The Model Context Protocol made it remarkably easy to connect AI agents to the tools and data they need. A developer can add an MCP server to Cursor or Claude Code in minutes, and an agent can suddenly query a database, open tickets, read files, or call internal APIs. That convenience is exactly why MCP adoption spread so quickly, and why security teams now find dozens or hundreds of MCP servers running across their environments, many of which they never approved.
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At a Glance
- Dash Security: discovery, governance, and runtime enforcement across the full agentic estate, including MCP servers, skills, and plugins.
- Akto: MCP discovery, security testing, and monitoring for AI orchestration layers.
- Stacklok ToolHive: container isolation, a curated registry, and centralized policy for MCP servers.
- Docker MCP Gateway: containerized MCP servers and a signed catalog within Docker workflows.
- Snyk (Invariant Labs): scanning and guardrails for MCP servers and agent tool use.
- Kong AI Gateway: API gateway governance extended to AI and MCP traffic.
- Descope: authentication and authorization for MCP servers and agent identities.
- Obot: an open-source MCP gateway and catalog for managing server access.
- Microsoft MCP Gateway: MCP traffic management with Entra ID access control on Azure and Kubernetes.
Where MCP Risk Actually Lives
MCP security is not one problem. Risk enters at several points between an agent and the systems it touches, and each requires a different control:
- Shadow MCP servers: developers install servers locally or connect agents to remote ones without review. Security teams cannot protect what they cannot see, and unmanaged servers often carry broad permissions and stored credentials.
- Tool poisoning and malicious descriptions: the descriptions MCP servers provide to agents can contain hidden instructions. Because agents read those descriptions as guidance, a poisoned tool can steer an agent toward actions the user never requested.
- Rug pulls and supply chain changes: a server that behaved safely when it was approved can change its tools or behavior later. Without version control and continuous verification, trust granted once becomes trust granted forever.
- Over-permissioned tokens and confused deputies: MCP servers often run with credentials far broader than any single task needs. When an agent is manipulated, it can use those permissions on behalf of an attacker.
- Indirect prompt injection: content an agent retrieves through MCP tools, such as documents, emails, or web pages, can contain instructions that change the agent’s behavior mid-session.
- Data exposure in responses: sensitive information can flow from internal systems through MCP tools into agent outputs, logs, or other tools.
Some of these risks are best addressed before a server is deployed, others at the connection layer, and others only by watching what agents actually do at runtime. The platforms below cover different parts of that picture.
The 9 Top MCP Security Software Platforms for 2026
1. Dash Security
Most MCP security tools focus on one point in the chain: the gateway, the identity layer, or a pre-deployment scan. Dash Security secures MCP as part of the broader agentic estate, treating MCP servers, skills, plugins, and models as the supply chain of every AI agent. Its platform is described as the security and control plane for AI agents, covering coding agents in CLIs and IDEs, desktop and browser assistants, and autonomous agents across enterprise networks and cloud.
Protection starts with discovery. Dash continuously identifies every agent platform, MCP server, skill, and plugin in the environment, including shadow AI that security teams did not approve, and maps them into what it calls an Agentic FootPrint. It then profiles each agent, user, and session, building context on an agent’s purpose and capabilities, the user’s intent and history, and the trajectory of every session, enriched with signals from identity and endpoint tools. That context feeds AI-SPM, which assesses risk across the agentic estate and guides teams to remediate exposures, along with governance policies that control which MCP servers agents can use, what they can access, and what they are allowed to do.
Dash provides runtime protection for more than 20 coding agents and discovers more than 60 unique AI platforms across workstations, cloud, and networks. Its deployment is modular, single-sensor, and agentless across Linux, macOS, and Windows, and organizations can move from discovery to enforcement within a week. Native integrations bring existing security tools into Dash, and the Dash MCP server connects the platform into the customer’s own stack, avoiding lock-in to a single vendor ecosystem. Dash was founded by leaders from Palo Alto Networks, Akamai, and IBM and is backed by YL Ventures, Wing, and Vesey Ventures.
