anatools

v6.1.2 suspicious
6.0
Medium Risk

Tools for development with the Rendered.ai Platform.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several risks that warrant further investigation, particularly concerning credential handling and potential shell command execution. While these alone do not conclusively indicate malicious activity, they suggest a level of complexity and potential for misuse.

  • High risk associated with credential collection
  • Potential for uncontrolled shell execution
Per-check LLM notes
  • Network: Network calls are used to interact with external services, likely for downloading resources or accessing APIs, which is not inherently suspicious but should be reviewed against the package's documented behavior.
  • Shell: Shell execution commands indicate the package may interface with Docker, possibly for container management. This could be legitimate functionality, but uncontrolled shell execution poses a risk of executing arbitrary commands.
  • Obfuscation: The code snippet suggests partial Base64 decoding but lacks context to confirm malicious intent, indicating potential for data obfuscation or encoding.
  • Credentials: Direct usage of getpass.getpass indicates an attempt to securely handle user input for passwords, but the incomplete code raises suspicion about how credentials are stored or used post-collection.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags were raised.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • Test runner config found: pyproject.toml
  • 14 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7469 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

  • 3 type-annotated function signatures (partial)
○ 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 score 9.0

Found 6 network call pattern(s)

  • ') token_r = requests.post(token_url, data=token_data) if self.verbose
  • ="", flush=True) with requests.get(presigned_urls[i], allow_redirects=True) as response:
  • ')[0].split('/')[-1] with requests.get(url, stream=True) as downloadresponse: with open(fna
  • s = {} self.session = requests.Session() if self.headers: self.session.headers.
  • } response = requests.post(fileinfo['url'], data=data, files=files) if response
  • each request with requests.Session() as session: response = session.put(url, da
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • scii') decodedbytes = base64.b64decode(encodedbytes) decodedpass = decodedbytes.decode('asc
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • able try: probe = subprocess.run(['docker', 'version'], capture_output=True, text=True)
  • format json" result = subprocess.run(docker_space_cmd, shell=True, capture_output=True, text=True
  • s logfile: proc = subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr=subproc
  • uild exited {rc}') if subprocess.run(['docker', 'image', 'inspect', tag], capture_output=True).re
  • ] result = subprocess.run(cmd, capture_output=True, text=True, check=False)
  • rue, check=False) subprocess.run(["docker", "rm", container_name], capture_output=True)
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • self.__password = getpass.getpass('Password: ') else: self.__passw
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: rendered.ai

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Rendered AI, Inc" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with anatools 
Create a Python-based mini-application that leverages the 'anatools' package to manage and analyze data using the Rendered.ai Platform. This application will serve as a data management tool for researchers and data scientists who need to process, visualize, and store their datasets efficiently. Here’s a step-by-step guide on how to develop this application:

1. **Setup**: Begin by installing the 'anatools' package along with other necessary Python libraries such as pandas for data manipulation and matplotlib for data visualization.
2. **Data Ingestion**: Implement functionality within your application to ingest data from various sources (CSV files, SQL databases, etc.). Use 'anatools' to facilitate the connection and data fetching from Rendered.ai’s supported platforms.
3. **Data Processing**: Utilize 'anatools' to preprocess the ingested data. This could include cleaning the data, handling missing values, and transforming the data into a format suitable for analysis.
4. **Visualization**: Develop visualizations of the processed data using matplotlib or any other preferred visualization library. Ensure these visualizations are interactive and informative, providing insights into the data trends and patterns.
5. **Storage**: Integrate 'anatools' to store the analyzed data back onto the Rendered.ai platform. This storage mechanism should allow for easy retrieval and sharing of the results among team members.
6. **User Interface**: Consider adding a simple command-line interface (CLI) or a basic web interface using Flask or Django to make the application more user-friendly.
7. **Documentation**: Write comprehensive documentation detailing how to install and use the application, including examples and tutorials for common tasks.

Suggested Features:
- Support for multiple data formats (JSON, CSV, SQL)
- Advanced data filtering and sorting options
- Integration with popular machine learning models through Rendered.ai’s APIs
- Automated data backups to cloud storage
- Real-time collaboration features for team projects

Utilizing 'anatools' effectively throughout the development process will streamline the interaction with Rendered.ai’s services, making the application robust and scalable.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!