AI Analysis
Final verdict: SUSPICIOUS
The package has minimal direct security risks but lacks sufficient metadata, raising concerns about its origin and maintenance.
- Repository not found
- Sparse author information
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
- Metadata: The repository not being found and the author's information being sparse suggest potential risks.
Package Quality Overall: Low (4.2/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
1 test file(s) detected (e.g. test_package.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/arogozhnikov/antipickle#readmeDetailed PyPI description (5515 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
18 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
score 3.0
Suspicious email domain flags: Very short email domain: my.personal.blog>
Very short email domain: my.personal.blog>
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 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 antipickle
Create a Python-based mini-application named 'DataGuard' which leverages the 'antipickle' package as its core serialization mechanism. This application will serve as a simple yet powerful data security tool, enabling users to serialize and deserialize their sensitive data while ensuring it remains secure against common attacks such as deserialization vulnerabilities that 'pickle' is known for. The 'DataGuard' application should include the following functionalities: 1. **User Interface**: Develop a simple command-line interface (CLI) that allows users to interact with the application. 2. **Serialization**: Implement a feature where users can input their data (text or binary) and have it serialized using 'antipickle'. This should ensure that the data is not only stored but also encrypted to prevent unauthorized access. 3. **Deserialization**: Users should be able to input a serialized file and have it deserialized back into its original form. The application must verify the integrity of the data before deserialization to avoid any potential security risks. 4. **Security Measures**: Integrate additional layers of security such as password protection for both serialization and deserialization processes. 5. **Logging**: Maintain a log of all operations performed within the application for auditing purposes. 6. **Help Documentation**: Include comprehensive help documentation within the CLI to guide users on how to use each feature effectively. Throughout the development process, emphasize the utilization of 'antipickle' over traditional 'pickle' to highlight its unique advantages in terms of security and reliability. Ensure that your implementation showcases these benefits clearly through comments and documentation.
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