Securing MCP: 5 Safeguards for Enterprise Teams
Protecting Model Context Protocol (MCP) AI Applications
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This blog is a follow-up to our last post on MCP. Check that out if you haven’t yet!
The rise of Model Context Protocol (MCP) is rapidly influencing how organizations will connect their internal tools and data to LLMs. This change requires organizations to think critically about implementing and securing their MCP infrastructure. While the benefits of AI-augmented workflows are clear: faster task completion, reduced context switching, and intuitive interactions with complex systems, these advantages can only be realized if the underlying infrastructure remains secure and trustworthy. The challenge lies in finding the right balance between operational efficiency and security controls, ensuring that AI assistants remain reliable partners rather than entry points for attackers.
This post outlines five security practices that enterprise teams can implement today to secure their MCP deployments while maintaining the protocol's benefits for everyday workflows. These practices represent a defense-in-depth approach to MCP security, addressing everything from credential management to isolated execution. By implementing these safeguards, organizations can confidently embrace MCP while mitigating inherent risks.
Understanding MCP Security
MCP functions as a universal connector between AI models and enterprise tools, essentially serving as the "USB-C for AI" by standardizing how large language models interact with business applications, databases, and other resources for productivity. This improves efficiency by enabling natural language interactions with complex systems to solve broader problems.
The protocol's design is intentionally modular, allowing organizations to connect virtually any tool to any LLM through clients like Cursor or Claude. This modularity creates tremendous flexibility but also introduces complexity from a security perspective. Each new tool represents another potential attack vector, and the interconnected nature of MCP means that compromising one component could affect a broader ecosystem.
The architectural foundation of MCP consists of servers, which provide tool capabilities, and clients, the interfaces through which AI models interact with these tools. Servers require direct access to sensitive systems and data, while clients make intelligent decisions about which tools to use based on user inputs and model reasoning capabilities. MCP creates a standardized integration framework where vendors or independent developers can produce servers as middleware between AI assistants and the tools they need to accomplish tasks. This design democratizes AI tool creation, but like most emerging protocols, MCP was initially optimized for functionality rather than security.
Today's implementation leaves organizations vulnerable to multiple attack vectors from credential theft and tool poisoning to prompt injection attacks that manipulate AI behavior. As the standard matures throughout 2025, we anticipate that robust security controls will emerge, but organizations need practical solutions today to protect their existing deployments. Here are five essential safeguards teams can apply today:
Secure Credential Management: Create dedicated service accounts with minimal permissions and rotate credentials regularly.
Supply Chain Security: Implement an approval process for MCP servers and use specialized scanning tools to detect malicious instructions.
Environment Isolation: Run MCP servers in containerized or sandboxed environments to limit their access to sensitive files and network resources.
Prevent Prompt Injection: Keep AI sessions separate, limit tool access to what's necessary for specific tasks, and watch for unusual AI behavior.
Comprehensive Monitoring: Enable logging for all MCP activity, monitor network traffic patterns, and conduct regular security reviews.
In the following sections, we'll explore these safeguards in detail, examining the specific attack vectors they address and providing practical implementation guidance for organizations at any stage of MCP adoption.
1. Secure Credential Management
The lowest hanging fruit in MCP deployments today is secrets management. MCP servers require access tokens to authenticate with business systems, including Salesforce, Microsoft 365, AWS, and internal databases, and these are typically stored in plaintext files on disk. For example, to configure an MCP Server with Claude Desktop, the following JSON file is configured:
{
"mcpServers": {
"notion": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"INTERNAL_INTEGRATION_TOKEN",
"mcp/notion"
],
"env": {
"INTERNAL_INTEGRATION_TOKEN": "ntn_****"
}
}
}
}Security Challenge: If an attacker obtains this file, they could compromise your MCP server secrets and potentially gain access to all connected tools, bypassing traditional security controls. Unfortunately, most user-friendly MCP clients lack support for secure credential management using tools like Hashicorp Vault or 1Password. As a result, API tokens are stored in plaintext, and this problem compounds when running multiple servers at once, creating a sprawl of high-privilege credentials across workstations.
