Model Context Protocol (MCP) — Enterprise Secrets Exposure Risks and RCE in mcp-remote (CVE-2025-6514)

Publication date: August 17, 2026
Category: AI Security / Identity Security

Introduction

As organizations increasingly integrate Artificial Intelligence (AI) agents into their operational and development workflows, new attack vectors emerge around Non-Human Identities (NHIs) and access credentials management. The open standard Model Context Protocol (MCP), originally introduced by Anthropic, allows AI assistants to connect with corporate databases, file systems, and cloud infrastructure. However, this bridge of convenience turns the MCP server into a critical repository holding API keys, tokens, and service credentials.

A recent report by The Hacker News, written in collaboration with Keeper Security, highlights how misconfigurations and governance gaps in these servers expose enterprise secrets. This risk is further magnified by critical flaws in ecosystem components, most notably CVE-2025-6514, an OS command injection vulnerability in the mcp-remote client proxy that grants remote code execution (RCE) on the end user’s machine.

What is Model Context Protocol (MCP)? (General Analysis)

The Model Context Protocol (MCP) is an open standard designed to provide Large Language Models (LLMs) with dynamic context and real-time access to external tools. Instead of relying solely on static training knowledge, an AI agent uses an MCP server as an intermediary to query records, manipulate files, or invoke Application Programming Interfaces (APIs).

Because the MCP server must interact with protected corporate resources, it requires by design privileges and authentication credentials to operate across those systems. This transforms AI agents into active identities within the corporate network. When MCP servers are deployed without rigorous security controls—such as plaintext credential storage or excessive permissions—any compromise of the server or manipulation of the agent can lead to massive data leakage and critical secret exposure.

For the vulnerability identified within the ecosystem, official NVD metadata includes:

  • CVE-2025-6514: Affects the mcp-remote component, which is exposed to OS command injection when connecting to untrusted MCP servers due to crafted input from the authorization_endpoint response URL.
  • CVSS v3.1 Score: 9.6 (CRITICAL)
  • Official CVSS Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H
  • Official CWE Classification: CWE-78 (Improper Neutralization of Special Elements used in an OS Command (‘OS Command Injection’))
  • CISA KEV Catalog: Not currently listed in the catalog.

How Does It Work? (Technical Analysis)

Analyzing the behavior of MCP servers and the specific vulnerability vector reveals structural architectural and execution risks:

  • Initial Infection Flow and Exploit Entry (CVE-2025-6514):
    mcp-remote acts as an OAuth proxy running on the client machine (with over 400,000 downloads). During the authentication or token exchange process, the client processes responses from the remote MCP server. If an attacker hosts a malicious MCP server, they can inject malicious parameters via the URL returned in the authorization_endpoint. Because the proxy insecurely handles this input when constructing operating system commands, an OS command injection (CWE-78) is triggered.

  • Execution and Privilege Escalation Mechanisms:
    The described command injection allows a remote attacker to achieve arbitrary remote code execution (RCE) within the security context of the user running the proxy on their workstation. Once inside the local machine, the attacker can extract configuration files, intercept session tokens, and harvest stored credentials to perform lateral movement into connected enterprise infrastructure.

  • Design-Level Exposure and Collateral Factors:
    Beyond the flaw in mcp-remote, MCP servers introduce logical exposure vectors due to:

    • Plaintext Credentials: Static storage of secrets in local configuration files (config.json, .env files) that frequently end up leaked in public Git repositories or insecurely copied.
    • Credential Sprawl: The absence of a central secret store leads to duplicated tokens and static keys scattered across development, staging, and production environments without rotation policies.
    • Prompt Injection: Indirect manipulation where an external document, email, or support ticket processed by the AI agent includes hidden malicious instructions, tricking the agent into leaking secrets or executing unauthorized actions.
    • Over-permissioning: Granting broad permissions by default to MCP servers to avoid authorization errors during development, violating the principle of least privilege.

Affected Systems / Environments

The risks described impact a wide variety of deployments integrating AI agents:

CVE Category (CWE) Impact CVSS Vector (summarized)
CVE-2025-6514 CWE-78 (OS Command Injection) Remote Code Execution (RCE) / Credential Theft 9.6 (Critical) Network / Low Complexity / No Privileges / User Interaction Required / Changed Scope
  • Specific Affected Components:
    • mcp-remote (vulnerable versions prior to the official security patch).
    • Development environments and engineer workstations running unvalidated OAuth MCP proxies.
    • Self-hosted MCP servers managing Non-Human Identities (NHIs) with access to cloud infrastructure and enterprise secret repositories.

Mitigation and Detection

Remediation

To contain the risks associated with MCP architecture and the mcp-remote vulnerability, strict hardening measures must be applied:

  • Component Updating: Immediately apply the security patch available in the official mcp-remote repository to fix input validation within the OAuth authentication flow.
  • Secret Centralization: Eliminate credential storage in plaintext configuration files, environment variables, or source code; utilize centralized secret managers (such as Keeper Secrets Manager) that mask values by default.
  • Ephemeral Credentials: Replace long-lived static secrets with on-demand generated credentials featuring automatic expiration and automated rotation policies.
  • Principle of Least Privilege: Strictly limit access permissions for each agent and enforce a human-in-the-loop workflow for critical or sensitive actions.

Detection

Defensive teams must implement continuous monitoring and behavioral analysis:

  • Regularly audit the inventory of active MCP servers across the network and workstations to eliminate instances of “Shadow AI”.
  • Monitor process execution derived from OAuth clients and local MCP proxies for anomalous commands or calls to command interpreters (cmd.exe, /bin/sh, /bin/bash).
  • Analyze audit logs for anomalous access patterns originating from non-human identities.

Defensive Security Warning: AI agents operate autonomously and at high speed; lack of visibility into exposed MCP servers allows attackers to exploit command injection vulnerabilities or prompt manipulation to compromise entire corporate identities without prior alerts on traditional perimeters.

Wrapping Up

The surge of AI agents powered by the Model Context Protocol (MCP) standard has introduced a critical new layer to enterprise architecture. While facilitating automation, it also centralizes high-value credentials and exposes organizations to severe risks, ranging from local misconfigurations to critical vulnerabilities like CVE-2025-6514 in mcp-remote. Securing this ecosystem demands a paradigm shift toward centralized secret management, rigorous enforcement of the principle of least privilege, and complete visibility over active AI components.

References