araras

v2.6.3 suspicious
4.0
Medium Risk

A collection of tools for Machine Learning and Data Science

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks related to shell execution and code obfuscation, raising concerns about its intended use. While there is no clear evidence of malicious activity, the opaque nature of some code segments warrants caution.

  • Potential for command injection via os.system and subprocess calls
  • Unusual code formatting suggesting possible obfuscation
Per-check LLM notes
  • Network: The network call to an SMTP server might be legitimate if the package is designed for email functionality, but it should be verified for unexpected usage.
  • Shell: Direct use of os.system and subprocess calls can pose risks if not properly sanitized, especially when executing external commands like 'nvidia-smi'. This could indicate potential for command injection or unintended execution.
  • Obfuscation: The code snippets suggest potential obfuscation around model evaluation and performance measurement, which could be benign but raises suspicion due to the unusual formatting.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The maintainer's information is sparse, indicating potential lack of transparency.

📦 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 (4803 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

  • 350 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 score 1.5

Found 1 network call pattern(s)

  • n to SMTP server with smtplib.SMTP(smtp_server, smtp_port) as server: server.startt
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • el.to(torch_device) model.eval() warmups = max(int(warmup_runs or 0), 0) def _exe
  • e == "forward": model.eval() with torch.no_grad(): fc = FlopCounter
  • t was_training: model.eval() return float(fc.get_total_flops()) def get_model_flo
  • ng = model.training model.eval() try: # Use no_grad instead of inference_mode t
  • s_training: model.eval() def estimate_training_memory( model: torch.nn.Module
  • s_training: model.eval() total_bytes = param_bytes + grad_bytes + optimizer_st
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • "nt": # Windows os.system("cls") else: # macOS and Linux os.syste
  • # macOS and Linux os.system("clear") except Exception as e: vp.printf(f"Erro
  • startup fails. process = subprocess.Popen( [sys.executable, script_path], cwd=os.getcw
  • try: result = subprocess.run( [ "nvidia-smi",
  • ] process = subprocess.Popen( terminal_cmd, cwd=w
  • md] process = subprocess.Popen( terminal_cmd, cwd=w
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 araras 
Create a predictive maintenance tool for industrial machinery using the 'araras' Python package. This tool will analyze historical data from various sensors attached to different machines to predict potential failures before they occur. The application should be able to ingest real-time sensor data, process it through a series of machine learning models provided by the 'araras' package, and generate alerts when a machine shows signs of impending failure.

Key Features:
1. Real-time Data Ingestion: The tool should be capable of collecting live data from multiple sensors attached to various machines.
2. Data Preprocessing: Implement data cleaning and transformation steps using 'araras' utilities to prepare the raw sensor data for analysis.
3. Model Training & Evaluation: Utilize 'araras' to train several machine learning models on historical data. These models should be evaluated based on accuracy, precision, recall, and F1-score.
4. Predictive Analytics: Once trained, use these models to predict the likelihood of future machine failures based on incoming sensor data.
5. Alert System: If any machine has a high probability of failing according to the model predictions, the system should trigger an alert via email or SMS.
6. Dashboard: Develop a simple web-based dashboard using Flask or Django where users can visualize the health status of all machines in real-time.
7. Documentation: Provide comprehensive documentation detailing how each part of the system works, including setup instructions and explanations of the machine learning models used.

The 'araras' package plays a crucial role in this project by providing the necessary tools for data preprocessing, model training, evaluation, and prediction. It simplifies the process of applying advanced machine learning techniques to real-world problems, making it easier to develop efficient and accurate predictive maintenance solutions.

💬 Discussion Feed

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