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Security audit

股票九剑

Security checks across malware telemetry and agentic risk

Overview

This skill is a disclosed A-share stock analysis tool that fetches public market data, runs local analysis scripts, and optionally writes chart images, with no evidence of credential access, exfiltration, destructive actions, or hidden persistence.

Install only if you are comfortable with the skill sending queried stock codes to third-party public market-data providers and writing optional chart images locally. Treat its buy/sell, position, confidence, and risk outputs as automated analysis, not financial advice; verify with independent sources before making trades.

SkillSpector

By NVIDIA
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (11)

Lp3

Medium
Category
MCP Least Privilege
Confidence
94% confidence
Finding
The skill clearly invokes local Python scripts that fetch stock and market data, implying network access and executable code behavior, yet no permissions are declared. This creates a transparency and policy-enforcement gap: an agent may run code with broader capabilities than a user expects, reducing oversight and increasing the chance of unintended data access or unsafe execution.

Context-Inappropriate Capability

Medium
Confidence
91% confidence
Finding
The skill expands from deterministic stock-code analysis into open-ended searching of latest announcements and news, which materially broadens its data access and behavior beyond the declared workflow. Open-ended external retrieval can introduce prompt injection, untrusted content ingestion, and privacy or compliance issues if the agent follows or summarizes adversarial pages.

Missing User Warnings

Medium
Confidence
90% confidence
Finding
The description presents the skill as simple stock analysis but omits that it will automatically fetch external data and execute local Python scripts. This lack of disclosure undermines informed consent and makes the skill more dangerous because users and platforms cannot accurately assess operational risk, data flows, or execution side effects.

Missing User Warnings

Low
Confidence
86% confidence
Finding
The documentation describes a chart-generation tool that returns a PNG path, but it does not clearly warn users that invoking the tool will write files to local disk. In an agent/tooling context, unexpected file creation can create privacy, storage, and operational risks, especially if users assume the tool is read-only or if output paths are later exposed to other tools.

Missing User Warnings

Medium
Confidence
92% confidence
Finding
The setup and usage guidance does not prominently warn that the skill fetches market data from external network sources such as akshare-backed providers. In an MCP/agent environment, silent network access can violate user expectations, leak usage metadata or queried stock interests to third parties, and break deployment assumptions in restricted or sensitive environments.

Missing User Warnings

Medium
Confidence
94% confidence
Finding
The CLI emits direct trading recommendations, confidence scores, and risk labels without any visible disclaimer that the output is informational and may influence real financial decisions. In a finance-focused skill, this increases the chance that users over-trust the automation and act on potentially inaccurate signals, creating harm through unsafe decision support.

Unpinned Dependencies

Low
Category
Supply Chain
Content
akshare>=1.12.0
pandas>=1.5.0
numpy>=1.24.0
mplfinance>=0.12.10
Confidence
94% confidence
Finding
akshare>=1.12.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
akshare>=1.12.0
pandas>=1.5.0
numpy>=1.24.0
mplfinance>=0.12.10
matplotlib>=3.7.0
Confidence
94% confidence
Finding
pandas>=1.5.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
akshare>=1.12.0
pandas>=1.5.0
numpy>=1.24.0
mplfinance>=0.12.10
matplotlib>=3.7.0
Confidence
94% confidence
Finding
numpy>=1.24.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
akshare>=1.12.0
pandas>=1.5.0
numpy>=1.24.0
mplfinance>=0.12.10
matplotlib>=3.7.0
Confidence
94% confidence
Finding
mplfinance>=0.12.10

Unpinned Dependencies

Low
Category
Supply Chain
Content
pandas>=1.5.0
numpy>=1.24.0
mplfinance>=0.12.10
matplotlib>=3.7.0
Confidence
94% confidence
Finding
matplotlib>=3.7.0

VirusTotal

65/65 vendors flagged this skill as clean.

View on VirusTotal

Static analysis

No suspicious patterns detected.