anosys-claude-code

v0.2.8 suspicious
4.0
Medium Risk

AnoSys observability hook for Claude Code — automatic audit logging of Claude Code sessions

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate network risk due to its ability to transmit data via API calls, which could potentially lead to unauthorized data leakage. However, other risks such as shell execution, obfuscation, and credential harvesting are minimal.

  • Moderate network risk due to API calls
  • Sparse author metadata
Per-check LLM notes
  • Network: The presence of API calls and network requests with an API key suggests potential unauthorized data transmission.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is sparse, but there are no clear signs of malicious intent from the provided metadata.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_claude_code.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.anosys.ai
  • Detailed PyPI description (2456 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

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

Limited contributor diversity

  • 2 unique contributor(s) across 85 commits in anosys-ai/anosys-sdk
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • uote(api_key)}" req = urllib.request.Request( url, headers={"User-Agent":
  • ller"} ) with urllib.request.urlopen(req, timeout=10) as response: if respons
  • date_api_key with patch("urllib.request.urlopen") as mock_urlopen: mock_response = MagicMock
  • date_api_key with patch("urllib.request.urlopen") as mock_urlopen: mock_urlopen.side_effect
  • try: resp = requests.post(INGESTION_URL, json=chunk, headers=headers, timeout=15)
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: anosys.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository anosys-ai/anosys-sdk 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 anosys-claude-code 
Develop a mini-application named 'ClaudeAuditLogViewer' that leverages the 'anosys-claude-code' package to automatically log and visualize audit logs of Claude Code sessions. This application will serve as a tool for developers and administrators to monitor and review interactions within their Claude Code environments, ensuring compliance and aiding in debugging processes.

**Core Functionality:**
1. **Session Tracking**: Automatically track all sessions initiated through Claude Code using 'anosys-claude-code'. Ensure each session log includes details such as session start/end times, user information, and actions performed.
2. **Log Visualization**: Implement a user-friendly interface that displays these logs in a readable format, highlighting critical actions and anomalies.
3. **Search & Filter**: Allow users to search and filter logs based on specific criteria such as user ID, date range, action types, etc.
4. **Alerts & Notifications**: Set up alert mechanisms to notify administrators of suspicious activities or anomalies detected within the logs.
5. **Export Logs**: Provide functionality to export logs in various formats (CSV, JSON) for further analysis or record-keeping purposes.

**Utilization of 'anosys-claude-code':**
- Integrate 'anosys-claude-code' into your application to automatically capture and store logs of Claude Code sessions without manual intervention. Use its hooks to seamlessly embed logging into the existing workflow of Claude Code.
- Leverage the package’s capabilities to ensure comprehensive coverage of all actions taken during a session, including but not limited to code execution, file modifications, and user interactions.

💬 Discussion Feed

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