Your AI Strategy’s Biggest Blind Spot: MCP Vulnerability Scanning

As AI adoption accelerates, a powerful new standard—Model Context Protocol (MCP)—is emerging as the backbone of modern AI agents. MCP connects AI assistants to tools and data sources, driving productivity. But in the race to innovate, security is being left behind. Familiar web vulnerabilities are resurfacing in this next-gen AI infrastructure.

The rapid spread of MCP servers has created a vast new attack surface, largely invisible to traditional security tools. This is not just a theoretical threat. A recent Tenable discovery of a critical RCE flaw in Anthropic’s MCP Inspector (CVE-2025-49596) shows how even developer tools can become serious risks.

For CISOs and security teams, this is a wake-up call: securing MCP isn’t optional—it’s essential to defending your organization’s most critical AI assets.

Understanding the MCP Landscape: More Than Just Another API

At first glance, an MCP server might look like any other API. It communicates over HTTP and uses structured messages, typically JSON-RPC, to function. As described in the protocol specification, a standard communication flow involves an initialization phase, an operation phase, and a closing phase.

However, MCP servers are fundamentally different. They are designed to manage, orchestrate, and optimize machine learning workflows, acting as a broker between an AI’s cognitive core and the tools it needs to perform tasks. This means they handle incredibly sensitive assets:

• Proprietary machine learning models.
• Connections to internal databases and data pipelines.
• Access to vast computational resources.
• Streams of potentially sensitive training and operational data.

This unique role creates specialized security challenges, where old vulnerabilities find new and devastating life.

The Ghosts of Insecurity Past: Classic Web Flaws Haunting Modern AI

The core danger of the current MCP ecosystem is that while developers focus on the complex LLM integrations, they often overlook basic web security hygiene. This allows well-known attack vectors to resurface with dangerous consequences.

1. Authentication & Authorization Flaws: The Open Front Door

A common mistake is assuming an “internal” MCP server is safe from remote threats. This is a flawed assumption. As demonstrated by Tenable’s research into DNS rebinding, attackers can easily bypass network perimeters. A typical attack looks like this:

1. A developer visits a malicious website.
2. The site’s JavaScript establishes a connection back to mcp.evil.com.
3. The attacker updates the DNS record for mcp.evil.com to point to a local IP address like 127.0.0.1 (localhost).
4. The browser, respecting the original connection’s origin, now has a direct, trusted channel to the MCP server running on the developer’s own machine, bypassing the Same-Origin Policy.

This vector becomes critical when paired with authentication flaws, like the CSRF vulnerability in Anthropic’s MCP Inspector (CVE-2025-49596). Because the Inspector lacked proper authentication, the attacker could use this DNS rebinding or a simple cross-site request to send unauthenticated commands and achieve RCE.

2. Tool Vulnerabilities: When “Fetch” Becomes Fetch-and-Exploit

Within the MCP framework, a “tool” is simply a function an LLM can call. A cognitive bias often leads developers to assume the LLM will use these tools predictably. This is a dangerous assumption.

Consider this simple tool that could be exposed by an MCP server:

@mcp.tool()
def fetch_url(url: str) -> str:
    """Fetch content from a URL."""
    try:
        response = requests.get(url, verify=False)
        response.raise_for_status()
        return response.text
    except requests.RequestException as e:
        return f"Error fetching URL: {e}"

An attacker doesn’t need to trick an LLM to abuse this. They can send a direct JSON-RPC message to the MCP server:

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "fetch_url",
    "arguments": {
      "url": "https://internal-sensitive-service/"
    }
  },
  "id": 1
}

Without proper input sanitization and network controls, this simple fetch_url tool becomes a wide-open vector for a full-read Server-Side Request Forgery (SSRF) attack, allowing attackers to probe and exfiltrate data from your internal network. This can be easily extrapolated to nearly all other web vulnerabilities, from SQL injection to RCE.

3. Sensitive Information Exposure

In the rush to build, developers sometimes hardcode secrets or make sensitive information readily available. A tempting shortcut is to expose data needed for one tool as a generic resource, which could then be used by another tool for authentication.

For example, an MCP server might expose a resource like this:

@mcp.resource("data://api_key")
def get_api_key() -> str:
    """Internal Service API Key"""
    return "API_KEY=internalAPIkey1234"

An attacker who discovers this resource can immediately leverage the key, bypassing intended security flows.

