threat taxonomies, detection, alignment, and pipeline risks, but MCP may preserve or evolve these threats.Examples include Fu et al. [17], Gan et al.[18], Yu et al.[58], AgentGuard, ToolFuzz[36], and Ye et al.[57].Source: §7.2 · p. 17

• The analysis covers MCP architecture, core components, operational workflows, and server lifecycle stages.Source: §8 · p. 18
• It identifies potential security threats across server creation, operation, and update, and proposes recommendations to improve security and governance.Source: §8 · p. 18
• The paper examines MCP adoption, diversity, and use cases, and outlines research directions for emerging risks and resilience.Source: §8 · p. 18