avai-monitor

v0.4.0 suspicious
7.0
High Risk

macOS / Linux host-security telemetry collector with an LLM threat judge and a single-page web dashboard.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple signs of potential misuse, including shell execution risks and obfuscation techniques. While the network and metadata risks are lower, the overall combination of findings raises concerns about its legitimacy and safety.

  • High shell execution risk
  • Potential credential and sensitive file handling issues
Per-check LLM notes
  • Network: Establishing a network session with a custom user-agent is not inherently malicious but requires further investigation into the purpose of the connection.
  • Shell: The presence of shell execution capabilities can be high risk if not properly controlled and documented, especially when interacting with system commands like 'spctl'. This suggests potential for unauthorized actions.
  • Obfuscation: Base64 decoding and JSON manipulation suggest an attempt to conceal code logic or data.
  • Credentials: Environment variable access for tokens and handling of sensitive files like /etc/hosts indicate potential unauthorized data extraction.
  • Metadata: The maintainer has an incomplete profile and a new account with only one package, which may indicate a less experienced or potentially suspicious user.

📦 Package Quality Overall: Low (4.2/10)

○ 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 (29862 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

  • 230 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 83 commits in iklobato/avai
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • 4.0): self._session = requests.Session() self._session.headers["User-Agent"] = _USER_AGENT
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • `).""" try: raw = base64.b64decode(b64key, validate=True) except (ValueError, binascii.Erro
  • rn "" try: return __import__("json").dumps(__import__("json").loads(value), indent=2) except
  • turn __import__("json").dumps(__import__("json").loads(value), indent=2) except Exception: retur
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • try: r = subprocess.run( cmd, capture_output=True, text=True, timeou
  • try: _gk = subprocess.run( ["spctl", "--status"], capt
  • ate, ] proc = subprocess.Popen( cmd, stdout=subprocess.PIPE,
  • try: r = subprocess.run( cmd, capture_output=True,
  • eading.Event): proc = subprocess.Popen( self._cmd(), stdout=subprocess.PIPE
⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

  • ", 4.0) self._token = os.environ.get("GITHUB_TOKEN", "") def _fetch(self, indicator: Indicator) -> Option
  • one. Names served from ``/etc/hosts`` never hit the wire, so they don't appear here — they'
  • d``: SSH authorized keys, /etc/hosts mappings, and privilege config — each list annotated wi
  • r(IndicatorExtractor): """/etc/hosts mappings — enrich the target IP (if public) and each re
  • unts (an attacker shadowing ``/etc/passwd`` with a tmpfs is a classic rootkit move) and persisten
  • Collector): """Snapshot ``/etc/hosts``. A mapping that points a real domain at an attacker I
✓ 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 iklobato/avai 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 avai-monitor 
Create a real-time security monitoring tool for macOS/Linux systems using the 'avai-monitor' Python package. This tool will collect host-level security telemetry data and provide real-time threat detection through an integrated large language model (LLM) threat judge. Additionally, it will offer a user-friendly single-page web dashboard for visualizing collected data and detected threats. Here’s a step-by-step guide on how to build this application:

1. **Setup Project Environment**: Initialize a new Python virtual environment and install the 'avai-monitor' package.
2. **Data Collection**: Utilize 'avai-monitor' to gather system logs, network activity, and other relevant security telemetry data from the host machine.
3. **Threat Detection**: Integrate an LLM capable of processing the collected telemetry data and providing real-time threat assessment. This could involve setting up an API endpoint for the LLM to receive and process data.
4. **Web Dashboard Development**: Develop a single-page web application that displays the collected data and threat assessments in an intuitive manner. Use frameworks like Flask or Django for backend development and React or Vue.js for frontend.
5. **Visualization**: Implement interactive charts and graphs within the web dashboard to visualize trends in security data over time.
6. **Alerting System**: Configure the application to send alerts (via email, SMS, etc.) whenever a potential threat is detected based on the LLM's judgment.
7. **User Interface Enhancements**: Add features such as customizable dashboards, user authentication, and the ability to export reports.
8. **Testing and Deployment**: Thoroughly test the application to ensure all components work seamlessly together, then deploy it to a cloud service provider like AWS or Heroku.

This project aims to create a comprehensive security monitoring solution that leverages modern AI capabilities and provides actionable insights to users.

💬 Discussion Feed

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