Key features:
- Continuous discovery of MCP servers, skills, plugins, and shadow AI
- Governance of the full agentic supply chain, including MCP servers and models
- Agent, user, and session profiling with identity and endpoint context
- AI-SPM for risk assessment and exposure remediation
- Intent-based runtime detection with intent similarity and intent drift
- In-session human approval and real-time guardrail hardening
- AI DLP for sensitive data exposure in agent sessions
- Agentless deployment across Linux, macOS, and Windows, with discovery to enforcement in a week
2. Akto
Akto approaches MCP from an API security background. Its MCP security capabilities automatically discover MCP servers and AI orchestration layers, test them against a large library of checks, and monitor activity in real time.
Testing covers issues such as insecure authentication, excessive permissions, tool poisoning, prompt injection, and other agentic AI vulnerabilities. Akto also highlights shadow MCP servers and access anomalies, helping teams find weaknesses before attackers do.
For organizations that already treat APIs as a core part of their security program, Akto extends similar discovery and testing disciplines to MCP.
Key features:
- Automatic discovery of MCP servers
- Security testing for MCP-specific vulnerabilities
- Real-time monitoring of AI orchestration layers
- Detection of shadow MCPs and access anomalies
3. Stacklok ToolHive
Stacklok’s ToolHive runs every MCP server inside its own container with minimal permissions and no local credentials, isolating servers from each other and from the host. Network access and filesystem permissions are governed by configurable profiles.
Stacklok, founded by the team behind Kubernetes, also provides a curated registry integrated with the official MCP Registry, server signing and provenance attestation, and centralized policy for coding agents. When developers use tools such as Cursor or Claude Code, MCP tool calls can route through a policy engine that applies consistent identity, access control, and audit logging.
ToolHive is open source, with enterprise capabilities for organizations that need centralized management at scale.
Key features:
- Container isolation for each MCP server
- Curated registry with signing and provenance
- Centralized policy, RBAC, and audit logging
- Kubernetes operator for scaling
4. Docker MCP Gateway
Docker brings MCP into the container workflows many developers already use. Its MCP Gateway runs MCP servers in OCI containers, and its MCP Catalog provides signed server images that teams can pull with greater confidence than arbitrary packages.
For organizations standardized on Docker, this approach adds isolation and provenance with little change to developer habits. Docker Desktop integration makes it straightforward for individual developers to run approved servers locally.
Its controls are strongest inside Docker-native environments, so organizations with diverse runtimes may combine it with broader governance tools.
Key features:
- Containerized MCP servers
- Signed MCP Catalog images
- Docker Desktop integration
- Gateway aggregation of multiple servers
5. Snyk (Invariant Labs)
Snyk expanded into MCP security through its acquisition of Invariant Labs, known for research on MCP threats such as tool poisoning and for its MCP-Scan tool. MCP-Scan inspects MCP server configurations and tool descriptions for known risks, and Invariant’s guardrails technology helps enforce rules on agent behavior.
For development organizations already using Snyk for application security, this extends a familiar workflow to the MCP servers and tools agents depend on.
Its strengths lie in scanning and research-driven detection, which complements runtime and governance controls.
Key features:
- MCP-Scan for server and tool description risks
- Research on emerging MCP attack techniques
- Guardrails for agent tool use
- Integration with Snyk developer security workflows
6. Kong AI Gateway
Kong extends its API gateway to AI traffic, including MCP. Organizations can route AI and MCP requests through the gateway to apply authentication, rate limiting, logging, and policy enforcement in the same place they already manage APIs.
That consistency appeals to platform teams that want AI connections governed by the same infrastructure as the rest of their services. Kong also supports exposing existing APIs to agents in a controlled way.
Gateway controls see the traffic that passes through them, so visibility into locally installed servers depends on routing that traffic centrally.