The Need for a New Lens: MCP Vulnerability Scanning

These examples make one thing clear: traditional vulnerability scanners are not enough. They may identify the open port of an MCP server, but they lack the context to understand its unique risks. They don’t know:

• What tools are being exposed?
• How can those tools be manipulated?
• Are there cross-origin vulnerabilities like CSRF or DNS rebinding?
• Is the server leaking sensitive data through its resource definitions?

This is where specialized MCP vulnerability scanning becomes a critical new category of security tooling.

Key Evaluation Criteria for MCP Security Scanners:

When assessing potential scanning tools, consider these critical factors:

• MCP Protocol Fluency: The scanner must understand the JSON-RPC message structure, including how to discover and interact with exposed tools and resources.
• AI-Specific Threat Models: It needs native support for detecting ML-specific threats like Tool Poisoning and model integrity attacks.
• Deep API Security Testing: Since MCP is API-driven, the scanner must have advanced capabilities for detecting vulnerabilities like SSRF, RCE, and SQL injection within the context of MCP tools.
• Comprehensive Coverage: The tool must scan across all layers of the MCP infrastructure, from the server configuration to the individual tools it exposes.

The Strategic Advantage: Benefits and Solutions

Adopting a specialized MCP scanning strategy provides profound benefits beyond just finding bugs. It offers a strategic advantage in the AI-driven landscape.

Key Benefits of Specialized MCP Scanning:

• Context-Aware Risk Prioritization: An MCP-aware scanner can differentiate between a low-risk data exposure and a critical RCE vulnerability in a production-facing AI agent, allowing you to focus on what matters most.
• Enabling Secure Developer Velocity: By integrating specialized MCP scanning into the CI/CD pipeline, developers get fast, relevant feedback, allowing them to innovate securely without being slowed down by security gates.
• Automated Governance and Compliance: As AI regulations mature, MCP scanners can automate the auditing of AI infrastructure against these standards, providing evidence-backed assurance.
• Visibility into “Shadow AI”: Specialized scanners excel at discovering unmanaged MCP servers deployed by development teams, bringing these “shadow AI” instances out of the dark and into your security program.

Available Solutions in the Market:

The market for MCP security is rapidly evolving to meet this new challenge.

• Dedicated MCP Scanners: At the forefront are new, purpose-built tools designed from the ground up. Scanners like mcpscan.ai (illustrative example) represent this emerging category, offering deep analysis of ML-specific threat models.
• Enhancements to Existing Platforms: Established leaders are expanding their platforms. Tenable, for example, has added new, MCP-specific plugins to its Web App Scanning capabilities. These plugins are designed to first identify MCP servers and then analyze them for a broad range of deep API vulnerabilities within the MCP context.

Conclusion: Don’t Let Your AI Innovation Become Your Biggest Breach

The massive adoption of the Model Context Protocol is set to redefine how businesses build and deploy AI. While MCP promises unprecedented workflow and intelligence opportunities, it must be implemented with care. The rush to innovate cannot come at the cost of security fundamentals.

Developers need to follow security best practices and safeguard against both traditional web threats and novel vulnerabilities emerging from AI technologies. For security leaders, continuously monitoring and assessing MCP server security is a mandatory requirement to avoid putting the organization at risk. The future of AI is thrilling, but it must be built on a foundation of security.

To further enhance your cloud security and implement Zero Trust, contact me on LinkedIn Profile or [email protected].

MCP Security FAQ:

• What is a Machine Control Plane (MCP) server? An MCP server is a specialized API gateway that acts as the connective tissue for AI ecosystems, allowing AI agents to interact with a wide range of tools and data sources to perform tasks.
• Why are traditional vulnerability scanners insufficient for MCP security? Traditional scanners often lack the context to understand the unique architecture of MCP. They may not recognize MCP-specific threats like Tool Poisoning or know how to test the individual “tools” exposed by an MCP server for classic vulnerabilities like SSRF or RCE.
• What is a “Tool Poisoning” attack? Tool Poisoning is a sophisticated attack that targets MCP environments by manipulating the trusted machine learning tools and libraries an AI agent uses. An attacker might introduce malicious code or compromise model integrity, leading to a long-term, undetected compromise.
• What was the core issue in the Anthropic MCP Inspector vulnerability (CVE-2025-49596)? The core issue was a lack of authentication, which, when combined with insecure default server configurations and a known browser flaw, allowed an attacker to use a malicious website to send commands to the MCP tool running on a developer’s local machine and achieve Remote Code Execution.
• What is the most important first step to securing my organization’s MCP infrastructure? The first step is visibility. Conduct a comprehensive security audit using specialized MCP vulnerability scanning tools to discover all MCP servers in your environment (including “shadow AI”), identify their vulnerabilities, and create a targeted plan for remediation.