Raw vector
CVSS:3.1/AV:N/AC:H/PR:H/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2025-66404 is a medium-severity Command Injection (CWE-77) vulnerability in Suyogs Mcp-Server-Kubernetes. Its CVSS base score is 6.4 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 28% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Protocol-Specific Risks risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-66404 is a command injection vulnerability (CWE-77) in the exec_in_pod tool of the mcp-server-kubernetes MCP Server, which connects to and manages Kubernetes clusters. In versions prior to 2.9.8, the tool accepts user-provided commands in both array and string formats. String-format commands are passed directly to shell interpretation via sh -c without input validation, enabling interpretation of shell metacharacters.
The vulnerability has a CVSS v3.1 base score of 6.4 (AV:N/AC:H/PR:H/UI:R/S:U/C:H/I:H/A:H), indicating exploitation over the network but requiring high attack complexity, high privileges, and user interaction. Attackers with sufficient privileges can exploit it through direct command injection by supplying malicious strings or via indirect prompt injection attacks, where AI agents execute unintended commands on Kubernetes pods without explicit user intent, potentially leading to high-impact confidentiality, integrity, and availability compromises.
The vulnerability is fixed in version 2.9.8, as detailed in the project's GitHub security advisory (GHSA-wvxp-jp4w-w8wg) and the corresponding commit (d091107ff92d9ffad1b3c295092f142d6578c48b). Security practitioners should upgrade to 2.9.8 or later and review usage of the exec_in_pod tool, particularly in environments integrating AI agents.
This issue highlights risks in AI/ML-adjacent tools interfacing with infrastructure like Kubernetes, where prompt injection can bypass intended controls. No public evidence of real-world exploitation is available at publication.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-201109
Vulnerability Data
MCP Server Kubernetes is an MCP Server that can connect to a Kubernetes cluster and manage it. Prior to 2.9.8, there is a security issue exists in the exec_in_pod tool of the mcp-server-kubernetes MCP Server. The tool accepts user-provided commands…
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in both array and string formats. When a string format is provided, it is passed directly to shell interpretation (sh -c) without input validation, allowing shell metacharacters to be interpreted. This vulnerability can be exploited through direct command injection or indirect prompt injection attacks, where AI agents may execute commands without explicit user intent. This vulnerability is fixed in 2.9.8.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Protocol-Specific Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, mcp, prompt injection
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.2.3V1.2.5V1.2.8V1.2.9
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover command-construction flaws before deployment.
Input validation directly stops construction of commands from untrusted data containing special elements.
Secure engineering principles include proper neutralization and safe command construction practices.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly require input validation and neutralization that prevent command injection.
Runtime monitoring of software and data can detect anomalous command execution resulting from injection.
Identifying recorded vulnerabilities enables remediation of command-injection flaws before exploitation.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Secure coding standards require proper escaping and parameterization of commands, directly eliminating CWE-77.
Security testing in development catches command-injection vulnerabilities before release.
Secure development life cycle mandates input validation and command construction practices that directly prevent command injection.
Application security requirements explicitly call for controls against injection flaws including command injection.
Secure architecture principles reduce the attack surface but do not prescribe the specific neutralization techniques needed.
Environment separation limits the blast radius of an exploited command injection but does not prevent the flaw itself.