AI Analysis
Final verdict: SAFE
The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks. The metadata suggests a potential new or less active project, but there are no clear signs of malicious intent.
- No network calls detected
- Repository lacks community engagement
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: The maintainer has only one package and the repository lacks community engagement, suggesting it may be new or less active.
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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Moshe Shenfeld" 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 PLD-accounting
Create a privacy-aware financial dashboard app using Python's PLD-accounting package. This app will allow users to input financial transactions and then generate summaries and insights while maintaining user privacy through numerical privacy accounting techniques. Step 1: Set up the project environment by installing necessary packages including PLD-accounting. Step 2: Design a simple UI where users can add their financial transactions (e.g., income, expenses). Step 3: Implement functionality to calculate basic financial metrics like total income, total expenses, savings rate, etc. Step 4: Utilize PLD-accounting to perform privacy-preserving operations on the transaction data. Specifically, apply PLD-accounting's methods to ensure that each operation respects user privacy by providing differential privacy guarantees. Step 5: Develop a feature that generates a summary report of financial health, using the privacy-preserving metrics calculated in Step 4. Step 6: Add visualizations to the dashboard, such as pie charts showing the distribution of expenses or line graphs illustrating income trends over time. Step 7: Ensure all privacy-preserving operations are clearly documented within the app, explaining to users how their data is protected. Suggested Features: - User authentication to secure personal financial data. - Option to export privacy-preserving reports. - Integration with common financial APIs to import transactions. - Notifications for budget overruns or significant changes in financial status. How PLD-accounting is Utilized: PLD-accounting will be used to implement privacy-preserving aggregation and analysis of financial transactions. For example, when calculating total income or expenses, PLD-accounting methods will be applied to ensure that these calculations do not reveal sensitive information about individual transactions or users.
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