TL;DR
The Model Context Protocol (MCP), pioneered by Anthropic, transforms AI assistants like Claude and Cursor from conversational tools into proactive agents by connecting them seamlessly to external resources like GitHub, Salesforce, and databases. Although MCP enables building powerful, personalized AI agents in minutes through thousands of available open-source MCP servers, it also introduces significant risks - such as unauthorized access, data leaks, supply chain threats, and accidental destructive actions. Aim Security addresses these risks with comprehensive, real-time protections, allowing organizations to safely leverage the transformative potential of MCP at scale.
What is MCP?
The Model Context Protocol (MCP) is an open specification introduced by Anthropic for connecting AI-powered tools (clients) with external resources and services (servers). An MCP server acts as a bridge, exposing specific resources or actions (tools) to clients. For example, a server might provide access to GitHub repositories, Figma designs, Salesforce data, database queries, browser automation, or local code compilation.
Each MCP server offers clearly defined "actions" or "tools" to the client applications. For instance, a database MCP server might expose tools for executing SQL queries and inspecting schemas, while a browser MCP might provide actions like "open new tab" or "click button."
Implementing an MCP server is straightforward - often achievable in just a few lines of Python code using readily available open-source libraries. MCP servers can operate remotely (cloud-based) or run locally, offering flexibility and ease of adoption.
Who Is Using MCP?
Today, MCPs are widely adopted by developers, data scientists, and an increasing range of technical and non-technical power users. In AI-first companies, it's not just early adopters who are embracing MCP - there's a broader push to stay aligned with the rapidly evolving AI landscape. Organizations are prioritizing MCP adoption from the top down, with leadership encouraging teams to integrate new AI capabilities into their workflows. Tools like Cursor and Claude Code, as well as a wide range of open-source community projects and third-party vendors following the MCP specification, have made it exceptionally easy to extend local AI agents with advanced, plug-and-play functionality.
MCP is evolving beyond individual employee productivity use cases toward centralized, business-critical AI systems. We will explore the production use cases in a future post.
Inherent Risks of Using MCP
Identity Misuse
A key risk with MCPs is identity misuse - when AI agents act under incorrect or inappropriate identities. This often occurs due to reliance on static, shared identities or long-lived keys instead of proper per-user authentication methods like OAuth, commonly skipped in local MCP setups. Without robust per-user credentials, tightly scoped permissions, and continuous runtime identity validation, agents may perform actions with excessive privileges, incorrect user context, unauthorized data access, unintended modifications, or even destructive operations.
Unintended Destructive Operations
Another significant risk is the accidental execution of harmful actions. Since MCP enables agents to perform powerful tasks, such as running database commands or interacting with production environments, a misinterpreted instruction or simple oversight can lead to catastrophic outcomes, like data loss or system outages. Theoretically, every MCP-invoked action should first be approved by a human. However, as user adoption grows and MCP usage scales, most major MCP clients offer users the ability to approve an action once for an entire project. While convenient, this effectively removes continuous human approval as a safety layer, increasing the likelihood of unintended or risky operations.
Malicious MCP Servers and Supply Chain Risks
Since MCP servers are often community-maintained and open-source, malicious MCP servers or compromised supply chains pose substantial threats. Untrusted MCP servers could intercept and leak sensitive credentials, execute malicious code directly on host machines, or misuse granted permissions, significantly increasing the risk of security breaches.
Indirect Context Injection and Data Leakage
Interestingly, even legitimate and well-configured MCP servers can lead to unintended negative outcomes due to the dynamic interactions between them. For example, indirect context injection might occur when sensitive information unintentionally flows from one legitimate MCP server to another, creating unforeseen privacy and security risks. For instance, if one MCP server accesses confidential internal data and another has the capability for external web searches, an agent might unintentionally expose private data to external services, creating significant security and privacy vulnerabilities. Using prompt injection hidden in a search advertisement, an attacker could manipulate the agent into querying sensitive information from internal data sources and uploading it to an attacker-controlled website.
How Aim Enables Secure Adoption of MCP
Aim Security addresses these security challenges, empowering organizations to leverage MCP safely through several key capabilities:
Shadow MCP Discovery
Aim identifies all active MCP servers within an organization's environment, providing immediate visibility into both official and unofficial (shadow) MCP implementations. This ensures security teams have continuous oversight of all MCP-related activities.
Comprehensive MCP Auditing
Aim records detailed logs of every interaction between AI agents and MCP servers, capturing tool invocations, inputs, outputs, and user approvals. This audit trail allows for thorough forensic analysis and compliance verification, enhancing transparency and accountability.
Runtime Security Controls
Aim provides real-time enforcement of security policies, embedding context-aware guardrails directly into MCP interactions. These guardrails validate the intent and parameters of each action, manage access permissions at a granular level, and isolate sensitive resources to prevent unauthorized or risky operations.
Through these measures, Aim Security enables organizations to confidently harness the full potential of MCP-connected AI agents, providing essential protections against identity misuse, accidental harm, data leakage, and malicious activity.
