Securing the Bridge: Where AI meets Enterprise Data

Imagine building a bridge between your application and an AI agent — one that lets your app seamlessly feed information to the AI while maintaining control over what it can access. That's exactly what the Model Context Protocol (MCP) provides: a groundbreaking standard that transforms how applications communicate with Large Language Models (LLMs).

Where our Enterprise Strategies for MCP Integration article introduces the importance of MCP, here we shine a light on specific vulnerabilities that can arise in implementations, how risk creeps in when untrusted inputs meet unsafe code and excessive privilege, and how Javelin’s Ramparts scanner can protect your agentic systems.

Introduction

MCP is a standardized protocol that enhances interaction between applications and LLMs. It enables applications to provide structured context — resources, tools, and prompts — to LLMs. By keeping these tasks separate, the LLM can focus on processing and generating responses while the application handles context provisioning. Ultimately, MCP creates a cleaner architecture where applications can expose specific resources, tools, and prompts to LLMs in a structured, efficient manner. For example, an LLM could use an MCP-enabled tool to check a user’s calendar, locate a stock price, or read a file from a corporate network. But like any powerful technology, this bridge can become a pathway for threats if not properly secured.

The MCP Rule of 2

Inspired by Chromium's security principle, we propose the MCP Rule of 2 for secure MCP implementations: Never combine more than two of the following risk factors:

• Untrusted inputs - Data or instructions that originate from an external, unverified source, like user prompts, tool outputs, third-party/business content
• Unsafe implementation - Code with vulnerabilities or flawed logic, like unsandboxed tool wrappers (e.g., shell/HTTP), weak argument validation/schemas, unsigned manifests, insecure server configurations, risky languages/modules without isolation
• High privilege - The ability to access sensitive data or perform critical system actions, like tools with write/delete/transfer powers, cross-tenant data access, long-lived credentials, or CI/CD and production controls exposed through MCP

When all three factors converge, you create a perfect storm for exploitation. For example, if an AI agent has high privilege (like access to a database) and a developer uses an unsafe implementation (like an unsanitized command), an untrusted input from a malicious actor could lead to a data breach. By eliminating at least one factor using input validation, secure coding practices, or privilege restriction, you significantly reduce your vulnerability surface. This is where tools like Javelin’s Ramparts, designed to identify this dangerous convergence, become essential.

Why Developers Should Care

As AI integration becomes a new standard of operating, implementing it securely is a critical business requirement. These vulnerabilities pose substantial risks to organizations deploying MCP-enabled applications, including:

• Data Breaches: A successful prompt injection attack could trick an LLM into revealing sensitive customer information, trade secrets, or internal documents — potentially affecting millions of users and triggering regulatory penalties.
• System Compromise: Malicious code execution could give attackers full control over servers, leading to lateral movement within networks and potentially compromising an entire organization's infrastructure.
• Reputation Damage: Imagine headlines announcing that your AI agent was tricked into revealing customer data or spreading misinformation. The trust damage can be irreparable, especially for companies in sensitive industries like healthcare or finance.
• Financial Loss: Beyond regulatory fines, exploits like token theft could lead to fraudulent use of paid services (like running up massive bills on cloud platforms) or unauthorized financial transactions.

How Javelin Helps Mitigate MCP Security Risks

Recognizing the unique security challenges of MCP implementations, Javelin has developed Ramparts, a fast, lightweight security scanner purpose-built for MCP servers. Engineered with Rust for optimal performance and minimal overhead, Ramparts specifically targets the vulnerabilities we've discussed throughout this blog.

Ramparts excels at identifying and preventing sophisticated threats like tool poisoning, tool shadowing, and rug pulls by scanning your AI systems for configuration vulnerabilities and indirect attack vectors. Its specialized design allows it to analyze complex prompt contexts, tool manifests, and server topologies with exceptional efficiency - providing a crucial security layer that traditional tools simply weren't designed to address.

By integrating seamlessly into your development pipeline, Ramparts helps ensure that the Rule of 2 is enforced across your MCP implementations, preventing the dangerous convergence of untrusted inputs, unsafe implementations, and excessive privileges that lead to exploitable vulnerabilities.