Best Tools for Securing MCP and LLM Integrations

Shadow IT used to mean employees spinning up unsanctioned software-as-a-service (SaaS) apps that stored company data without approval. Today, shadow MCP and unsanctioned LLM integrations represent the next evolution, and they're more dangerous. Model context protocol (MCP) servers don't merely store data; they act on it, executing code, calling APIs, and accessing internal tools on behalf of AI agents that developers connect with a config file. Meanwhile, large language models (LLMs) process sensitive prompts and generate outputs that can leak proprietary information.

The scale of shadow MCP is already widespread, with one in 15 MCP servers being a lookalike designed to impersonate legitimate services. The IBM Cost of a Data Breach Report 2025 found that breaches involving shadow AI added $670,000 to average breach costs. MCP represents the most privileged variant of shadow AI infrastructure because these servers can trigger deployments and modify production systems, while unmonitored LLM integrations can expose customer data and intellectual property without your security team ever knowing they exist.

This guide breaks down the tools available to secure MCP and LLM integrations, organized by whether you need discovery and exposure management or runtime defense.

What is MCP and LLM security?

Introduced by Anthropic, MCP is an open standard that acts as a universal interface between an AI agent and internal data sources. Instead of building custom integrations for each service, developers deploy MCP servers to provide models with direct, programmatic access to systems such as databases and code repositories.

Because this architecture bridges natural-language reasoning with privileged code execution, securing it requires separating your strategy into two distinct operational layers:

Exposure and discovery handle the foundational visibility questions. They answer which MCP servers and AI integrations are exposed or referenced externally in connection with your organization, whether they are sanctioned or impersonated, and what sensitive endpoints they might be leaking.

Runtime defense steps in after visibility is established. This layer involves prompt firewalls, output filtering, and agent guardrails that intercept malicious inputs before they reach a model.

The current market gap is significant: most security teams invest disproportionately in runtime prompt firewalls without first mapping their exposure. In effect, they're defending a perimeter they haven't yet defined.

Navigating these two layers requires a specific set of evaluation criteria. Before looking at vendors, consider this checklist of technical capabilities that define MCP and LLM security best practices for securing both discovery and runtime.

Key features to look for

Before evaluating vendors, define what capabilities matter most for your environment. MCP and LLM security spans everything from network-level discovery to real-time prompt filtering. Use the checklist below to map vendor capabilities against your gaps.

• MCP registry and inventory visibility. Can the tool identify which MCP servers your organization is using, including unsanctioned ones deployed by developers? Without a complete inventory, runtime controls protect only the servers you already know about.
• Lookalike and typosquat detection. Attackers register MCP servers with names designed to mimic legitimate services.
• Runtime defense against prompt injection and jailbreaks. This uses pattern matching, classifiers, and policy enforcement at the LLM boundary to stop adversarial inputs from manipulating model behavior.
• Tool and agent permission scoping. Least-privilege controls on what an MCP-connected LLM can do, including file access, API calls, and code execution. A compromised MCP server can escalate privileges across your entire environment.
• Data leak and personally identifiable information (PII) filtering. Scanning both prompts and model responses for sensitive data before it leaves the organization. This capability is especially relevant for MCP integrations that connect LLMs to internal databases, code repositories, and customer records.
• Continuous monitoring with low false-positive rates. AI security tooling generates high volumes of alerts, and noise is a real operational problem. ACM research on alert fatigue found that over half of security operations center (SOC) teams feel overwhelmed by alert volume, with analysts spending more than 25% of their time on signals that turn out to be benign. Look for platforms with automated triage that surface actionable findings rather than forwarding raw signals.
• Audit logging and compliance evidence. Documentation that maps to frameworks like the OWASP LLM Top 10, the National Institute of Standards and Technology (NIST) AI Risk Management Framework, and EU AI Act requirements. Regulators are moving faster than most security teams expect, and retroactive compliance costs more than building it in from the start.

There's no solution that includes all the listed features, so the market remains structurally split. Below, we've organized the leading tools by primary focus — starting with discovery — to help you match tools to your gaps.

Best tools for securing MCP and LLM integrations

UpGuard Breach Risk

Breach Risk is the discovery-and-exposure layer for MCP and AI security. Where most tools on this list focus on what happens after a model receives a prompt, Breach Risk focuses on what's connected to your environment in the first place. That distinction matters because runtime guardrails can only protect infrastructure that the security team has identified and onboarded.

