anyscale

v0.26.102 suspicious
6.0
Medium Risk

Command Line Interface for Anyscale

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks related to shell execution and obfuscation techniques, which could potentially be leveraged for malicious purposes. However, there are no clear signs of credential harvesting or direct evidence of malicious intent.

  • High shell risk due to process group creation and shell command execution
  • Use of eval with '__IPYTHON__' for potential obfuscation
Per-check LLM notes
  • Network: Network calls may be legitimate for notifications and updates, but the presence of POST and PUT methods suggests possible data transfer to external services.
  • Shell: Creating new process groups and executing shell commands can be risky as it might indicate attempts to bypass security controls or execute arbitrary code.
  • Obfuscation: The code attempts to execute '__IPYTHON__' using eval, which is commonly used for obfuscation and can be risky.
  • Credentials: No clear patterns of credential harvesting are present.
  • Metadata: The package shows some signs of low maintenance and effort but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Brief PyPI description (451 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

  • 1006 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • try: r = requests.post(WORKSPACE_NOTIFICATION_ADDRESS, json=notification.dict())
  • } requests.put( info.url, data=zip_file_bytes, headers=head
  • _token else {} response = requests.get(url, headers=headers) response.raise_for_status() re
  • : str) -> str: async with aiohttp.ClientSession() as session: response = await asyncio.wait_for(
  • in the request async with aiohttp.ClientSession() as session: headers = {"Authorization": f"Bearer {
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • oks. """ try: eval('__IPYTHON__') except NameError: return False
  • lse b.encode() return pickle.loads(bb) else: raise TypeError(f'Unknown protocol: {p
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • a new process group with subprocess.Popen(*popenargs, start_new_session=True, **kwargs) as process:
  • rocess group and exit subprocess.Popen( f"while kill -s 0 {parent_pid}; do sleep 1; don
  • 9 -{child_pgid}", shell=True, # Suppress output stdout=subproces
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Anyscale Inc." 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 anyscale 
Develop a command-line tool named 'AnyscaleDash' that leverages the 'anyscale' Python package to monitor and manage remote Jupyter Lab sessions hosted on Anyscale. This tool should enable users to easily start, stop, and monitor their Jupyter Lab instances from their local machine. Additionally, it should provide status updates, session logs, and allow for interactive commands to be executed within these sessions.

### Features:
- **Session Management:** Users should be able to start new Jupyter Lab sessions, list all active sessions, and terminate specific sessions.
- **Status Updates:** Provide real-time status updates for each session, including whether they are running, idle, or stopped.
- **Log Access:** Allow users to view logs from their sessions to diagnose issues or track progress.
- **Interactive Commands:** Enable users to execute simple commands directly within the Jupyter Lab sessions from the command line.
- **User Interface:** Develop a clean and intuitive CLI interface for ease of use.

### How to Utilize the 'anyscale' Package:
- Use 'anyscale' to establish connections to the Jupyter Lab instances.
- Leverage its functionalities to start, stop, and interact with Jupyter Lab sessions remotely.
- Integrate logging capabilities to capture session activity and errors.

### Step-by-Step Guide:
1. **Setup Environment:** Install necessary packages including 'anyscale'.
2. **CLI Development:** Create the main script for 'AnyscaleDash' that accepts user commands.
3. **Session Management Functions:** Implement functions to handle starting, stopping, and listing Jupyter Lab sessions.
4. **Status Update Mechanism:** Develop a method to fetch and display the current status of each session.
5. **Log Integration:** Incorporate functionality to retrieve and show logs from the sessions.
6. **Interactive Command Execution:** Allow users to input commands to be executed within their Jupyter Lab sessions.
7. **Testing:** Thoroughly test the application to ensure all features work as expected.
8. **Documentation:** Write clear documentation for setup, usage, and troubleshooting.

This project aims to simplify the management of remote Jupyter Lab sessions, making it easier for developers to work collaboratively and efficiently on projects.

💬 Discussion Feed

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