QC Data Processor

• Outcome: Pass
• Latest audit: Jul 2, 2026 · 2:18 AM UTC
• Version: 1.0.2

Overview

This skill is a disclosed QC data analysis MCP server, with no evidence of hidden exfiltration or destructive behavior, but it should only be used with trusted local data files and controlled dependencies.

Install only in an environment where the MCP server can access the QC files you intend to analyze. Avoid pointing it at sensitive unrelated files, and prefer pinning or locking dependencies to reviewed versions before production use.

SkillSpector

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Trigger Abuse: Overly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access

Findings (9)

Vague Triggers

Medium

• Confidence: 87% confidence
• Finding: The trigger keyword list includes broad, common terms such as "日报", "周报", and "客诉", which can match routine business conversations unrelated to SPC or reliability analysis. This increases the chance of unintended skill activation, causing the agent to route user requests to this skill inappropriately and potentially expose local file-processing behavior or produce misleading analysis in the wrong context.

Missing User Warnings

Low

• Confidence: 93% confidence
• Finding: The function trusts `_file_path` from `data_schema` and directly opens it with `pd.read_csv`/`pd.read_excel` without validation, restriction, or user disclosure. In an MCP/server context, this can become arbitrary local file read, allowing a caller to access unintended files on the host if they can influence `data_schema`.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

mcp>=1.0.0
pandas>=2.0.0
openpyxl>=3.0.0
numpy>=1.24.0

• Confidence: 90% confidence
• Finding: Using a lower-bounded dependency specifier like `mcp>=1.0.0` makes builds non-reproducible and allows future package releases to be pulled in without review. This increases supply-chain risk and can unexpectedly introduce vulnerable or breaking versions into a QC data processing server that may parse untrusted files and expose networked MCP functionality.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

mcp>=1.0.0
pandas>=2.0.0
openpyxl>=3.0.0
numpy>=1.24.0
scipy>=1.10.0

• Confidence: 87% confidence
• Finding: `pandas>=2.0.0` is unpinned, so installations may resolve to different versions over time. While this is often operational rather than directly exploitable, it still creates supply-chain and reproducibility risk, especially for software that ingests external data files.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

mcp>=1.0.0
pandas>=2.0.0
openpyxl>=3.0.0
numpy>=1.24.0
scipy>=1.10.0
reliability>=0.8.0

• Confidence: 87% confidence
• Finding: `openpyxl>=3.0.0` permits any newer release to be installed, which can introduce unreviewed code paths into Excel parsing functionality. Because this skill likely processes spreadsheet input, dependency drift in a file-parsing library increases the attack surface for malicious document handling issues.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

mcp>=1.0.0
pandas>=2.0.0
openpyxl>=3.0.0
numpy>=1.24.0
scipy>=1.10.0
reliability>=0.8.0

• Confidence: 86% confidence
• Finding: `numpy>=1.24.0` is not version-pinned, so environments may install different releases without review. This primarily creates integrity and reproducibility concerns and can indirectly affect security if future compromised or vulnerable builds are consumed.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

pandas>=2.0.0
openpyxl>=3.0.0
numpy>=1.24.0
scipy>=1.10.0
reliability>=0.8.0

• Confidence: 86% confidence
• Finding: `scipy>=1.10.0` allows arbitrary newer versions, which weakens dependency control and reproducibility. In analytical software, this is usually a supply-chain hygiene issue rather than an immediate exploit, but it still raises risk when combined with automated deployments.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

openpyxl>=3.0.0
numpy>=1.24.0
scipy>=1.10.0
reliability>=0.8.0

• Confidence: 86% confidence
• Finding: `reliability>=0.8.0` is also unpinned, allowing unreviewed future versions into the environment. For a server performing statistical and reliability analysis, this creates supply-chain risk and can introduce insecure or unstable behavior unexpectedly.

Known Vulnerable Dependency: mcp==1.0.0 — 3 advisory(ies): CVE-2025-53366 (MCP Python SDK vulnerability in the FastMCP Server causes validation error, lead); CVE-2025-66416 (Model Context Protocol (MCP) Python SDK does not enable DNS rebinding protection); CVE-2025-53365 (MCP Python SDK has Unhandled Exception in Streamable HTTP Transport, Leading to )

High

• Category: Supply Chain
• Confidence: 98% confidence
• Finding: The dependency specification allows installation of `mcp==1.0.0`, which is reported as affected by multiple high-severity advisories including DNS rebinding protection gaps and transport/validation issues. Because this skill is an MCP server, the vulnerable package is directly in the network-exposed control plane, making the context materially more dangerous than a passive library use.

Static analysis

No suspicious patterns detected.