🤖 AI Analysis

Final verdict: SUSPICIOUS

The package 'ac2' has a moderate risk score due to its metadata red flags, including potential typosquatting and lack of maintainership details. While it poses minimal direct threat based on current analysis, its purpose and origin raise concerns.

metadata risk with potential typosquatting
lack of maintainer history

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API access.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting no immediate risk to secrets or credentials.
Metadata: The package shows several red flags including typosquatting potential, lack of maintainer history, and no associated git repository.
⚠ Typosquatting target: arq

🔬 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 score 3.0

Possible typosquat of: arq

"ac2" is 2 edit(s) from "arq"

✓ Registered Email Domain

Email domain looks legitimate: appliedcompute.com>

✓ 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

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 ac2

Create a Python-based mini-application that leverages the 'ac2' package to manage and analyze data from a simulated sensor network. This application will serve as a tool for monitoring environmental conditions such as temperature, humidity, and air quality in real-time. The goal is to provide users with an interactive dashboard that displays current readings and historical trends, along with alerts for any abnormal conditions.

Steps to complete the project:
1. Set up the development environment including installing the 'ac2' package.
2. Design a simple API using Flask that interacts with the 'ac2' package to fetch live sensor data.
3. Develop a front-end interface using HTML/CSS/JavaScript to visualize the data in real-time. Include charts and graphs to show historical data trends.
4. Implement alert mechanisms within the application that notify users via email or SMS when sensor readings exceed predefined thresholds.
5. Integrate logging and error handling into the application to ensure robustness and maintainability.
6. Test the application thoroughly under various conditions to ensure reliability.
7. Document the setup process and usage instructions clearly.

Features:
- Real-time data visualization of sensor readings.
- Historical data analysis and trend prediction.
- Customizable alert settings for abnormal conditions.
- User-friendly dashboard for easy navigation and interaction.
- Logging and error reporting capabilities.

How 'ac2' is utilized:
- Use 'ac2' to simulate sensor data generation and management.
- Leverage 'ac2' functionalities for processing and analyzing sensor data.
- Implement 'ac2' methods for setting up alert systems based on sensor data thresholds.