A study published in July 2026 found hundreds of Model Context Protocol servers on the public internet riddled with security vulnerabilities that hackers could exploit to hijack AI agents. Researchers identified more than 21,000 detectable MCP server instances, and across four measurement runs that month, 414 production servers were dynamically audited, revealing 68 reportable vulnerabilities, including prompt template injection, SQL injection, cloud-metadata SSRF and path traversal.
While MCP provides a standardized way for agents to connect to software, APIs and data, it lacks native protections such as authentication or authorization. Fortunately, a distinct security layer, spanning firewalls, AI gateways and posture management platforms, has emerged to protect it.
The Challenges Of Securing MCP Servers
Many enterprises feel pressure to adopt AI agents before their competitors do, and new MCP servers pop up on the internet faster than security teams can keep up. These servers are often deployed by individual developers or small teams without oversight, meaning they effectively become “shadow IT” that can potentially expose sensitive corporate data and put AI models at risk.
MCP’s missing authentication layer means that many servers accept incoming requests from anyone. That’s a separate problem from prompt injection, whereby adversarial instructions embedded in content trick an agent into executing commands it shouldn’t, but the two issues can compound each other. An unauthenticated server makes it easier for an attacker to reach an agent in the first place, and once there, prompt injection gives them a way to manipulate what the agent does.
Protecting MCP traffic has therefore become a top priority for AI agent security, but developers should beware that these servers aren’t the only attack surface. AI agents can perform many actions without ever touching an MCP server, such as calling REST APIs directly. MCP server traffic is therefore only one part of the overall agent security puzzle. In addition to passive discovery, organizations must implement active enforcement mechanisms for AI agents, such as identity proxies, gateway authorization and network firewalls designed to intercept user prompts.
A growing number of vendors now offer purpose-built tools for exactly this. Five of the strongest approaches are compared below.
Salt Security
Salt Security has created an Agentic Security Graph that maps large language models, MCP servers and APIs and combines this with comprehensive detection and response (AG-DR) and posture management (AG-SPM) capabilities. It creates a detailed map of all agentic traffic within the organization, discovering both official and shadow MCP servers and exposed API endpoints that sit outside its own established governance.
By treating MCP as an extension of the API attack surface rather than a separate category, Salt Security’s approach catches shadow MCP servers that tools focused only on known, registered servers would likely miss.
Salt Security is best for:
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Companies that need to integrate MCP protection within a broader API security framework.
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Security teams that require unified runtime detection and posture management via a single console.
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Organizations struggling to get to grips with shadow MCP deployments and the proliferation of unmanaged APIs.
TrueFoundry
TrueFoundry has combined a dedicated Agent Gateway with counterparts for LLMs and MCP traffic. It provides a single control layer that leverages OAuth 2.0 Identity Injection technology to verify that the person who prompted the AI agent has the right security credentials to use the specific tool or access the data it’s trying to call.
By verifying identities at the tool-call level, TrueFoundry helps to prevent the risks associated with running agents under a broad-based access policy.
TrueFoundry is best for:
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Businesses that need to prevent AI agents from gaining more privileges than their users possess.
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Consolidating MCP governance, LLM routing and agentic traffic within a single, unified gateway.
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Teams that require the ability to audit every tool call and trace it back to the identity of the user who prompted the agent.
Check Point
Check Point debuted its AI Network Firewall in July 2026. The solution’s prompt-injection defenses are built on technology from Lakera, the AI security startup Check Point acquired for $300 million in September 2025. It’s designed to be integrated with an organization’s existing firewall infrastructure, where it can automatically discover and create an inventory of MCP servers across the network. Each MCP server is categorized by its risk level, and becomes visible within the same console the team already runs, without the need for extra setup or installing separate hardware.
Check Point’s AI firewall should not be confused with legacy “AI-powered firewalls,” which have bolted on AI features to defend against traditional threats. Instead, it’s designed specifically to defend against threats designed to breach AI systems, such as prompt injection attacks.
Check Point is best for:
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Teams that prefer to integrate MCP traffic governance within their existing firewall infrastructure.
