alab-cli

v0.1.5 suspicious
6.0
Medium Risk

Local agent-first experiment workbench CLI

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks, particularly concerning shell execution and credential handling, which are critical security issues.

  • High shell risk due to subprocess execution.
  • Significant credential risk from potential harvesting of environment variables.
Per-check LLM notes
  • Network: The use of urllib for network requests is common but should be reviewed for the destination URLs and data being transmitted.
  • Shell: Execution of subprocesses and Popen can introduce significant risk, especially if commands are not properly sanitized or if the package executes arbitrary code.
  • Obfuscation: No obfuscation patterns detected in the provided code snippet.
  • Credentials: The code snippet shows potential credential harvesting from environment variables, which is a significant risk.
  • Metadata: The package shows signs of low activity and maintenance, which could indicate it might be less trustworthy.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • Test runner config found: pyproject.toml
  • 12 test file(s) detected (e.g. test_auth.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (17161 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

  • 639 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 68 commits in bebetterest/ALab
  • Single author but highly active (68 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • tions, Request _URL_OPENER = urllib.request.build_opener(urllib.request.ProxyHandler({})) def _urlopen
  • = urllib.request.build_opener(urllib.request.ProxyHandler({})) def _urlopen(request: str | urllib.reque
  • def _urlopen(request: str | urllib.request.Request): return _URL_OPENER.open(request) def _field_
  • uld fail") request = urllib.request.Request( base_url + "/api/summary",
  • tems(): request = urllib.request.Request( base_url + endpoint,
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • try: completed = subprocess.run( list(args), cwd=str(cwd) if cwd els
  • ) try: proc = subprocess.Popen( command if command else ["/bin/sh", "-c", shell
  • tedProcess[bytes]: return subprocess.run( ["docker", *args], stdin=subprocess.DEVNULL
  • tedProcess[bytes]: return subprocess.run(["uv", *args], stdin=subprocess.DEVNULL, env=env, capture_ou
  • try: completed = subprocess.run( [str(python_path), str(wrapper), str(entry), st
  • fcntl.LOCK_EX) proc = subprocess.Popen( [sys.executable, "-c", script, str(home.path),
⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

  • == "1", "secret_env": os.environ.get("SECRET") == "sky-secret", }} print(f"visible={{os.environ.
  • '')}}") print(f"secret={{os.environ.get('SECRET', '')}}") return {{ "metrics": {{"combined_scor
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "ALab contributors" 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 alab-cli 
Create a Python-based local experimentation utility named 'AgentLab' using the 'alab-cli' package. This utility will serve as a sandbox environment for developers to test out various local agent configurations and behaviors without needing to set up complex cloud infrastructures. The application should have a user-friendly command-line interface (CLI) and allow users to define, run, and analyze experiments involving different types of agents (e.g., chatbots, recommendation systems, etc.).

### Core Features:
- **Experiment Definition**: Users should be able to define experiments by specifying the type of agents involved, the parameters for each agent, and the metrics to track during the experiment.
- **Agent Configuration**: Allow users to configure different aspects of the agents, such as their behavior algorithms, communication protocols, and data sources.
- **Execution & Monitoring**: The utility must execute the defined experiments and provide real-time monitoring capabilities to observe how agents interact and perform under different conditions.
- **Analysis Tools**: After an experiment concludes, the utility should offer tools to analyze the collected data, including visualizations and statistical summaries.
- **Report Generation**: Automatically generate comprehensive reports summarizing the experiment results, highlighting key findings and suggesting areas for improvement.

### Utilization of 'alab-cli':
- Use 'alab-cli' to manage the local setup required for running experiments. This includes initializing the workspace, installing necessary dependencies, and configuring the environment.
- Leverage 'alab-cli' commands to start, stop, and manage the lifecycle of individual experiments.
- Integrate 'alab-cli' functionalities to streamline the process of setting up and tearing down experimental setups, ensuring that developers can focus more on the actual experiments rather than the technical details.

### Additional Suggestions:
- Implement a feature allowing users to save and load experiment configurations, making it easier to reuse setups or share them with others.
- Incorporate a logging mechanism to record all actions performed within the utility, aiding in debugging and auditing purposes.
- Provide a tutorial or documentation within the utility to guide new users through the process of setting up and conducting their first experiment.

💬 Discussion Feed

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