独孤九剑V2

• Outcome: Pass
• Latest audit: Jul 24, 2026 · 3:54 PM UTC
• Version: 1.0.1

Overview

This stock-analysis skill is coherent and purpose-aligned, but users should understand that it contacts public market-data providers and may create local chart files.

Install only if you are comfortable with the skill contacting external Chinese market-data/news providers for requested tickers and saving generated chart PNGs locally. Prefer a virtual environment with pinned dependencies, and treat the trading output as analysis support rather than financial advice.

SkillSpector

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep
• MCP Least Privilege: Underdeclared Capability, Wildcard Permission, Missing Permission Declaration
• MCP Tool Poisoning: Hidden Instructions, Unicode Deception, Parameter Description Injection

Findings (11)

Lp3

Medium

• Category: MCP Least Privilege
• Confidence: 86% confidence
• Finding: The skill clearly instructs the agent to fetch live stock, capital-flow, and potentially news data from external services, yet no permissions are declared. This creates an undeclared network capability that can surprise hosts, bypass expected consent boundaries, and expose user queries or usage metadata to third-party endpoints.

Tp4

High

• Category: MCP Tool Poisoning
• Confidence: 81% confidence
• Finding: The advertised purpose is stock analysis, but the skill also exposes standalone framework-retrieval and chart-generation/file-writing capabilities. This widens the attack surface beyond the stated function, making it easier for users or other agents to invoke unintended behaviors and increasing the risk of unauthorized local file creation or capability misuse.

Context-Inappropriate Capability

Medium

• Confidence: 92% confidence
• Finding: The code explicitly implements anti-blocking measures such as randomized delays, User-Agent rotation, retries, and '防御层' logic to sustain access to third-party data sources. In a skill context, this crosses from ordinary API consumption into stealthy scraping behavior that can evade provider controls and may violate service terms, increasing legal, operational, and abuse risk.

Missing User Warnings

Low

• Confidence: 78% confidence
• Finding: The description omits a warning that the skill will fetch stock and possibly news data from external providers. Users may assume analysis is local, causing uninformed disclosure of requested tickers, timing, and related prompts to third parties, which is a transparency and privacy issue.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

akshare>=1.12.0
pandas>=1.5.0
numpy>=1.24.0
mplfinance>=0.12.10

• Confidence: 96% confidence
• Finding: The dependency is specified with only a lower bound, which allows future unreviewed versions to be installed. This creates supply-chain and reproducibility risk because builds may silently pick up breaking or compromised releases.

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: 96% confidence
• Finding: Using pandas with a >= constraint permits installation of any newer release, including versions not validated by the skill author. That weakens reproducibility and increases exposure to accidental breakage or supply-chain compromise.

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
requests>=2.28.0

• Confidence: 96% confidence
• Finding: An unpinned numpy dependency allows dependency resolution to select newer unvalidated versions over time. This is dangerous from a supply-chain perspective because security posture and behavior can change without code changes in the skill.

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
requests>=2.28.0

• Confidence: 95% confidence
• Finding: The mplfinance package is not pinned to a specific reviewed release, so future installations may resolve to different code than expected. That introduces modest but real supply-chain and operational risk.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

pandas>=1.5.0
numpy>=1.24.0
mplfinance>=0.12.10
matplotlib>=3.7.0
requests>=2.28.0

• Confidence: 95% confidence
• Finding: Matplotlib is allowed to float to any later version, which undermines deterministic builds and can introduce unreviewed code. While common in development, this remains a genuine dependency-management weakness.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

numpy>=1.24.0
mplfinance>=0.12.10
matplotlib>=3.7.0
requests>=2.28.0

• Confidence: 93% confidence
• Finding: The requests dependency is unpinned, allowing installation of arbitrary newer versions that may introduce regressions or vulnerabilities. Because this skill automatically fetches external stock data, network-facing libraries deserve stricter version control.

Known Vulnerable Dependency: requests==2.28.0 — 8 advisory(ies): CVE-2024-47081 (Requests vulnerable to .netrc credentials leak via malicious URLs); CVE-2024-35195 (Requests `Session` object does not verify requests after making first request wi); CVE-2026-25645 (Requests has Insecure Temp File Reuse in its extract_zipped_paths() utility func) +5 more

High

• Category: Supply Chain
• Confidence: 90% confidence
• Finding: The file permits requests>=2.28.0, and static analysis notes that requests 2.28.0 has multiple advisories. Because this skill fetches remote data, use of a vulnerable HTTP client can expose credential leakage, TLS/session verification flaws, or other network-originated attacks if the vulnerable version is resolved in deployment.

Static analysis

No suspicious patterns detected.