Implementation Strategy:
Pick MCP clients with encryption support for application secrets:
Tools like Codename Goose handle secure credential management
Write a client that utilizes standard SDKs for encrypted credentials
Create service accounts with limited credential scope and lifespan:
Use dedicated service accounts for MCP tool integrations
Use restrictive scopes, using read-only where full access isn’t mandatory
Rotate credentials regularly, at least every 30 days
Physical endpoint protection:
Enable full-disk encryption and use endpoint protection
Apply all security updates promptly
While these mitigations aren't perfect, they significantly reduce the risk surface until MCP clients integrate with secure credential management solutions. Organizations should also advocate for better credential security features from MCP client developers, as this represents one of the most significant security gaps in the current ecosystem.
2. Supply Chain Security
MCP's open ecosystem allows anyone to develop and distribute tool implementations, creating significant supply chain risks for organizations. This openness enables rapid innovation but also introduces the potential for malicious or compromised tools to infiltrate your systems. The lack of a singular, trusted MCP registry deepens this issue, where it’s difficult to discern “official” servers from community-contributed ones.
Security Challenge: The MCP ecosystem faces two distinct but related supply chain threats: malicious tools deliberately created to cause harm, and legitimate tools that become compromised through updates or dependencies. Either scenario can lead to data exfiltration, credential theft, or persistent access to enterprise infrastructure. Tool poisoning represents a particularly insidious risk where attackers hide malicious instructions within tool descriptions. These instructions are often invisible to users but can manipulate the AI into performing unauthorized actions whenever the tool is used. For example, a tool might claim to analyze documents while secretly instructing the AI to exfiltrate sensitive data through seemingly legitimate parameters.
Implementation Strategy:
Implement a tool approval process:
Create a list of approved MCP servers and tools.
Verify digital signatures where available.
Prioritize tools from established vendors over unknown sources.
Use trusted scanning tools to detect poisoned descriptions:
Use mcp-scan to check installed MCP servers for vulnerabilities
Run scans after installing new tools and after updates.
Look for unusually long or complex descriptions, particularly instructions like "before using this tool" or "do not tell the user."
Implement MCP version pinning:
Pin to specific versions rather than using "latest."
Test updates in non-production environments first.
Review release notes and changes before upgrading.
These practices help ensure that the tools your AI assistants use behave as expected, without hidden instructions or malicious functionality that could compromise your business systems. As the MCP ecosystem grows, expect security vendors to develop more sophisticated tools for detecting and preventing supply chain attacks, along with a standard and trusted MCP registry.
3. Environment Isolation
MCP servers function as automated intermediaries between AI models and your business infrastructure. Controlling this risk requires firm isolation limits for which tools and resources are accessible and shared.
Security Challenge: Local MCP servers typically inherit the permissions of the user who launched them, which means broad access to local files, network resources, and potentially sensitive data. If a server is compromised through a malicious tool or misconfiguration, it could lead to data exposure and exfiltration.
This risk is further complicated because many MCP implementations are still relatively new, with security features that continue to evolve. Running these servers directly on employee workstations, a typical pattern in early adoption, creates significant risk if proper isolation isn't in place. The situation resembles the early days of container adoption, when organizations sometimes deployed containers without adequate security controls, only to discover significant vulnerabilities later.
Implementation Strategy:
Use containerization to isolate MCP servers from your host system:
Deploy MCP servers in Docker containers with minimal permissions.
Use read-only file systems where possible.
Apply network controls and limit where MCP servers can connect:
Run MCP behind a proxy to control inbound/outbound connections.
Segment MCP servers from other critical infrastructure.
These isolation techniques create firm boundaries around MCP servers, containing potential damage if they become compromised. The approach should be tailored to your organization's risk tolerance and technical capabilities—more sensitive environments warrant stronger isolation controls.
4. Prevent Prompt Injection
MCP tools extend AI capabilities by connecting large language models to your business applications, data sources, and services. While this creates powerful workflows, it also introduces security risks when AI processes external content that might contain hidden instructions.