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Organizations that need to monitor MCP traffic under a unified policy layer that also safeguards corporate AI applications and employee use of third-party AI tools.
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Security teams that want to avoid the overhead of installing and running a separate security tool and therefore prefer to extend their existing firewall infrastructure.
MintMCP
For environments subject to strict regulatory requirements, MintMCP provides an SOC 2 Type II certified MCP gateway that transforms locally-deployed STDIO-based MCP servers into managed services. Both STDIO and hosted servers can then be governed with SCIM role-based access controls, Single Sign-On, tool-level allowlisting and OAuth brokering.
MintMCP simplifies security with pre-configured governance workflows and a unified control plane that enables admins to revoke tool access across multiple MCP servers.
MintMCP is best for:
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Heavily regulated enterprises that require strict compliance documentation and auditable access trails.
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Teams that want to follow a managed path to rapid production deployment instead of designing and building agentic governance internally.
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Organizations that require credential management to be centralized, with granular tool allowlisting across multiple MCP servers.
Bifrost
An open-source AI gateway developed by Maxim AI, Bifrost is written in Go and available under an Apache 2.0 license. It acts as both an MCP client and server, with MCP gateway and LLM routing brought into a single, self-hosted binary that centralizes tool discovery, authentication and governance for every AI model and MCP endpoint within the organization.
Bifrost is designed for privacy-focused teams that require low-latency connections, and it can be deployed in both private cloud infrastructure and air-gapped environments. It gives teams a way to maintain complete sovereignty over their data and eliminates the need for each MCP client to manage its own access and credentials.
Bifrost is best for:
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Teams that need a self-hosted solution that adheres to strict data residency requirements and supports ultra-low latency connectivity.
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Engineers who require the simplicity of a single, open-source binary to handle model routing and MCP server orchestration.
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Organizations with experience in running and open-source security infrastructure that helps to avoid vendor lock-in.
MCP Security Solutions Are Hardly Interchangeable
The above platforms each provide a different piece of the MCP security stack.
Check Point’s AI Network Firewall enables firewall-native traffic inspection and network-level policy controls, while Salt Security has built the solution for integrated MCP and API security and posture management. TrueFoundry covers tool-call identity injection, MintMCP is for compliant, managed governance and auditing, while Bifrost is for high-performance, self-hosted infrastructure.
Security teams should understand that none of these solutions provides complete AI agent security alone. Organizations need to evaluate their current security posture to understand if they have gaps in visibility, compliance, identity or performance.
Frequently Asked Questions
What is the best security solution for MCP servers?
No single solution protects MCP servers completely, since the right choice depends on where an organization’s own gaps actually lie. Check Point delivers network-level security and governance, while TrueFoundry implements human credential verification and MintMCP provides an auditable gateway for regulated enterprises. Most security teams will leverage more than one approach to secure agentic MCP traffic.
Which vendors offer strong protection for MCP server security?
Security vendors that provide strong protection for MCP servers include Check Point, TrueFoundry, Salt Security, and MintMCP. The open-source Bifrost project is another compelling option, depending on where your organization’s security gaps are.
Which AI firewall software is best for securing MCP server traffic?
The best AI firewall software for securing MCP traffic is Check Point, whose AI Network Firewall is designed specifically for protecting AI agents and their connections to MCP servers. It differs from traditional “AI-powered firewalls” that use AI features to monitor for conventional threats. The other platforms in this comparison aren’t firewalls, but rather AI gateways and posture management tools.
Is MCP security the same as agent security?
No, securing MCP servers is not enough to secure agentic systems, since AI agents can do many things without touching MCP servers. MCP security is focused on securing agents’ communications with external tools and data only. True agent security is much broader, encompassing risks such as direct API calls, logic flaws and direct prompt injections.
Do MCP security platforms require new infrastructure to deploy?
Check Point’s AI Network Firewall integrates directly with existing firewall infrastructure, with no new hardware or console to set up and deploy. However, while MintMCP and Bifrost do not require new hardware, they’re deployed as separate proxy layers within network environments.