afrilink-sdk

v0.8.16 suspicious
6.0
Medium Risk

AfriLink SDK — One-line access to GPUs, models and datasets from your notebook

🤖 AI Analysis

Final verdict: SUSPICIOUS

The Afrilink SDK presents a notable risk due to its execution of shell commands and handling of sensitive credentials, suggesting potential for unauthorized actions. Additionally, the lack of a repository and an inactive maintainer's account raise concerns.

  • High Shell risk due to potential for executing arbitrary commands
  • High Credential risk due to insecure handling of sensitive information
Per-check LLM notes
  • Network: Network calls are common for SDKs to communicate with services, but the use of urllib without proper input sanitization could pose a risk.
  • Shell: Executing shell commands can be risky, especially when installing packages or downloading files from untrusted sources, indicating potential for unauthorized actions.
  • Obfuscation: The base64 decoding is likely for legitimate purposes such as decrypting encoded data, but the incomplete code raises some suspicion.
  • Credentials: Direct use of getpass indicates handling of sensitive credentials, which could be legitimate but also poses a risk if not securely managed.
  • Metadata: The maintainer has a new or inactive account and the repository is not found, raising some concerns but not definitive proof of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • pt ImportError: req = urllib.request.Request( url, data=json.dumps(body).
  • try: with urllib.request.urlopen(req, timeout=timeout) as r: return {
  • ib fallback req = urllib.request.Request( url, data=_json.dum
  • try: with urllib.request.urlopen(req, timeout=30) as resp: body =
  • b.request urllib.request.urlretrieve(url, tarball) # Extract
  • else: req = urllib.request.Request( url, data=_json.dum
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • try: key_material = base64.b64decode(key_b64).decode("utf-8") except Exception: key_m
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • try: result = subprocess.run( cmd, capture_output=capture
  • cept ImportError: subprocess.run( [sys.executable, "-m", "pip", "install", "-
  • result = subprocess.run([path, "--version"], capture_output=True, text=True, timeout
  • bprocess.DEVNULL} subprocess.run(["wget", "-q", "-O", "/tmp/chrome.deb",
  • alse, **_devnull) subprocess.run(["apt-get", "install", "-y", "-qq", "/tmp/chrome.deb"], chec
  • alse, **_devnull) subprocess.run(["apt-get", "install", "-f", "-y", "-qq"], check=False, **_d
⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

  • d: password = getpass.getpass("DataSpires Password: ") if not email or not passwo
  • word: return getpass.getpass(prompt) else: return input(pro
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: dataspires.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "DataSpires" 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 afrilink-sdk 
Create a mini-application called 'GPU Model Trainer' that leverages the Afrilink SDK to facilitate easy access to GPUs, models, and datasets directly from a Jupyter notebook environment. This application will streamline the process of training machine learning models by abstracting away the complexities of setting up and managing computational resources.

### Key Features:
1. **Model Selection**: Users should be able to select from a variety of pre-configured machine learning models available through the Afrilink SDK. These models could include popular frameworks like TensorFlow, PyTorch, and others.
2. **Dataset Integration**: Provide a seamless way for users to load datasets into their training sessions either from local storage or from cloud-based sources supported by Afrilink.
3. **GPU Resource Management**: Automatically allocate GPU resources based on the model and dataset size requirements. The app should also allow users to specify preferred GPU configurations if needed.
4. **Training Execution**: Once a model and dataset are selected, the application should initiate the training process using the specified GPU resources. It should provide real-time feedback on the training progress.
5. **Model Evaluation & Saving**: After training, evaluate the model's performance using a test dataset and give the user the option to save the trained model locally or upload it to a cloud storage service via Afrilink.
6. **User Interface**: Develop a simple web interface (using Flask or a similar lightweight framework) to interact with the Afrilink SDK functionalities described above.

### Steps to Build the Application:
1. Set up a new Python project and install the Afrilink SDK.
2. Design and implement the backend logic for model selection, dataset loading, GPU resource allocation, and training execution.
3. Create a frontend UI that allows users to interact with the backend functionalities.
4. Test the application thoroughly with different models and datasets to ensure robustness.
5. Deploy the application locally or on a cloud platform for wider accessibility.

This project aims to demonstrate the power and flexibility of the Afrilink SDK in simplifying complex tasks related to machine learning model development.