AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate risks due to potential shell execution and network interactions without clear documentation, suggesting possible unauthorized use.
- Shell risk at 5/10
- Lack of package description
Per-check LLM notes
- Network: The network call pattern suggests HTTP requests which might be used for legitimate purposes like API calls or updates but requires further investigation to confirm.
- Shell: The shell execution pattern indicates the package may execute commands on the system, potentially for building documents or other tasks, but could also be used for unauthorized actions.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows low maintenance and effort, raising some suspicion but not definitive evidence of malice.
Package Quality Overall: Low (3.0/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ 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
313 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)
r {})} self._client = httpx.Client( follow_redirects=False, timeout=htt
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 4.0
Found 2 shell execution pattern(s)
exRunResult: result = subprocess.run( self._pdflatex_cmd(build_name, cv_data_dir),cwd. See ADR-0038. proc = subprocess.run( args, input=stdin, capture_output=T
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with application-pipeline
Create a fully-functional mini-application called 'DataFlowDirector' using the Python package 'application-pipeline'. This application will serve as a data processing pipeline manager for various data sources and destinations. It will allow users to define pipelines that can ingest data from different sources (such as CSV files, databases, or APIs), process it through a series of customizable stages (like filtering, transformation, or enrichment), and then output the processed data to various destinations (like another database, file storage, or even real-time analytics services). The core functionality of 'DataFlowDirector' includes: - Defining pipelines with multiple stages. - Configuring each stage with specific operations (e.g., filtering out rows based on certain criteria, applying transformations to data, enriching data with external sources). - Supporting dynamic configuration changes without needing to restart the application. - Providing a user-friendly interface for managing pipelines and monitoring their status. - Logging and alerting mechanisms for troubleshooting and performance monitoring. Here’s how you would utilize the 'application-pipeline' package: 1. **Pipeline Definition**: Use the package to define the structure of your data processing pipelines, including specifying the input source, the sequence of processing stages, and the output destination. 2. **Stage Configuration**: Each stage in the pipeline can be configured with specific operations using the functionalities provided by the 'application-pipeline' package. For example, if a stage involves filtering, you might use a predefined filter function or create a custom one within the framework of the package. 3. **Execution and Monitoring**: Leverage the package's capabilities to execute the defined pipelines and monitor their progress. Implement logging and alerting systems to keep track of any issues during execution. 4. **Dynamic Management**: Ensure that the application allows for dynamic modification of pipelines while they are running, such as adding new stages or changing existing ones without interrupting the ongoing processes. 5. **User Interface**: Develop a simple web-based UI that enables users to view, modify, and manage their pipelines. This UI should also display real-time statuses and logs related to pipeline executions. Your task is to design and implement 'DataFlowDirector', ensuring it adheres to best practices in software development, leverages the 'application-pipeline' package effectively, and provides value through its unique features and usability.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue