初始版本:支持 28+ 新闻源、10+ LLM 模型、情感分析、图表生成、数据持久化、定时任务

Security checks across malware telemetry and agentic risk

Overview

The skill is not clearly malicious, but its main finance workflow can produce authoritative-looking reports from hardcoded articles and random sentiment labels.

Review this skill carefully before installing or using it. Treat the main.py output as prototype/demo output unless the mock news and random sentiment paths are replaced with real fetching and validated analysis. Do not rely on generated briefings for trading decisions, and only run provider-backed analysis after choosing which LLM service may receive your news text and API credentials.

SkillSpector

By NVIDIA
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (35)

Description-Behavior Mismatch

Medium
Confidence
86% confidence
Finding
The documentation states that configuration is auto-loaded and that reports/raw data may be written to disk, which extends the skill from transient analysis into persistent local storage. Persistence increases privacy and data-handling risk because fetched articles, derived analysis, and potentially user-provided URLs or prompts can remain on disk without explicit consent or retention controls.

Intent-Code Divergence

Medium
Confidence
98% confidence
Finding
The code explicitly labels sentiment analysis as mock/TODO but still feeds randomized outputs into the main workflow and presents them in an investment briefing as if they were meaningful. In a finance-analysis skill, fabricated sentiment, confidence, and impact fields can mislead users into making trading or risk decisions based on false information.

Description-Behavior Mismatch

High
Confidence
99% confidence
Finding
The skill advertises professional financial-news sentiment and impact assessment, but the implementation assigns random sentiment, confidence, and market-impact values. This is dangerous because the output appears authoritative while being untethered to the actual news content, creating a strong risk of user deception in an investment context.

Description-Behavior Mismatch

High
Confidence
98% confidence
Finding
Although the skill claims to fetch and analyze multi-source financial news, the main flow uses hard-coded sample articles instead of real fetched data. In this context, users may believe they are receiving current market intelligence when they are actually seeing stale or fabricated examples, which can directly distort financial decisions.

Intent-Code Divergence

Medium
Confidence
90% confidence
Finding
Functions documented as generating full, industry, or stock reports all silently fall back to the brief report. This discrepancy can mislead users about the depth and specificity of analysis they are receiving, which is especially problematic for finance workflows where users may rely on promised granularity.

Missing User Warnings

Medium
Confidence
84% confidence
Finding
The document advertises SQLite persistence, data export, scheduled execution, and API key handling without any user-facing disclosure of retention, sensitive-data handling, export risks, or system-impact considerations. In a skill that aggregates external content and uses third-party LLM APIs, this omission can lead users to enable storage and automation without understanding privacy exposure, credential sensitivity, or the operational footprint on the host system.

Missing User Warnings

Medium
Confidence
87% confidence
Finding
The main usage sections promote automated fetching, report generation, persistence, and scheduling without prominently warning that the skill will access remote sources and write data locally. That weakens informed consent and makes risky operations feel routine, which is especially problematic when processing user-provided URLs and storing outputs/history. Users may expose local environment context or accumulate sensitive market research artifacts without realizing it.

Missing User Warnings

Medium
Confidence
95% confidence
Finding
Saving raw data by default without a clear warning creates a real privacy and data-retention risk. Raw financial-news inputs may include user-supplied URLs, scraped article content, metadata, and analysis context that persist locally longer than users expect, increasing exposure if the host is shared or compromised.

Missing User Warnings

Medium
Confidence
94% confidence
Finding
The script sends user-supplied news text and file-derived content to third-party LLM providers, but it does not present an explicit consent step or warning at the point of transmission. In a finance-news skill, inputs may contain proprietary research, unpublished market-moving information, or sensitive internal summaries, so silent exfiltration to external APIs creates a real confidentiality and compliance risk.

