The Model Context Protocol (MCP) is quickly becoming a cornerstone of modern AI development. It enables models to interact with external data sources, APIs, and tools in a structured, standardized way — effectively giving large language models the ability to “see” and “do” more. For developers, it’s transformative. For security teams, it’s a wake-up call.

The surge in MCP adoption has been fueled by open-source enthusiasm. Thousands of MCP servers have already appeared across GitHub, Hugging Face, and other repositories — connecting AI models to everything from Salesforce to Slack, databases to DevOps systems, and beyond. But in the rush to integrate and innovate, many organizations are overlooking a critical fact:
Every MCP server is software. And software can be vulnerable, malicious, or misconfigured.

This is the next evolution of the software supply chain problem — one that extends beyond packages and dependencies into the world of intelligent automation.

The New Supply Chain of AI

Traditional software supply chain attacks exploit the trust developers place in external libraries, frameworks, and open-source dependencies. Attackers inject malicious code, compromise a maintainer’s account, or exploit a vulnerable dependency buried five layers deep in a project. Now, MCP servers have opened a new frontier for similar exploits — but with potentially greater impact.

Each MCP server acts as a broker between an AI model and the outside world. It translates the model’s requests into API calls, executes them, and returns structured results. If an MCP server is compromised, the attacker gains a high-privilege position in this data and command flow. They can inject false data into the model’s reasoning, exfiltrate sensitive context, or perform unauthorized actions on behalf of the model.

Imagine an enterprise AI assistant that uses open-source MCP servers to pull data from cloud storage, HR systems, or ticketing platforms. If just one of those servers includes a hidden data exfiltration function — or depends on an unpatched library — it can undermine the entire AI workflow. The same logic that once applied to npm or PyPI dependencies now applies to MCP integrations.

And unlike traditional code dependencies, MCP servers aren’t just executed once at build time — they’re live, persistent, and often interact dynamically with production systems. That raises the stakes considerably.

Why This Problem Is Growing So Fast

The sheer speed of MCP adoption has outpaced security review. A quick search on GitHub reveals thousands of MCP servers written by individual developers, small startups, and hobbyists. Many have no documentation of security practices, no versioning, and no signed releases. Some are only days old and depend on unverified APIs.

At the same time, the barrier to entry is extremely low. The protocol is simple to implement, and developers can publish a working MCP server in a few hours. That’s great for innovation — but disastrous for supply chain control. Enterprises integrating “community” MCP servers may not realize they’re pulling in unaudited code that can open sensitive pathways between AI agents and enterprise systems.

Compounding the risk, MCP servers are inherently contextually trusted. When an AI model interacts with an MCP server, it assumes the data is accurate and the function is safe. There’s no built-in validation layer between the AI’s reasoning engine and the MCP’s output. If the MCP is compromised, the model won’t question it — it will act on it.

What’s Needed: Visibility, Validation, and Verification

Solving this problem doesn’t mean abandoning open-source innovation — it means treating MCP servers with the same discipline applied to modern software supply chains. Security leaders must start building control points around three key pillars:

1. Visibility into MCP Servers in Use

The first step is understanding what MCP servers are being used across the organization. Just as software composition analysis (SCA) tools inventory code dependencies, AI security teams need visibility into which MCP endpoints their models are connecting to.

This includes:

• Enumerating all active MCP servers integrated into agent workflows or orchestration layers.
• Mapping dependencies and transitively linked APIs for each server.
• Continuously monitoring for new or updated MCP integrations introduced by developers or automated build processes.

Without visibility, there’s no way to measure exposure — and no baseline for security enforcement.

2. Validation of MCP Sources

Next, organizations must validate the origin and trustworthiness of MCP servers before integrating them. This means:

• Preferring MCP servers from verified publishers or known enterprise vendors.
• Checking repository hygiene: documentation, commit history, versioning, and community engagement.
• Scanning for signs of tampering, such as recent unexplained commits, credential leaks, or missing integrity signatures.
• Applying domain and certificate validation for hosted MCP endpoints.

In short: treat MCP servers like any other third-party software vendor. A GitHub repo with no history or maintainer identity should never connect to enterprise data or tools.

3. Testing and Hardening MCP Code

Finally, every MCP server — whether open source or custom-built — must be tested and hardened before deployment. Recommended practices include:

• Static code analysis to detect insecure dependencies, hard-coded credentials, or potential injection paths.
• Dynamic testing and fuzzing of the server’s API endpoints to surface logic flaws or privilege escalation issues.
• Security baselines for how the server handles input/output sanitization, authentication, and authorization.
• Sandboxing MCP interactions so that even if a server misbehaves, its impact is contained.

Because MCP servers often bridge between AI agents and production APIs, even a single overlooked vulnerability can become an enterprise-wide breach vector.

From Code Trust to AI Trust

The broader takeaway is that AI security doesn’t stop at model behavior — it extends into the ecosystem of integrations and extensions that give models their capabilities. Each MCP server, plug-in, and connector adds new code, new context, and new risk.

The software supply chain now includes not just packages, but agents and protocols that dynamically shape what AI systems can do. This demands a shift in mindset: from trusting AI outputs to validating AI inputs, infrastructure, and dependencies.

Visibility, validation, and testing aren’t just DevSecOps hygiene — they’re essential for maintaining the integrity of the AI reasoning loop itself.

How PointGuard AI Helps

PointGuard AI provides enterprises with the tools to secure their entire AI integration ecosystem — including MCP-based workflows and agent architectures.

Our platform helps organizations:

• Discover and inventory all MCP servers and AI integrations used across models, pipelines, and applications.
• Analyze and validate MCP server sources through automated code scanning, dependency assessment, and reputation scoring.
• Continuously test and monitor MCP endpoints for vulnerabilities, tampering, or anomalous behaviors that may indicate compromise.
• Enforce policy controls that restrict AI agents to verified, approved MCP sources — preventing shadow integrations or unvetted open-source usage.

By integrating AI-native visibility and supply chain defense into your security stack, PointGuard AI ensures your AI systems stay both powerful and protected. Because securing AI isn’t just about protecting models — it’s about securing the entire ecosystem that surrounds them.

Secure Your Path to AI Adoption — with full visibility into the tools your AI depends on.