AI Analysis
Final verdict: SUSPICIOUS
The package has low risks for network, shell, obfuscation, and credential threats. However, the metadata risk score is elevated due to low repository activity and a new maintainer, raising concerns about potential supply-chain attacks.
- Low repository activity
- New maintainer
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.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository's low activity and the maintainer's newness suggest potential risk, but there is no clear evidence of malintent.
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
Email domain looks legitimate: gmail.com
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 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "LuauYhen" 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 NTMem
Create a memory monitoring utility called 'MemoryGuard' using the Python package 'NTMem'. This utility will help users monitor their system's memory usage in real-time and provide alerts when certain thresholds are exceeded. Here are the steps and features to include: 1. **Setup and Initialization**: Begin by installing the 'NTMem' package. Ensure you have Python installed on your system, then install NTMem using pip. 2. **Real-Time Monitoring**: Implement a feature that continuously monitors the system's memory usage. Use NTMem's functions to retrieve current memory statistics such as total memory, used memory, and free memory. 3. **Threshold Alerts**: Allow users to set custom memory usage thresholds. When the system's memory usage exceeds these thresholds, send an alert via email or SMS. For simplicity, simulate sending an alert by printing a message to the console. 4. **Graphical Representation**: Display the memory usage data in a graphical format. You can use libraries like Matplotlib or Plotly to plot the memory usage over time. 5. **User Interface**: Develop a simple command-line interface where users can interact with MemoryGuard. They should be able to start/stop monitoring, view current memory stats, and manage their threshold settings. 6. **Logging**: Log all significant events such as threshold breaches, start/stop commands, and user interactions to a file for later review. For each step, detail how NTMem is utilized. For example, when retrieving memory statistics, explain how NTMem's functions are called to get the necessary data. Additionally, ensure that your implementation is efficient and does not consume too much system resources itself.