MCP Made Your Tools Discoverable. AppSentinels Makes Them Governable
Map every MCP server, tool, agent, and API dependency, then enforce ownership, intent, and business logic on every tool call in real time.
Map every MCP server, tool, agent, and API dependency, then enforce ownership, intent, and business logic on every tool call in real time.
Gain visibility into what’s happening inside MCP tool interactions. Expose and govern every MCP server, tool, agent, and action to prevent unauthorized access, misuse, and workflow manipulation.
A malicious or compromised MCP server ships tool descriptions crafted to steer the agent into actions the user never requested.
A tool description, schema, or behavior changes after the MCP server was approved, turning a sanctioned tool into an attack vector.
A tool returns output containing instructions the agent treats as authoritative, pivoting the workflow toward exfiltration or unauthorized actions.
An agent chains its way to a tool it should never have been able to reach, often through a sanctioned tool that exposes more than intended.
The MCP server’s own privileges exceed the requesting user’s, and a tool call ends up accessing objects the user couldn’t access directly.
Developers wire up new MCP servers without registering them, creating ungoverned execution paths into production data.
AppSentinels applies the same three pillars that secure your AI agents and APIs to the MCP layer that now sits on top of them.
Inventory every MCP server, tool, agent, and downstream resource the moment it appears, including shadow servers and unregistered tools.
Continuously probe the MCP surface with adversarial agents tuned to the attacks that matter at this layer.
Enforce ownership, intent, and sequence on every MCP tool call; inline through the AppSentinels MCP Proxy or out-of-band via sensors.
A purpose-built enforcement point for the MCP layer. Deploy in-line to enforce every tool call, or out-of-band to observe and learn before turning enforcement on. Either way, the BLG drives the decision.
Passive observation via sensors. Full visibility, zero added latency, used for greenfield discovery and progressive rollout.
Works with the agent frameworks your teams use: LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, and managed runtimes like AWS Bedrock AgentCore.
Take control of shadow AI extensions, find logic vulnerabilities automatically, and run real-time security enforcement across your whole workflow ecosystem.
Model Context Protocol (MCP) security focuses on protecting the open standard that connects AI models to data sources and operational tools. Since MCP allows autonomous agents to execute actions and query systems directly, dedicated security is needed to prevent agents from being manipulated into bypassing traditional application controls.
Attackers exploit MCP environments by manipulating the AI agent’s prompt context or poisoning its data sources. This forces the agent to execute unauthorized tools, run malicious commands, or exfiltrate sensitive backend corporate data under the guise of a legitimate user request.
No. Traditional tools inspect static traffic parameters but lack the linguistic and contextual awareness required to understand AI agent behavior. They cannot differentiate between an authorized agent function call and an exploited, multi-step business logic attack.
An MCP Gateway acts as an inline proxy between AI models, agents, and downstream systems. It intercepts traffic in real-time to analyze user intent, enforce least-privilege resource access, and block unauthorized tool executions before they can affect production data.
MCP security is tested using automated red teaming and semantic fuzzing. This process simulates real-world attack workflows, such as prompt injection and tool poisoning, to find logic gaps and over-permissive agent permissions before the application goes live.