apilinker

v0.7.1 safe
4.0
Medium Risk

A universal bridge to connect, map, and automate data transfer between any two REST APIs

🤖 AI Analysis

Final verdict: SAFE

The package shows some potential risks, particularly concerning subprocess usage and basic obfuscation techniques, but these do not strongly indicate malicious intent. Overall, the package appears safe with no evident signs of a supply-chain attack.

  • Moderate shell risk due to subprocess calls
  • Basic obfuscation techniques observed
Per-check LLM notes
  • Network: The use of HTTPX for network calls is common for making API requests and doesn't inherently indicate malicious activity.
  • Shell: Subprocess calls to git and the package itself may be used for version control and internal testing but could pose risks if not properly sanitized or controlled.
  • Obfuscation: The observed pattern suggests basic obfuscation but does not indicate malicious intent; it could be part of normal functionality to serialize configuration.
  • Credentials: No clear evidence of credential harvesting or secret handling is present.
  • Metadata: The author has only one package, suggesting a potentially new or less active account.

📦 Package Quality Overall: Medium (7.2/10)

✦ High Test Suite 9.0

Test suite present — 18 test file(s) found

  • Test runner config found: pyproject.toml
  • 18 test file(s) detected (e.g. test_aggregation.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://apilinker.readthedocs.io/
  • Detailed PyPI description (3396 chars)
✦ High Contributing Guide 9.0

Has contribution guidelines and governance files

  • Governance file: security.py
  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 483 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in kkartas/APILinker
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • session = requests.Session() return {"session": session, "base_url"
  • ns=128) self.client = httpx.AsyncClient( base_url=self.base_url, timeout=self.timeout, l
  • scope response = httpx.post( auth_config.token_url, data
  • } response = httpx.post( auth_config.token_url, data
  • scope response = httpx.post( auth_config.device_authorization_url,
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • h / "c.json" p.write_text(__import__("json").dumps(cfg)) l = ApiLinker(config_path=str(p), log_level
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • [str]: try: sha = subprocess.check_output( ["git", "rev-parse", "HEAD"], cwd=cwd or os.get
  • nd works.""" result = subprocess.run( [sys.executable, "-m", "apilinker", "--help"],
  • nd works.""" result = subprocess.run( [sys.executable, "-m", "apilinker", "version"],
  • run flag.""" result = subprocess.run( [ sys.executable, "-m", "apilin
  • fig, f) result = subprocess.run( [ sys.executable, "-m",
  • fig file.""" result = subprocess.run( [ sys.executable, "-m", "apilin
✓ 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

Repository kkartas/APILinker appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "K. Kartas" 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 apilinker 
Your task is to create a mini-application called 'DataBridge' using the Python package 'apilinker'. This application will serve as a universal bridge to connect, map, and automate data transfer between two different REST APIs. Here’s a step-by-step guide on how to build it:

1. **Project Setup**: Begin by setting up your Python environment and installing necessary packages including 'apilinker'. Ensure you have API keys or credentials from at least two REST APIs that you wish to connect.
2. **Connecting APIs**: Use 'apilinker' to establish connections with both APIs. Your goal is to retrieve data from one API (let's call it Source API) and send it to another (Target API).
3. **Data Mapping**: Implement a feature within 'DataBridge' that allows users to define how data fields from the Source API should be mapped to corresponding fields in the Target API. This could involve renaming fields, transforming data types, or even performing simple calculations on the data before sending it over.
4. **Automated Data Transfer**: Integrate a scheduler into 'DataBridge' so that data transfers occur automatically at regular intervals (e.g., every hour). This ensures continuous and reliable data flow between the two APIs.
5. **Error Handling & Logging**: Add robust error handling mechanisms to manage scenarios where data retrieval or transmission fails. Additionally, implement logging to track all actions performed by 'DataBridge', including successful transfers and errors encountered during execution.
6. **User Interface**: Develop a basic command-line interface (CLI) for 'DataBridge'. This CLI should allow users to configure API connections, set up data mappings, schedule transfers, and view logs.
7. **Security Measures**: Since 'DataBridge' will handle sensitive information, ensure all communications between APIs are encrypted using HTTPS. Also, securely store API credentials and avoid exposing them in plain text.
8. **Testing & Documentation**: Before finalizing 'DataBridge', thoroughly test its functionality across various scenarios. Document all steps involved in setting up and using 'DataBridge', ensuring new users can easily understand and operate the tool.

By following these steps, you'll create a versatile and user-friendly mini-application that leverages the powerful capabilities of 'apilinker' to streamline data transfer processes between REST APIs.

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