MCP Is the New Attack Surface. Let's Lock It Down.
The Model Context Protocol is rapidly becoming the standard for connecting AI models to tools, data, and services. But every MCP server you integrate is a potential entry point. IronProbe specialises in reviewing, hardening, and securing MCP deployments before they become a problem.
Why MCP Security Can't Be Ignored
MCP integrations connect your AI models directly to your systems - making them a high-value target for attackers
Stop MCP from Becoming an Entry Point
Every MCP server you connect to your AI model is a potential entry point for attackers. A single vulnerable MCP integration can give an attacker the ability to manipulate your AI model's behaviour, exfiltrate data, or execute commands on connected systems.
Protect Against Indirect Prompt Injection
Attackers can embed malicious instructions in tool outputs returned by MCP servers - hijacking the AI model's behaviour through data it trusts. Our testing specifically targets this attack vector, which is often invisible to both developers and users.
Meet Enterprise Security Requirements
Enterprise customers and regulated industries increasingly require security evidence for AI integrations. Our MCP security assessment provides the documentation and assurance you need for procurement questionnaires, compliance audits, and customer due diligence.
What's Included
End-to-end MCP security coverage from server assessment to adversarial integration testing
MCP Server Security Assessment
We perform a full security review of your MCP server implementation - examining how it handles requests, exposes tools, manages sessions, and interacts with underlying systems. We identify misconfigurations and vulnerabilities before they can be exploited.
Authentication & Authorisation Review
Weak or absent authentication on MCP endpoints is one of the most common vulnerabilities we find. We review your auth mechanisms, token handling, session management, and access control logic - ensuring only authorised AI models and users can access your MCP server.
Input Validation & Output Sanitisation
MCP servers that process AI-generated tool calls without proper input validation are vulnerable to injection, command execution, and data corruption attacks. We test every input pathway and ensure outputs are sanitised before being returned to the AI model.
Tool Exposure & Permission Audit
Every tool exposed through your MCP server represents a capability an AI model can invoke. We audit exactly which tools are exposed, whether they're scoped correctly, and whether a manipulated AI model could use them to take unintended or destructive actions.
Secure MCP Server Development Guidance
Building a new MCP server? We provide hands-on security guidance during development - including secure architecture patterns, recommended libraries, hardening checklists, and code review - so security is built in from the first commit.
Integration Testing with Adversarial Scenarios
We test the full integration - AI model to MCP server to backend - using adversarial scenarios that simulate real attacks: prompt injection via tool outputs, permission escalation, cross-tool contamination, and denial-of-service through resource exhaustion.
Our Testing Methodology
A three-phase adversarial assessment designed specifically for MCP environments
Enumerate & Map
We begin by fully enumerating your MCP integration - cataloguing every exposed tool, resource, and prompt; mapping data flows between the AI model and backend systems; and identifying all trust boundaries in scope for testing.
Adversarial Test
We attack your MCP integration from multiple angles: directly as a malicious user, indirectly through manipulated data sources, and via logic abuse. We attempt to exfiltrate data, escalate permissions, and make the AI model take unintended actions.
Harden & Verify
We deliver a detailed report covering all findings with severity ratings and developer-ready remediation steps. We then re-test the integration after fixes are applied to verify that vulnerabilities are fully resolved.
Why Choose IronProbe for MCP Security?
Built for teams integrating Claude, GPT, or other LLMs with external tools via MCP - especially in enterprise or regulated environments.
MCP-Specialist Knowledge
We've studied the MCP specification in depth and maintain current knowledge of the evolving threat landscape for AI tool integrations. We find vulnerabilities that generic security reviews miss.
End-to-End Integration Coverage
We test the full stack - AI model, MCP transport layer, server implementation, and backend systems - not just the server in isolation. Real attacks span the whole chain.
Enterprise-Ready Security Evidence
Our reports are structured to satisfy enterprise security questionnaires, compliance requirements, and customer due diligence processes - giving you the documentation you need to sell confidently into regulated markets.
Frequently Asked Questions
Common questions about MCP security assessment
The Model Context Protocol (MCP) is an open standard that allows AI models like Claude and GPT to connect to external tools, APIs, and data sources. It's powerful - but each MCP server you connect to your AI model is an additional attack surface. Vulnerabilities in MCP implementations can allow attackers to manipulate what the AI model sees, control what actions it takes, or use the integration as a bridge to access sensitive backend systems.
Yes. We test any MCP server - whether it's a third-party server from the MCP ecosystem, an open-source implementation you're hosting yourself, or a custom MCP server your team has built. Each has different risk profiles: third-party servers may have known vulnerabilities or hidden behaviours; custom servers often have implementation flaws that only emerge under adversarial testing.
Indirect prompt injection via MCP occurs when an attacker embeds malicious instructions in data that your MCP server returns to the AI model - for example, in a document retrieved from a file server, a database record, or a webpage fetched by a web-search tool. The AI model reads the attacker's instructions as trusted content and follows them. We test this by constructing adversarial payloads and injecting them at every point where externally-sourced data could enter the model's context through MCP tool outputs.
Standard MCP assessments for a single server with 5-15 tools typically take 3-5 days. More complex integrations with multiple MCP servers, custom authentication schemes, or enterprise backend connections may take longer. We also offer faster rapid-review engagements focused on the highest-risk tool categories if you need a quicker turnaround ahead of a launch or compliance deadline.
Harden Your MCP Integrations Before Attackers Find Them
Don't give attackers a direct line into your systems through AI tool integrations. Get your MCP implementation independently assessed today.
Get a Free MCP Security Assessment