Secure Your AI-Generated Code with Socket MCP

The world of software development is undergoing a seismic shift. AI-powered coding assistants, like GitHub Copilot and Cursor, are no longer novelties but increasingly integral parts of our daily workflows. They promise (and often deliver) a future where we can build faster, prototype more rapidly, and even learn new languages and frameworks with unprecedented ease. But as we race to embrace this AI code revolution, a shadow looms – a new generation of security risks quietly embedding themselves deep within our software supply chains.

While AI tools can churn out functional code in seconds, they don't inherently prioritize security. This is where the real challenge begins. Proactively securing our software dependencies, especially those suggested or generated by AI, is no longer a "nice-to-have" – it's a critical necessity. Today Socket is releasing socket-mcp, an experimental Model Context Protocol (MCP) server that leverages the power of Socket to assess AI-generated dependencies.

AI-Generated Code Risks#

The allure of AI-generated code is strong, but its convenience can mask underlying vulnerabilities. Understanding these risks is the first step towards mitigating them.

• The "Black Box" Problem: AI models, particularly large language models (LLMs), can sometimes feel like black boxes. We provide a prompt, and code appears. But how that code was constructed, what data it was trained on, and what subtle vulnerabilities might be lurking within its logic isn't always transparent. This opacity can lead to the unintentional introduction of security flaws.
• AI's Bias Towards Functionality, Not Security: AI models are typically optimized to produce code that works. They learn from vast datasets of public code, which, unfortunately, doesn't always exemplify best security practices. The primary goal is often to satisfy the functional requirements of a prompt, with security considerations taking a backseat unless explicitly and expertly requested.
• "Slopsquatting" – AI's Dependency Hallucinations: A particularly insidious threat highlighted by security researchers is "slopsquatting." This occurs when an AI model "hallucinates" a non-existent but plausible-sounding package name within its generated code. For example, an AI might suggest importing ultra-validator-plus when no such package exists. Attackers can anticipate these common hallucinations, register these "slop" package names, and upload malicious code. Unsuspecting developers, trusting the AI's output, then install these malicious packages, compromising their applications. Research has shown that a significant percentage of these hallucinated package names are repeated consistently by AI models, making this a predictable and scalable attack vector.
• The Amplification Effect: If a single malicious package becomes a common suggestion by a popular AI coding tool, the potential for widespread compromise is enormous. What might have been an isolated incident can quickly become a supply chain catastrophe, as countless developers unknowingly incorporate the malicious dependency into their projects.

Understanding the Model Context Protocol (MCP)#

As AI models become more sophisticated, they need to interact with the world beyond their training data. This is where the Model Context Protocol (MCP) comes into play.

• What is MCP? MCP is an open protocol that standardizes how applications provide context to LLMs. It's designed to allow AI models and agents to dynamically request, fetch, and manage contextual information from various external tools and data sources at runtime. This could mean an AI directly querying a database, accessing files on a local system, or interacting with third-party APIs.
• The Power of Context: MCP empowers AI assistants to perform much more complex and useful tasks. For instance, an AI could not only generate code for a new feature but also use MCP to connect to your CI/CD pipeline to run tests, or query a live API to fetch data needed for the code it's writing. This makes AI a more active and integrated partner in the development process.

Introducing Socket MCP: Real-Time Dependency Checks for Your AI Assistant#

To combat the risks of AI-generated dependencies, we need more than just a safety net at the end of the development cycle. We need real-time intelligence, delivered directly within the tools we use to write code.

This is why we've built the Socket MCP Server, a new experimental way to bring Socket's deep dependency analysis directly into your conversations with AI assistants.

The Socket MCP Server is a lightweight server that implements the Model Context Protocol (MCP), allowing AI tools like Claude or VS Code Copilot to connect to and use Socket's powerful security API. The server exposes a simple but powerful tool to your AI:

The Socket MCP Server exposes our new depscore tool that allows your AI assistant to query the Socket API on the fly for any package you're considering. It provides a comprehensive set of security and quality metrics, including:

• Supply Chain Risk Score: Score factors relating to supply chain security.
• Quality Score: Metrics on code quality, popularity, and community health.
• Vulnerability Score: Score factors relating to package vulnerabilities
• Maintenance Score: Score factors to how well the package is maintained.

