arrowbit

v0.3.5 suspicious
6.0
Medium Risk

arrowbit language

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package contains potentially harmful elements such as the use of eval and obfuscated strings, which could be exploited for malicious purposes. However, there are no direct indications of malicious intent or active attacks.

  • Use of eval and obfuscated strings
  • Low maintainer activity
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: The use of os.system for clearing the console can be benign, but it may indicate interactive usage. Further investigation into the context is recommended.
  • Obfuscation: The use of eval and obfuscated strings suggests potential for code injection or hiding malicious functionality.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: The package shows signs of low maintainer activity and effort, which may indicate a lower quality package but does not necessarily imply malicious intent.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7124 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 71 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • ion = entry[1:-1] result = eval(condition, {}) if result is True: return Object('BO
  • e, env): return node.eval(self, env) def start(self, env: Environment = None):
  • ue: self.ast.eval(self, self.env) if not self.is_cycle:
  • otImplementedError def eval(self, runtime, env: Environment) -> Any: raise NotI
  • obj.__repr__(deep) def eval(self, runtime, env: Environment) -> Object: return
  • ABLE {self.name}>" def eval(self, runtime, env: Environment) -> Object: try:
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • t(env: Environment): os.system('cls' if os.name == 'nt' else 'clear') logger.log(f
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with arrowbit 
Create a time-series data visualization tool using the Python package 'arrowbit'. This tool will allow users to input time-series data in various formats and visualize it on a customizable chart. The application should include the following features:

1. **Data Input**: Users should be able to upload CSV files containing time-series data (e.g., stock prices, weather data). The tool should support parsing dates from different columns and formats.
2. **Data Manipulation**: Utilize 'arrowbit' to manipulate and clean the uploaded data. This includes handling missing values, converting date formats, and filtering specific time periods.
3. **Visualization Customization**: Allow users to customize their charts by choosing between line graphs, bar charts, and area charts. Users should also be able to select which variables to plot and choose color schemes and other aesthetic options.
4. **Interactive Features**: Implement interactive elements such as tooltips that display detailed information when hovering over points on the graph, and zooming capabilities to explore data at different scales.
5. **Export Options**: Provide functionality for users to export their visualizations as high-resolution images or PDFs.
6. **Real-Time Data Feeds**: Integrate real-time data feeds from financial APIs (such as Alpha Vantage or Yahoo Finance) and use 'arrowbit' to process and visualize the incoming data streams.
7. **User Interface**: Develop a user-friendly interface using web technologies like Flask or Django for the backend and HTML/CSS/JavaScript for the frontend.

The 'arrowbit' package will be utilized throughout the project to handle all aspects of time manipulation, including but not limited to parsing dates, formatting timestamps, and calculating durations. Ensure that the tool demonstrates the robustness and flexibility of 'arrowbit' in managing complex time-series datasets.

💬 Discussion Feed

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