Full Report
MCP servers can expose enterprise secrets through plaintext configuration files, over-permissioned access and prompt injection, often before security teams even know the server is running. As more organizations adopt AI agents into their systems, that exposure can silently become a major gap in MCP server security. The Model Context Protocol (MCP) allows AI agents to reach the tools and data,
Analysis Summary
# Vulnerability: Model Context Protocol (MCP) Implementation Risks
## CVE Details
- **CVE ID:** CVE-2025-6514
- **CVSS Score:** Not explicitly listed in the text (NVD status pending per typical timelines for 2025/2026-era articles).
- **CWE:** CWE-256 (Plaintext Storage of Credentials), CWE-522 (Insufficiently Protected Credentials), CWE-94 (Improper Control of Generation of Code - "Prompt Injection").
## Affected Systems
- **Products:** Model Context Protocol (MCP) Servers and AI Agents (e.g., Anthropic, various third-party implementations).
- **Versions:** All current versions of MCP-integrated servers (e.g., `mcp-remote-control`, `mcp-server-git`, etc.).
- **Configurations:** Systems where MCP servers are deployed using local plaintext configuration files, environment variables, or broad "over-permissioned" API scopes.
## Vulnerability Description
The Model Context Protocol (MCP) acts as a bridge between AI agents and enterprise data/tools. The security flaw is not a single bug but a systemic architectural risk:
1. **Plaintext Storage:** MCP servers frequently store sensitive Non-Human Identities (NHIs) like API keys and service account tokens in local configuration files on disk.
2. **Prompt Injection:** AI agents can be manipulated via "hidden instructions" in documents or tickets to abuse the MCP server's tools, leaking credentials or executing unauthorized actions.
3. **Identity Governance Gap:** Because MCP servers act as active identities, they often lack the rotation, audit logging, and secret management typical of traditional enterprise software.
## Exploitation
- **Status:** PoC available (referenced via CVE-2025-6514 and supply chain risks); potential for exploitation in the wild regarding prompt injection.
- **Complexity:** Medium (requires knowledge of AI prompt manipulation or local access to config files).
- **Attack Vector:** Network (via Prompt Injection) or Local/Adjacent (via config file access).
## Impact
- **Confidentiality:** High (Exposure of enterprise secrets, API tokens, and internal data).
- **Integrity:** High (AI agents can be coerced into modifying data or executing commands in production environments).
- **Availability:** Medium (Unauthorized tool execution could lead to service disruption).
## Remediation
### Patches
- Users must update specific MCP server implementations as vendors release security patches (e.g., updates for `mcp-remote-control` and related libraries).
- Ensure the latest version of the MCP SDK from Anthropic is utilized.
### Workarounds
- **Principle of Least Privilege:** Limit MCP server scopes to the minimum required API permissions.
- **Secret Management:** Move credentials from plaintext config files to dedicated vaults (e.g., HashiCorp Vault, AWS Secrets Manager).
- **Input Sanitization:** Implement "human-in-the-loop" confirmations for sensitive actions triggered by AI agents.
## Detection
- **Indicators of Compromise:** Unusual API traffic originating from AI service accounts; unauthorized access to `.json` or `.yaml` configuration files containing "mcp" or "credentials."
- **Detection Methods:**
- Monitor for prompt injection patterns in LLM logs.
- Use static analysis to find plaintext tokens in developer repositories (Git leaks).
- Audit Non-Human Identities (NHIs) specifically associated with AI agent deployments.
## References
- Anthropic MCP Documentation: hxxps[://]modelcontextprotocol[.]io/
- CVE-2025-6514 Detail: hxxps[://]nvd[.]nist[.]gov/vuln/detail/CVE-2025-6514
- Keeper Security Analysis: hxxps[://]www[.]keepersecurity[.]com/blog/2026/01/05/how-the-model-context-protocol-is-redefining-zero-trust-for-ai-agents/