ai4icore-core

v1.1.9 suspicious
5.0
Medium Risk

AI4ICore Core - consolidated utility libraries (exceptions, logging, telemetry, observability, bootstrap, email) for AI4ICore microservices

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to its high obfuscation risk and the maintainer's incomplete profile. While there are no direct indicators of malicious activity, the combination of these factors raises concerns about potential supply-chain risks.

  • High obfuscation risk
  • Incomplete maintainer profile
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
  • Obfuscation: The use of base64 decoding and the suppression of built-in functions through 'eval' suggests potential obfuscation tactics that may hide malicious code.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: The maintainer has an incomplete profile and appears to be new or inactive, raising some suspicion but not conclusive evidence of malintent.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1971 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 154 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 10 unique contributor(s) across 100 commits in COSS-India/ai4i-core
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • try: audio_data = base64.b64decode(base64_audio) with wave.open(io.BytesIO(audio_da
  • y: return len(base64.b64decode(base64_audio)) / 32000 except Exception:
  • e.keys())}") result = eval(expression, {"__builtins__": {}}, namespace) logger.
✓ 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: tarento.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository COSS-India/ai4i-core appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 ai4icore-core 
Create a mini-application named 'AI4ILogMonitor' that serves as a log monitoring and alerting tool for developers. This tool will utilize the 'ai4icore-core' package to handle exceptions, logging, telemetry, and observability. The application should have the following functionalities:

1. **Log Monitoring**: Continuously monitor a specified directory for new log files.
2. **Error Detection**: Detect errors or warnings within the logs and categorize them.
3. **Alert System**: Send alerts via email when critical errors are detected.
4. **Telemetry Data Collection**: Collect telemetry data on log file sizes, error counts, and timestamps.
5. **User Interface**: Provide a simple web interface to view recent logs and error summaries.
6. **Configuration Management**: Allow users to configure directories to monitor, email recipients, and alert thresholds through a configuration file.

**Utilization of 'ai4icore-core':**
- Use 'ai4icore-core' for exception handling to ensure the application gracefully handles any issues encountered while monitoring logs.
- Implement custom logging mechanisms using the provided logging utilities to track the application's operations and status.
- Leverage the telemetry features to gather operational metrics such as log file activity and error rates.
- Utilize observability tools from 'ai4icore-core' to gain insights into the application's performance and behavior.
- Employ the email module within 'ai4icore-core' to send out alerts whenever critical errors are identified in the monitored logs.

This project aims to demonstrate the versatility and robustness of the 'ai4icore-core' package in building practical applications that require comprehensive error handling, logging, telemetry, and observability features.