Ssd 1

Medium
Confidence
97% confidence
Finding
User-controlled title, summary, and source fields are interpolated directly into the prompt, allowing a malicious article or supplied text to inject instructions that compete with or override the intended analysis task. Because the system then parses the model output as JSON and may use it downstream as trusted analysis, prompt injection can corrupt sentiment classifications, insert fabricated entities, or trigger denial of service through malformed output.

External Script Fetching

High
Category
Supply Chain
Content
### Q: 本地模型怎么安装?
```bash
# 1. 安装 Ollama
curl -fsSL https://ollama.com/install.sh | sh

# 2. 下载模型
ollama pull llama3.1
Confidence
98% confidence
Finding
curl -fsSL https://ollama.com/install.sh | sh

Unpinned Dependencies

Low
Category
Supply Chain
Content
## 基础依赖(必需)
```txt
requests>=2.31.0
beautifulsoup4>=4.12.0
lxml>=4.9.0
jinja2>=3.1.0
Confidence
95% confidence
Finding
requests>=2.31.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
## 基础依赖(必需)
```txt
requests>=2.31.0
beautifulsoup4>=4.12.0
lxml>=4.9.0
jinja2>=3.1.0
pyyaml>=6.0
Confidence
95% confidence
Finding
beautifulsoup4>=4.12.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
```txt
requests>=2.31.0
beautifulsoup4>=4.12.0
lxml>=4.9.0
jinja2>=3.1.0
pyyaml>=6.0
```
Confidence
96% confidence
Finding
lxml>=4.9.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
requests>=2.31.0
beautifulsoup4>=4.12.0
lxml>=4.9.0
jinja2>=3.1.0
pyyaml>=6.0
```
Confidence
95% confidence
Finding
jinja2>=3.1.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
beautifulsoup4>=4.12.0
lxml>=4.9.0
jinja2>=3.1.0
pyyaml>=6.0
```

## LLM API 依赖(可选,根据使用模型安装)
Confidence
96% confidence
Finding
pyyaml>=6.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
### OpenAI (GPT 系列)
```txt
openai>=1.0.0
```

### Anthropic (Claude 系列)
Confidence
93% confidence
Finding
openai>=1.0.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
### Anthropic (Claude 系列)
```txt
anthropic>=0.18.0
```

### 阿里通义千问
Confidence
93% confidence
Finding
anthropic>=0.18.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
### 阿里通义千问
```txt
dashscope>=1.14.0
```

### 百度文心一言
Confidence
92% confidence
Finding
dashscope>=1.14.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
### 百度文心一言
```txt
qianfan>=0.3.0
```

### 智谱 AI (GLM 系列)
Confidence
92% confidence
Finding
qianfan>=0.3.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
### 智谱 AI (GLM 系列)
```txt
zhipuai>=2.0.0
```

### Ollama (本地模型)
Confidence
92% confidence
Finding
zhipuai>=2.0.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
## 完整安装(所有模型支持)
```txt
requests>=2.31.0
beautifulsoup4>=4.12.0
lxml>=4.9.0
jinja2>=3.1.0
Confidence
95% confidence
Finding
requests>=2.31.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
## 完整安装(所有模型支持)
```txt
requests>=2.31.0
beautifulsoup4>=4.12.0
lxml>=4.9.0
jinja2>=3.1.0
pyyaml>=6.0
Confidence
95% confidence
Finding
beautifulsoup4>=4.12.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
```txt
requests>=2.31.0
beautifulsoup4>=4.12.0
lxml>=4.9.0
jinja2>=3.1.0
pyyaml>=6.0
openai>=1.0.0
Confidence
96% confidence
Finding
lxml>=4.9.0

Unpinned Dependencies

Low
Category
Supply Chain
Content
requests>=2.31.0
beautifulsoup4>=4.12.0
lxml>=4.9.0
jinja2>=3.1.0
pyyaml>=6.0
openai>=1.0.0
anthropic>=0.18.0
Confidence
95% confidence
Finding
jinja2>=3.1.0

VirusTotal

66/66 vendors flagged this skill as clean.

View on VirusTotal