Security Challenge: Be aware of "prompt injection attacks," where malicious instructions hidden in documents, emails, or web pages can manipulate the AI into performing unauthorized actions using the tools at its disposal. This type of attack, known as indirect prompt injection, occurs when the AI misinterprets embedded instructions in external content as valid commands from you. For example, a seemingly innocent document could trick the AI into forwarding sensitive information, modifying settings, or performing other unauthorized actions with your connected tools and permissions.
Implementation Strategy:
Keep AI sessions separate and focused:
Never analyze untrusted external content and perform sensitive business tasks in the same AI session.
Start fresh sessions when switching between different security contexts, and close MCP sessions entirely when not actively using them.
Apply the principle of least access:
Only connect to specific tools and data sources necessary for your task.
Avoid running too many simultaneous MCP servers at once.
Watch for unusual AI behavior:
AI suggesting unrelated actions
AI attempting to access unexpected systems
AI making recommendations that seem out of context
These simple precautions significantly reduce the risk of prompt injection attacks without requiring technical expertise. As MCP transitions from today's predominantly local server deployments to more secure remote implementations throughout 2025, maintaining these security habits will help ensure your AI assistants remain reliable partners rather than potential security vulnerabilities.
5. Comprehensive Monitoring
Even with preventative controls, organizations need visibility into MCP activities to detect and respond to potential compromises or misuse.
Security Challenge: MCP servers often operate behind the scenes, making their activities difficult to monitor through traditional tools. When coupled with AI-driven interactions, this creates a significant visibility gap in security operations. Actions that usually generate obvious audit trails (like an employee accessing a CRM platform) might happen through MCP with less visibility, creating potential blind spots in monitoring.
2025-04-30T21:17:20.242Z [info] Initializing server...
2025-04-30T21:17:20.281Z [info] Server started
2025-04-30T21:17:20.283Z [info] {"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"claude-ai","version":"0.1.0"}},"jsonrpc":"2.0","id":0}
2025-04-30 21:17:22,270 - Initializing server 'mcp-panther'
2025-04-30 21:17:22,271 - Registering 30 tools with MCPImplementation Strategy:
Enable built-in logging:
Check your MCP client for logging configuration options.
Direct logs to your central logging platform when possible.
Implement network-level monitoring:
Monitor for data volume anomalies that could indicate exfiltration.
Consider deploying dedicated monitoring for MCP traffic.
Implement regular security reviews:
Audit installed tools and their permissions.
Validate that security controls remain effective.
These monitoring and response capabilities create the visibility needed to detect MCP-related security issues early and respond effectively. As the protocol matures, we can expect more sophisticated monitoring tools and best practices to emerge; however, organizations should not wait for these developments before implementing basic visibility controls.
Balancing Security and Productivity
Implementing the five safeguards—secure credential management, supply chain security, environment isolation, prompt injection prevention, and comprehensive monitoring—creates a robust framework for securing MCP deployments while maintaining productivity benefits. Rather than blocking MCP usage, organizations should implement it in a controlled, secure manner through:
User education: Train employees on MCP security risks and best practices, establishing clear guidelines that balance productivity and security.
Regular reviews: Audit MCP configurations and connected tools with the same rigor as critical infrastructure components.
Risk-based implementation: Apply stricter controls to high-sensitivity environments while allowing appropriate flexibility for lower-risk scenarios.
Security best practices will evolve alongside the MCP ecosystem as it matures and transitions from local to remote hosted deployments. Security vendors and cloud platforms are already developing specialized controls and environments for MCP; however, organizations should not wait for perfect solutions before beginning their journey. The future of work will increasingly involve AI assistants working alongside human employees, with MCP providing the connective tissue that makes this collaboration possible at scale.
Organizations that find the right balance—embracing both MCP's transformative potential while implementing these critical safeguards—will gain significant advantages in efficiency and effectiveness, achieving secure AI integration at scale in a responsible manner. The goal isn't to avoid MCP adoption—it's to adopt it securely, creating a foundation for AI-human collaboration that can grow with your organization.
Any ideas on how to setup a small MCP lab for blue team research?