For more details about the scores see here: https://docs.socket.dev/docs/package-scores

Parameters for depscore:

• ecosystem: The package ecosystem (e.g., npm, PyPI). Defaults to npm.
• depname: The name of the dependency.
• version: The version of the dependency. Defaults to unknown.

With Socket MCP, you are no longer reacting to a security report in a pull request. You are actively querying for security information as part of your creative coding process.

A Practical Guide: Using Socket MCP with Your AI Assistant#

Getting started with Socket MCP is straightforward. It's designed to integrate smoothly into your existing AI-powered development environment.

First, Get a Socket API Key

To use the Socket MCP Server, you'll need an API key. You can create one for free by following the steps in our API documentation. Once you have your key, keep it handy.

Integrating with VS Code

You can bring the same power directly into your editor.

1. Install in VS Code: The easiest way is to use the direct installation link, which will prompt you for your API key.

Alternatively, you can manually configure it.

1. Set the API Key (if not using the direct install link's prompt):

export SOCKET_API_KEY=your_api_key_here

2. Manual VS Code Configuration: In VS Code settings, add the Socket MCP server to your .vscode/mcp.json file:

{
    "inputs": [
        {
            "type": "promptString",
            "id": "socket_api_key",
            "description": "Socket API Key",
            "password": true
        }
    ],
    "servers": {
        "socket-mcp": {
            "type": "stdio",
            "command": "socket-mcp",
            "args": [],
            "env": {
                "SOCKET_API_KEY": "${input:socket_api_key}"
            }
        }
    }
}

3. Automate Checks with Custom Rules: This is where it gets really powerful. You can instruct your AI assistant (like GitHub Copilot in VS Code) to always check dependencies by creating or editing a .github/copilot-instructions.md file in your project with rules like:

## Socket MCP Server
Always check the dependencies scores with the depscore tool when you add a new dependency. If the score is low, consider using an alternative library. If you decide to use a library with a low score, make sure to document the reasons for your choice. When checking dependencies, make sure to also check the imports, not just the package.json file or other dependency manifest files.

This rule transforms your AI from a simple code generator into a security-aware partner. It will now proactively check dependencies as it suggests them.

The New Secure AI Workflow: Interactive and Proactive#

The traditional security workflow—code, commit, create a PR, wait for a scan—is too slow for the age of AI. Socket MCP introduces a new, interactive paradigm.

1. Prompt: Ask your AI assistant to generate code or suggest a library for a specific task.
2. Verify (or let the AI do it for you): Instruct the AI to immediately use the depscore tool to check the score of the new dependency it suggested. With custom rules (like in .github/copilot-instructions.md), this step becomes automatic.
3. Decide: Review the security and quality scores in real-time. Is this a dependency you're comfortable adding to your project? You now have the data to make an informed choice instantly.
4. Iterate: If a package score is low, you can ask the AI for alternatives and check them just as quickly, all without leaving your chat or editor.

This doesn't replace the need for defense-in-depth security, like Socket's pull request scanning, but it shifts the first line of defense much, much earlier, preventing risky dependencies from ever touching your codebase.

Blocking Malicious Dependencies in Real-Time#

Here’s an example on how Claude with socket-mcp blocks a malicious npm package that Socket discovered recently:

Embrace AI in Coding, But Don’t Skip the Safety Checks#

AI-generated code is undeniably a transformative force in software development. It’s making us more productive and opening up new possibilities. However, this power demands new tools and new workflows. We cannot blindly trust the code or the dependencies suggested by AI tools without rigorous, real-time validation.

The path forward involves embracing these new technologies while embedding security intelligence directly into the creative process. With the Socket MCP Server, you can now have a conversation about security with your AI assistant, making informed decisions at the speed of thought. By bringing security checks to the very beginning of the development lifecycle, we can finally harness the incredible potential of AI in coding, safely and securely.

So, explore the new Socket MCP server. Be critical and curious about the code your AI assistants generate. And most importantly, empower your teams to build not just faster, but safer, in this exciting new era of software development.