autogluon.common

v1.5.0 suspicious
5.0
Medium Risk

Fast and Accurate ML in 3 Lines of Code

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks in network, shell execution, and obfuscation areas, indicating potential misuse or supply-chain attack vectors. However, there is no concrete evidence of malicious intent.

  • moderate network risk
  • shell execution risk
  • obfuscation risk
Per-check LLM notes
  • Network: The detected network call patterns suggest the package may be performing legitimate HTTP requests to external URLs, possibly for downloading resources or updates.
  • Shell: The shell execution patterns indicate that the package might be executing commands on the host system, which could be part of its functionality, but requires further investigation to ensure it's not being misused.
  • Obfuscation: The observed patterns suggest the use of pickle for deserializing objects from S3, which could be potentially risky due to the known vulnerabilities in Python's pickle module, but may also be legitimate depending on the context.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The author has only one package on PyPI, which could indicate a new or less active maintainer, raising some suspicion but not enough to conclusively determine malice.

📦 Package Quality Overall: Medium (7.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_check_style.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://auto.gluon.ai
  • Detailed PyPI description (10024 chars)
◈ Medium Contributing Guide 7.0

Some contribution signals present

  • Contributing link: "Contribute!" -> https://github.com/autogluon/autogluon/blob/master/CONTRIBUT
  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 114 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 18 unique contributor(s) across 100 commits in autogluon/autogluon
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • else: r = requests.get(url, stream=True, verify=verify_ssl) if
  • url(url: str): response = requests.get(url) response.raise_for_status() # Raise an error for b
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • resource("s3") return pickle.loads(s3.Bucket(s3_bucket).Object(s3_prefix).get()["Body"].read())
  • r bad status codes return pickle.loads(response.content) def load_with_fn(path, pickle_fn, format
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • y_free_info = _output_to_list(subprocess.check_output(COMMAND.split()))[1:] memory_free_values = [int(
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository autogluon/autogluon appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AutoGluon Community" 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 autogluon.common 
Create a simple yet powerful data exploration tool using the Autogluon Common package. This tool will allow users to quickly load datasets, perform basic exploratory data analysis, and apply machine learning models to predict outcomes based on user-defined features. The goal is to streamline the process of data science tasks, making it accessible even to beginners.

Steps:
1. **Setup**: Install necessary packages including Autogluon Common, pandas, matplotlib, and scikit-learn. Ensure your environment is set up correctly.
2. **Data Loading & Preprocessing**: Use Autogluon Common to facilitate easy data loading from various sources like CSV files, databases, etc. Implement functions to preprocess the data, such as handling missing values, encoding categorical variables, and scaling numerical features.
3. **Exploratory Data Analysis (EDA)**: Develop a module within the tool that allows users to visualize distributions, correlations, and relationships between different features in the dataset. Utilize Autogluon Common’s utilities for efficient data manipulation and visualization.
4. **Model Training & Evaluation**: Integrate Autogluon Common’s capabilities to train predictive models on the preprocessed dataset. Users should be able to select from a variety of algorithms and see immediate performance metrics. Implement cross-validation to ensure robust model evaluation.
5. **Prediction Interface**: Finally, create a user-friendly interface where users can input new data points and get predictions from the trained model. Ensure that the predictions are presented in a clear, understandable format.

Suggested Features:
- A clean, intuitive GUI built using libraries like Streamlit or Flask.
- Support for multiple file formats and database connections.
- Detailed EDA reports including statistical summaries and visual plots.
- Customizable model training parameters and options for hyperparameter tuning.
- Real-time prediction updates and explanations for model outputs.

How Autogluon Common is Utilized:
- For seamless data handling and preprocessing tasks, leveraging its built-in functions for efficient data manipulation.
- To expedite the model training phase, taking advantage of Autogluon Common’s automation and optimization techniques for quick yet accurate predictions.
- In facilitating the overall workflow, ensuring that each step from data ingestion to prediction is streamlined and optimized.

💬 Discussion Feed

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