amp-instrumentation

v0.3.0 suspicious
4.0
Medium Risk

Automatic instrumentation for Python agent applications, monitored via WSO2 Agent Management Platform

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to subprocess execution and low maintenance status, though direct malicious activities are not evident.

  • Subprocess execution detected requiring further investigation
  • Low maintenance and poor metadata quality
Per-check LLM notes
  • Network: No network calls detected, which is typical and not indicative of malicious activity.
  • Shell: Subprocess execution detected but without suspicious flags or arguments that indicate malicious intent, however, it requires further investigation to confirm legitimacy.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintenance and poor metadata quality, but there are no clear indicators of malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: conftest.py
  • 6 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4634 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 4 type-annotated function signatures (partial)
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 8 unique contributor(s) across 100 commits in wso2/agent-manager
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • ent try: result = subprocess.run(args, env=env) sys.exit(result.returncode) excep
  • (bootstrap_dir) result = subprocess.run( [sys.executable, "-c", script], env=env,
  • "] = "test-key" result = subprocess.run( [sys.executable, "-c", script], env=env, capture_ou
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: wso2.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository wso2/agent-manager appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 amp-instrumentation 
Create a Python-based web scraping tool that monitors its performance and resource usage in real-time using the 'amp-instrumentation' package. This tool will scrape data from a specified website, store it in a local SQLite database, and continuously monitor its own CPU usage, memory consumption, and network activity. The monitoring data will be sent to the WSO2 Agent Management Platform for real-time analysis and alerting.

Step 1: Set up a basic Flask web server to serve as the user interface for the web scraping tool. Users should be able to input a URL and initiate the scraping process.

Step 2: Implement the web scraping functionality using Python's requests and BeautifulSoup libraries. Ensure that the scraper can handle dynamic content by integrating Selenium if necessary.

Step 3: Use the 'amp-instrumentation' package to automatically instrument your application. Configure it to collect metrics such as CPU usage, memory consumption, and network I/O.

Step 4: Integrate the SQLite3 library to store scraped data locally. Design a simple schema that fits the structure of the expected data.

Step 5: Implement error handling and logging mechanisms to ensure that any issues during scraping are captured and logged appropriately.

Step 6: Connect the collected metrics to the WSO2 Agent Management Platform. Configure the platform to send alerts if certain thresholds are exceeded, such as high CPU usage or prolonged network latency.

Suggested Features:
- A dashboard within the Flask app to display live scraping status and performance metrics.
- An option to schedule regular scrapes at specified intervals.
- Detailed logs and error reports that can be viewed directly through the Flask UI.
- The ability to pause and resume scraping operations.

How to Utilize 'amp-instrumentation':
- Install the 'amp-instrumentation' package via pip.
- Import and initialize the package at the start of your script.
- Enable automatic instrumentation for all relevant modules and functions within your application.
- Configure the package to report metrics to the WSO2 Agent Management Platform using the provided API endpoints.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!