🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a high shell risk due to the use of potentially dangerous system commands and some obfuscation techniques, raising concerns about its intentions. However, it does not appear to harvest credentials or make network calls.

High shell risk due to system command usage
Some obfuscation techniques observed

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access.
Shell: The use of commands like 'rundll32', 'net start', 'shutdown', and 'certutil' suggests potential system-level modifications or administrative actions that could be risky if not properly documented.
Obfuscation: The use of __import__ with a dummy list suggests an attempt to bypass or obfuscate import mechanisms, which is suspicious.
Credentials: No clear patterns indicative of credential harvesting were found.
Metadata: The maintainer has a new or low activity account and the package lacks PyPI classifiers, suggesting potential low effort or poor metadata quality.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

, False): __import__(name, globals(), locals(), ["dummy"]) log.debug('Import
e): try: module = __import__(name, globals(), locals(), ["dummy"]) except (ImportError, SyntaxError)

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

ection(byref(wow64)) os.system(f'cmd /c "rundll32 setupapi.dll, InstallHinfSection DefaultI
tall 132 {new_inf}"') os.system(f"net start {service_name}") si = STARTUPINFO()
own command is robust subprocess.run(["shutdown", "/r", "/t", "0", "/f"], check=False) #
try: _ = subprocess.check_output( ["certutil", "-addstore", "-enterprise"
topping WMI Service") subprocess.call(["net", "stop", "winmgmt", "/y"], startupinfo=si) #
Stopped WMI Service") subprocess.call("sc config winmgmt type= own", startupinfo=si) log.

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: users.noreply.github.com>>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "enzok" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with CAPEsolo

Create a standalone application named 'CAPE Monitor' using the Python package 'CAPEsolo'. This application will serve as a monitoring tool for network traffic analysis, specifically focusing on identifying potential malicious activities. The application should have a user-friendly interface and provide real-time alerts for suspicious behavior detected in network packets.

Core Features:
1. Real-time packet capture from a specified network interface.
2. Analysis of captured packets to identify patterns indicative of malicious activity.
3. Integration of machine learning models provided by 'CAPEsolo' to enhance detection accuracy.
4. User-configurable alert system that sends notifications via email or SMS when suspicious activity is detected.
5. Historical data storage and visualization for past incidents.
6. A web-based dashboard for monitoring ongoing traffic and reviewing historical data.

Steps to Build the Application:
1. Set up a Python virtual environment and install 'CAPEsolo' along with other necessary libraries such as Scapy for packet manipulation, Flask for the web interface, and SQLAlchemy for database operations.
2. Design the database schema to store packet information and analysis results.
3. Implement the packet capture functionality using Scapy and integrate it with 'CAPEsolo' for analysis.
4. Develop the machine learning model integration to improve detection capabilities.
5. Create the alert system with configurable thresholds and notification methods.
6. Build the web-based dashboard using Flask, allowing users to monitor current traffic and view historical data.
7. Test the application thoroughly under different scenarios to ensure reliability and effectiveness.
8. Document the setup process, configuration options, and usage instructions for end-users.