🤖 AI Analysis

Final verdict: SAFE

The package appears safe based on low scores across all categories with no clear signs of malicious intent or supply-chain attack.

Low network and shell risks
No evidence of credential harvesting

Per-check LLM notes

Network: The network calls appear to be establishing a connection to localhost on a specified port, which is likely part of the Zabbix protocol implementation and not indicative of malicious activity.
Shell: No shell execution patterns were detected.
Obfuscation: The use of zlib.decompress suggests data is being decompressed for legitimate purposes rather than obfuscation.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The author has only one package on PyPI, which may indicate a new or less active maintainer.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

try: s = socket.create_connection(("127.0.0.1", port), timeout=3) s.close()
data).encode('utf-8') socket.setdefaulttimeout(60) s = socket.socket(socket.AF_INET, socket.SOCK_ST

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

ompressed: body = zlib.decompress(body) return Response(body.decode('utf-8')) DEFAUL

✓ 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

Repository akomic/python-zabbix-proto appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Alen Komic" 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 ZabbixProto

Create a real-time monitoring dashboard using the Zabbix communication protocols with Python's 'ZabbixProto' package. Your application should allow users to connect to their Zabbix server and retrieve live monitoring data for various metrics such as CPU usage, memory usage, network traffic, etc., from different hosts and services. The dashboard should display this information in a visually appealing manner, utilizing graphs and tables to represent trends over time.

Step 1: Set up the environment - Ensure you have Python installed along with the 'ZabbixProto' package. This package simplifies the process of sending and receiving data according to the Zabbix protocol, which is essential for communicating with a Zabbix server.

Step 2: Implement user authentication - Allow users to log into your application by providing their Zabbix server URL, username, and password. Use the 'ZabbixProto' package to authenticate these credentials against the Zabbix server.

Step 3: Fetch monitoring data - Once authenticated, use the 'ZabbixProto' package to fetch live monitoring data for selected hosts and services. This data should include metrics like CPU usage, memory usage, disk space, and network traffic.

Step 4: Display the data - Design a user-friendly interface that displays the fetched data in real-time. Consider using libraries like Matplotlib or Plotly for graphing the data and Pandas for handling the data efficiently.

Suggested Features:
- Ability to select specific hosts and services for monitoring.
- Real-time updates for monitoring data every few seconds.
- Graphs showing trends over time for each metric.
- Tables displaying current values for each monitored item.
- Alerts or notifications when certain thresholds are exceeded.

How 'ZabbixProto' is Utilized:
- For authentication: Use the 'ZabbixProto' package to send authentication requests to the Zabbix server using the provided credentials.
- For data retrieval: Utilize the 'ZabbixProto' package to query the Zabbix server for monitoring data based on the selected hosts and services.
- For data processing: The package simplifies the handling of responses from the Zabbix server, making it easier to extract and process the required monitoring data.