Core capabilities include:

• Continuous Attack Surface Discovery. Agentless daily mapping of internet-facing assets. More than 330 security checks are run per asset, with scans and rescans completing in under five minutes. No agents to install means the platform starts producing results on day one.
• AI Threat Analyst. In its first three months of deployment across the existing customer base, AI Threat Analyst processed 1.5 million signals, dismissed approximately 60% as non-threatening, and saved those customers roughly 215,000 analyst-hours in combined effort.
• Brand Protection. Detects lookalike and typosquatted domains, including those impersonating your AI/MCP services, using the same detection methods used in UpGuard's MCP-lookalike research. This catches brand-impersonation attempts that traditional MCP security tools don't monitor.
• Threat Monitoring (shadow AI / MCP). Monitors emerging MCP registries for published server definitions that reference your organization. Plus AI app-builder platforms like v0.dev, Lovable, and Replit, giving early warning of shadow AI infrastructure before it becomes live, undocumented risk.

"I can look at a critical alert, see that it's exposed GitHub credentials from a classroom lab exercise, and move on within seconds because the context is right there." — Tom Grundig, Boston University

Pros: The only listed vendor combining external attack surface management, dark web exposure monitoring, and brand-impersonation detection in one platform. Strong original research on the MCP threat landscape. No agents to install or maintain.

Cons: Does not provide in-line, runtime prompt firewalls.

Lakera

Lakera excels at intercepting adversarial inputs at the LLM boundary. It deploys as a low-friction API layer between user input and model inference, making it a favorite for engineering teams who need to block jailbreaks and prompt injections in production applications in real time. It doesn't, however, map your external attack surface or inventory shadow MCP servers.

Prompt Security

Prompt Security focuses on user-level shadow AI and data loss prevention (DLP). It operates as a centralized gateway to monitor employee interactions with public AI tools like ChatGPT or Copilot, tracking what sensitive data is being shared. Its coverage of infrastructure-level MCP deployments is limited compared to dedicated discovery tools.

Lasso Security

Lasso Security provides continuous governance from development through production. It features a dedicated MCP Secure Gateway that enforces strict access controls for agent tool invocation. It covers the full application lifecycle, from development through production, though it currently has a smaller enterprise deployment footprint than legacy platforms.

Robust Intelligence (Cisco) and Protect AI (Palo Alto)

These heavyweight, acquired platforms secure the integrity of the underlying model. Protect AI scans machine learning notebooks for backdoor code and poisoned weights before production, while Robust Intelligence automates adversarial red-teaming. Both are enterprise-grade options tailored for teams training or fine-tuning custom models, rather than monitoring third-party agent protocol handling.

Wiz (AI-SPM)

Wiz extends its cloud security architecture into AI infrastructure. It discovers models, pipelines, and endpoints natively hosted within Amazon Web Services (AWS), Azure, and GCP, mapping relationships to surface misconfigurations. It is a natural extension for existing Wiz customers, but it focuses on cloud workloads rather than local developer toolchains or public MCP registries.

How to evaluate MCP and LLM security tools

Choosing the right tool comes down to identifying your immediate operational bottleneck:

• If your priority is governance and risk reduction, then start with discovery and exposure management. You cannot secure an architecture you haven't inventoried, and runtime firewalls cannot protect shadow MCP servers sitting completely outside your perimeter.
• If you are actively deploying customer-facing AI apps, prioritize runtime defense. You'll need immediate, inline guardrails to block prompt injections and data exfiltration vectors at the user boundary.

Regardless of your starting point, verify that a prospective vendor can explicitly surface shadow MCP infrastructure tied to your organization —including externally exposed servers, lookalikes, and registry definitions—rather than checking a box for generic "AI compliance".

The coming convergence

MCP security, AI posture management, and runtime defense are distinct categories of tooling today. Over the next 12 to 24 months, expect these categories to converge as the market matures and enterprise buyers demand consolidated visibility across their entire AI infrastructure.

The current gap between rapid AI adoption and security maturity mirrors the early days of cloud computing. The organizations that establish governance now—by prioritizing visibility first and layering runtime controls second—will avoid the costly rip-and-replace cycles that fragmented point solutions tend to create.

Your attack surface is expanding whether you see it or not. The question is whether you'll map it before an attacker does. Start a free trial of UpGuard Breach Risk today.

Frequently asked questions

What are the biggest security risks with MCP?

The primary risks include prompt injection attacks that manipulate AI agent behavior, tool poisoning, in which compromised MCP servers feed malicious context to models, shadow servers deployed without security review, and credential exposure through unauthenticated MCP endpoints.

What tools are available for MCP and LLM security?

The category spans three types of tools: discovery and exposure platforms like UpGuard Breach Risk that inventory MCP servers and detect lookalikes; runtime guardrails like Lakera and Prompt Security that filter prompts and block adversarial inputs; and cloud AI-SPM platforms like Wiz that find AI assets running in cloud environments.

How does MCP security differ from traditional API security?

AI agents initiate MCP connections at runtime rather than relying on developer-predefined configurations, making the attack surface dynamic and often invisible to traditional API gateways. Traditional security tools monitor known endpoints with static configurations, while MCP security must account for servers that appear and disappear as AI agents operate, often added to a development environment with a single configuration change.

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