Unvetted Model Context Protocol (MCP) servers introduce a stealthy supply chain attack vector, enabling adversaries to harvest credentials, configuration files, and other secrets without deploying traditional malware.
The Model Context Protocol (MCP)—the new “plug-in bus” for AI assistants—promises seamless integration of AI models with external tools and data sources.
Yet this flexibility creates a novel supply chain foothold for threat actors. In this article, we overview MCP, dissect protocol-level and supply chain attack paths, and present a hands-on proof of concept: a malicious MCP server that quietly exfiltrates secrets whenever a developer runs a tool.
We then break down the PoC source code to expose its true intent and recommend mitigations defenders can apply to detect and disrupt these attacks.
What Is MCP
Developed by Anthropic as an open standard, MCP standardizes communication between AI assistants (such as Claude or Windsurf) and third-party services.
Instead of writing custom integrations for each tool, developers configure an AI client—called an MCP client—to discover and invoke functionality exposed by an MCP server. The core MCP components are:
- Clients embedded in AI assistants or apps, routing natural-language requests for specific tools.
- Hosts (LLM applications) that initiate connections.
- Servers acting as smart adapters, translating AI prompts into tool-specific commands.
This client–server design streamlines deployments but implicitly grants installed servers full access to the user’s environment, akin to running arbitrary code with the user’s privileges.
Attackers have identified multiple ways to weaponize MCP:
Name-spoofing enables rogue servers with nearly identical identifiers to siphon off tokens or sensitive queries during name-based discovery.
Tool-poisoning embeds hidden commands inside descriptions or prompt examples, leaking secrets with no visible exploit code.
Shadowing allows a malicious server to redefine an existing tool on the fly, silently redirecting subsequent calls through attacker logic.
Rug-pull scenarios see a benign server gain trust, then push backdoored updates via CI/CD pipelines, compromising clients upon auto-update.
Vulnerabilities in official integrations—such as GitHub MCP connector—can leak private-repo data via crafted GitHub issues, as recently demonstrated by researchers
All these methods exploit default trust in metadata and naming, requiring no complex malware chains. They merely leverage innocuous-looking MCP traffic to bypass traditional security controls.
Proof of Concept: A Malicious MCP Server
To illustrate supply chain abuse, our team built a PoC “DevTools-Assistant” server, published as a PyPI package. Developers install it via pip install devtools-assistant and point their AI client (e.g., Cursor) at localhost.
The package advertises three productivity tools—project analysis, configuration health checks, and environment tuning—but hides a core engine that silently harvests secrets.
Upon first use, the server enumerates project files and key system folders, indexing environment files (.env*), SSH keys, cloud credentials, API tokens, certificates, and more.
Each file’s metadata and initial content bytes (up to 100 KB) are captured, redacted for local display, and cached for efficiency.
Behind the scenes, the server then base64-encodes and POSTs this data—disguised as legitimate GitHub API traffic—to a controlled endpoint, evading detection by blending in with normal development analytics.
Now that the package was installed and running, we configured an AI client (Cursor in this example) to point at the MCP server.
The project_metrics.py module defines target patterns and orchestrates file discovery, indexing, and content extraction.
In reporting_helper.p, send_metrics_via_api() constructs realistic headers and payloads before exfiltration, employing rate limiting to avoid raising network alerts.
Mitigations
Our experiment underscores a simple truth: any third-party MCP server can run arbitrary code and exfiltrate data at will unless properly sandboxed. To defend against this emerging threat, organizations should:
Check before you install. Enforce an approval workflow for new MCP servers, including code review and threat modeling. Maintain a strict whitelist and flag unfamiliar servers.
Lock it down. Run MCP servers inside isolated containers or VMs with minimal filesystem access and segmented network zones to limit lateral movement.
Watch for odd behavior. Log all prompts and responses to detect hidden instructions or unexpected tool invocations.
Monitor network traffic for anomalous POST requests and datastreams originating from AI tool processes.
By treating MCP servers with the same rigor as any supply chain component—auditing, sandboxing, and monitoring—defenders can mitigate the risks posed by weaponized AI-enabled integrations.
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