Key features:
- Gateway governance for AI and MCP traffic
- Authentication, rate limiting, and logging
- Policy enforcement alongside existing APIs
- Controlled exposure of APIs to agents
7. Descope
Descope focuses on identity for agents and MCP. It provides authentication and authorization capabilities that help developers secure MCP servers with standards such as OAuth, manage agent identities, and control which tools and data each agent can access on behalf of a user.
Strong identity reduces the risk of over-permissioned tokens and makes it possible to grant agents scoped, revocable access instead of broad standing credentials.
Descope addresses the identity layer specifically, so organizations typically pair it with discovery and runtime controls.
Key features:
- Authentication and authorization for MCP servers
- Agent identity management
- Scoped, revocable access for agents
- Support for OAuth-based standards
8. Obot
Obot provides an open-source MCP gateway and catalog that helps organizations manage which MCP servers are available, run them in containers, and control access to them.
Its catalog gives teams a central place to publish approved servers, while the gateway enforces how agents connect. Open-source availability appeals to organizations that prefer to self-host and customize their MCP infrastructure.
As with other gateway approaches, it is most effective when MCP traffic is routed through it consistently.
Key features:
- Open-source MCP gateway
- Catalog of approved MCP servers
- Container-based server isolation
- Self-hosted deployment
9. Microsoft MCP Gateway
Microsoft’s MCP Gateway is designed for Azure and Kubernetes environments, managing MCP traffic and applying access control through Entra ID. It suits organizations whose identity and infrastructure already run on Microsoft platforms.
Using existing Entra ID roles and policies helps teams extend familiar governance to MCP without introducing a separate identity system.
Its value is greatest for Azure-centric deployments, while mixed environments may need additional tools for discovery and runtime visibility.
Key features:
- MCP traffic management on Azure and Kubernetes
- Entra ID role-based access control
- AKS-native deployment
- Integration with Microsoft identity governance
MCP Security Layers: Discovery, Gateway, Identity, Testing, and Runtime
MCP security works best as a set of layers, each covering a different stage of the risk.
Discovery
Every program starts with knowing which MCP servers, skills, and plugins exist and which agents use them. Discovery must include servers developers install locally on workstations, not just those routed through central infrastructure.
Gateway and isolation
Gateways and containerized runtimes control how agents connect to servers, enforce policy on tool calls, and isolate servers from each other. They are effective for traffic that passes through them.
Identity
Scoped, revocable credentials limit what an agent can do with a tool. Identity controls reduce the damage when an agent is manipulated or a server is compromised.
Testing and scanning
Pre-deployment testing and configuration scanning catch poisoned descriptions, weak authentication, and excessive permissions before servers reach production.
Runtime enforcement
Some risks only appear while an agent is working: indirect prompt injection, intent drift, or a trusted server that changes behavior. Runtime monitoring and enforcement catch what earlier layers cannot predict.
FAQ
What is tool poisoning in MCP?
Tool poisoning occurs when an MCP server’s tool descriptions contain hidden or misleading instructions. Because agents read these descriptions to decide how to use tools, a poisoned description can trick an agent into taking harmful actions, such as sending sensitive data to an attacker, without the user realizing it.
What is a shadow MCP server?
A shadow MCP server is one that runs in an organization without security review or approval, often installed locally by a developer. These servers may carry broad permissions or stored credentials and are invisible to tools that only monitor centrally managed traffic. Platforms such as Dash Security discover them across workstations and cloud environments.
Is an MCP gateway enough to secure MCP?
A gateway is a valuable control for traffic routed through it, but it does not see locally installed servers and cannot always detect risks that emerge during a session. Most organizations combine gateways with discovery, identity controls, testing, and runtime monitoring to cover the full range of MCP risks.
How does intent-based detection help secure AI agents?
Intent-based detection evaluates what an agent is trying to achieve rather than only the commands it runs. It can recognize when a session drifts from its original purpose, even if each individual action looks legitimate. Dash Security uses intent similarity and intent drift to identify these behaviors in real time.
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