Is Your AI Secure? Uncovering Risks in the Model Context Protocol (MCP)

Read full article from CyberArk here: https://www.cyberark.com/resources/agentic-ai-security/is-your-ai-safe-threat-analysis-of-mcp-model-context-protocol/?utm_source=nhimg

The Model Context Protocol (MCP) is quickly becoming the backbone of how modern Large Language Models (LLMs) connect to external systems. Developed by Anthropic and supported by industry leaders like OpenAI and Google, MCP promises a standardized, secure way for AI applications to access tools, files, and resources. But as this protocol spreads across AI ecosystems—from Claude Desktop to local automation environments—security experts are beginning to ask a vital question: Is your AI really safe?

This article provides a comprehensive security and threat model analysis of MCP, examining its core structure, emerging attack surfaces, and potential for exploitation. While MCP enhances AI capabilities by enabling dynamic access to tools, resources, and prompts, it also introduces a new attack surface for adversaries to exploit. Our research outlines how composability, sampling, and unverified tool integration could lead to serious compromise scenarios—including data exfiltration, command injection, privilege escalation, and account takeover.

Understanding MCP: The New Interface Layer for LLMs

At its core, MCP standardizes how applications provide context to LLMs—similar to a USB-C port for AI systems. It enables hosts like Claude Desktop or Cursor to connect to MCP servers that expose capabilities in the form of tools, resources, and prompts.

• Tools allow the LLM to perform actions (e.g., file read, code review, search queries).
• Resources make data (text or binary) available to the model.
• Prompts define workflows or pre-formatted templates for consistent task execution.

MCP relies on JSON-RPC over HTTP/SSE, making it fast, lightweight, and extensible. However, its openness and dynamic connectivity—while beneficial for developer productivity—also create blind spots for identity management, access control, and trust validation.

The sampling and composability features, in particular, extend LLM capabilities but also increase the potential for cross-server attacks and malicious data injection.

The Security Challenge: Functionality vs. Trust

As with many emerging AI protocols, MCP prioritizes usability and rapid integration. Unfortunately, that agility comes at a cost: security assumptions. The ecosystem’s flexibility allows any developer to deploy an MCP server that other users—or their AI agents—can connect to.

This decentralization introduces several risks:

• Lack of code signing or source validation for MCP servers
• No enforced identity verification between clients and servers
• Inconsistent permission and consent models across implementations
• LLM-driven actions that bypass user scrutiny

In essence, MCP’s power is also its biggest weakness—its openness can be weaponized by attackers who exploit trust boundaries between AI agents, users, and connected systems.

Threat Analysis: 13 High-Impact Vulnerabilities

The article identifies and dissects 13 distinct threat vectors, each mapped with preconditions and real-world exploit scenarios. The most critical include:

• Composability Chaining – benign-looking servers proxy hidden malicious calls through secondary endpoints, injecting commands or leaking environment variables.
• Tool Poisoning – attackers host fake tools that appear legitimate but silently exfiltrate sensitive files or tokens.
• Sampling Abuse – malicious sampling requests embed hidden prompts to extract credentials or secrets.
• Command Injection & Path Traversal – unvalidated parameters allow arbitrary command execution or unauthorized file access.
• Rug Pull Attacks – servers initially offer safe tools, then swap them for malicious ones post-installation.
• Lookalike Domain Hijacking – spoofed MCP manifests trick LLMs into connecting to rogue servers.
• Hidden Jailbreaks – oversized prompts carry encoded payloads that trigger data leaks.
• User Consent Fatigue – repeated permission prompts condition users to blindly approve harmful actions.
• Admin Bypass & Token Theft – poor authentication allows privilege escalation or credential theft via exposed config files.

Each attack pattern demonstrates how unverified tool connections and permissive trust models can escalate into system-wide compromise.

Identity and Access Implications

Beyond the technical exploits, these findings highlight a deeper issue: identity management for AI systems. MCP currently lacks a native concept of workload identity—a cornerstone of modern security frameworks. Without clear identity boundaries, AI agents can operate beyond intended privilege levels, impersonate trusted servers, or chain multiple contexts without verification.

This gap directly parallels the broader Non-Human Identity (NHI) security challenge: ensuring every machine, agent, and protocol has a traceable, revocable, and verifiable identity. Until MCP integrates such controls, every connection remains a potential breach vector.

Mitigation and Defense Strategies

To reduce MCP exposure, organizations and developers should:

• Use only official or verified MCP servers listed on trusted repositories.
• Conduct manual and automated code reviews before local installation.
• Sandboxed test any third-party server before connecting to production AI environments.
• Employ Zero Trust principles: authenticate every connection, monitor every action, and validate every tool.
• Ensure users can review all tool calls and inputs before execution.
• Integrate identity-aware security layers that enforce per-agent authentication and least privilege.

These measures should complement standard cybersecurity practices, such as secret management, encryption, and audit logging, to mitigate cascading risk across interconnected AI systems.

Conclusion: MCP Is Powerful—But Power Demands Control

The Model Context Protocol is one of the most transformative innovations in AI infrastructure, bridging the gap between LLMs and real-world data. Yet with every bridge comes a security trade-off. MCP’s flexibility and composability empower developers—but they also empower attackers.

Securing MCP isn’t about abandoning the protocol; it’s about governing it with identity-first principles. Organizations adopting MCP should treat it not as a harmless plugin layer but as a high-privilege integration channel that demands continuous monitoring, authentication, and isolation.

As MCP evolves, so must our security frameworks, identity strategies, and red-teaming practices. AI safety is no longer theoretical—it’s architectural.