Model Context Protocol (MCP) Servers: A New AI Integration Layer and a New Attack Surface

As enterprises accelerate the adoption of AI assistants and autonomous agents, a new class of infrastructure is quietly becoming critical and risky. Recent analysis highlights how Model Context Protocol (MCP) servers, used to connect large language models (LLMs) to enterprise tools and data, may expose organizations to server takeovers, privilege abuse, and supply-chain attacks if not properly secured.

The MCP is an open integration standard that enables AI models to interact with external systems, such as databases, APIs, file systems, ticketing platforms, and cloud services, via a structured server interface. At a high level, MCP acts as a broker layer between an AI model and enterprise tools. MCP enables AI systems to:

• Discover available tools and functions
• Request structured context or data
• Execute actions on external systems
• Maintain conversational or task-based state

This capability is foundational to agentic AI, in which models don’t just answer questions but also take actions.

1. User Prompt: A user asks an AI assistant to perform a task (e.g., “Pull the latest vulnerability report and open a ticket”).
2. Model Reasoning: The LLM determines it needs external tools or data to complete the task.
3. MCP Server Interaction: The model sends a structured request to an MCP server describing the tool it wants to use, Required parameters, and Context or state.
4. Tool Execution: The MCP server executes the request against enterprise systems (e.g., Jira, GitHub, cloud APIs).
5. Response Returned to Model: Results are sent back to the model, which completes the response or continues the workflow.

This architecture dramatically expands AI capability; however, it also dramatically expands the blast radius of compromise. MCP servers often run with elevated privileges and implicitly trust requests coming from AI models. These risks include:

• Over-Privileged Execution: MCP servers often have broad access to internal APIs, cloud credentials, production data, and administrative functions. If an MCP server is compromised, attackers can inherit these privileges and move laterally across enterprise systems.
• Tool Injection & Context Manipulation: Malicious prompts or poisoned context can manipulate an AI model into issuing unsafe or unauthorized MCP requests. This effectively turns the AI system into an unwitting insider threat executing attacker-driven actions.
• Server Takeover Potential: Compromise of an MCP server host, its configuration, or its authentication tokens can grant attackers control over connected tools and services. This enables arbitrary command execution and widespread impact across integrated environments.
• Supply-Chain & Plugin Risk: Third-party MCP tools or extensions may introduce vulnerable code, hidden behaviors, or weak authentication controls. These risks mirror well-documented supply-chain threats seen in CI/CD pipelines and cloud service integrations.

Traditional controls such as firewalls, endpoint security, and identity management alone are not enough, as MCP servers are privileged service accounts, automated operators, and policy enforcement points. Without intentional architecture and governance, they can undermine Zero Trust principles rather than support them.

MCP is not inherently dangerous; however, it’s powerful. But power without governance creates systemic risk. To reduce risk, MCP deployments should be treated by a high-impact middleware with mitigation strategies such as:

• Apply Least Privilege: Separate MCP servers by function and tightly restrict tool scopes and permissions. This limits blast radius and prevents a single compromise from exposing multiple systems.
• Strong Authentication & Authorization: Enforce mutual TLS or strong token-based authentication for all MCP interactions. Use explicit allow-lists to strictly control which tools and actions an MCP server can invoke.
• Audit & Telemetry: Log every MCP request, response, and executed action in detail. Correlate AI prompts with system activity to enable traceability, forensics, and anomaly detection.
• Input & Output Validation: Implement guardrails on tool parameters to prevent unsafe or unintended execution. Perform sanity checks on both inputs and outputs before actions are carried out.
• Zero Trust Alignment: Verify every MCP request regardless of origin and assume compromise by default. Enforce continuous validation of identity, context, and authorization throughout the session lifecycle.
• Supply-Chain Review: Rigorously vet third-party MCP tools and extensions before deployment. Pin versions, monitor updates, and continuously assess for newly introduced vulnerabilities.