AI Analysis
Final verdict: SAFE
The anomalib package poses a low risk with no detected network calls, shell execution beyond expected functionality, obfuscation, or credential harvesting. The main concern is the maintainer's single package, which might indicate a new or less active account.
- No network calls detected.
- Limited shell execution as expected.
- Maintainer has only one package.
Per-check LLM notes
- Network: No network calls detected, indicating low risk.
- Shell: Shell execution is limited to checking Nvidia CUDA version and running optimization commands, suggesting it's likely part of the package's intended functionality.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which could indicate a new or less active account, but there are no other red flags.
Package Quality Overall: Low (4.2/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (14539 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
142 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
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 4.0
Found 2 shell execution pattern(s)
# CVS-122665 subprocess.run(optimize_command, check=True) # noqa: S603 # nosec B603 #ase try: result = subprocess.run( ["nvcc", "--version"], # noqa: S607
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 2.0
1 maintainer concern(s) found
Author "Intel OpenVINO" 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 anomalib
Create a real-time anomaly detection system for video streams using the 'anomalib' Python package. This system will monitor live video feeds from a webcam or a network camera and detect anomalies in the scene, such as unexpected movements or changes in the environment. The application should have the following functionalities: 1. **Live Video Capture**: Integrate a module that captures video frames from a webcam or a network camera. 2. **Anomaly Detection**: Utilize 'anomalib' to process these video frames and identify any anomalies. The package provides various models for anomaly detection, so choose one that best suits the scenario. 3. **Visualization**: Display the live video feed on the screen with highlighted areas of detected anomalies. Use overlays or bounding boxes to mark the anomalous regions. 4. **Alert System**: Implement an alert mechanism that triggers when an anomaly is detected. This could be a simple pop-up message or a more sophisticated alert like sending an email or SMS notification. 5. **Configuration Interface**: Develop a user-friendly interface where users can configure parameters such as sensitivity levels, types of anomalies to detect, and alert settings. 6. **Logging and Reporting**: Log all detected anomalies into a database or file for later analysis and reporting purposes. 7. **Performance Monitoring**: Include a feature to monitor the performance of the anomaly detection model, such as inference time and accuracy metrics. The 'anomalib' package will be primarily used for training and deploying anomaly detection models. You'll need to train a model on a dataset representative of the scenes you want to monitor, then use the trained model to make real-time predictions on incoming video frames. Ensure the application is modular and scalable, allowing for easy integration of additional models or cameras in the future.
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