amla-sandbox

v0.2.8 suspicious
5.0
Medium Risk

Capability-based sandboxed runtime for AI agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to potential obfuscation techniques and credential checks, along with incomplete metadata. These factors raise concerns about its legitimacy and intent.

  • High obfuscation risk due to base64 decoding
  • Potential credential harvesting from reading '/etc/passwd'
  • Incomplete and suspicious metadata
Per-check LLM notes
  • Network: The package makes network calls to an external API which is common for fetching data or services but requires scrutiny on the legitimacy and purpose of the API.
  • Shell: No shell execution patterns detected, indicating a low risk of direct system command abuse.
  • Obfuscation: Base64 decoding is commonly used for obfuscation but could be legitimate in certain contexts.
  • Credentials: Reading '/etc/passwd' may indicate credential harvesting attempts, but it could also be part of a test case for capability checks.
  • Metadata: The author's information is incomplete and the account seems new or inactive, which raises some suspicion but does not conclusively indicate malice.

📦 Package Quality Overall: Medium (5.8/10)

✦ 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. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3020 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

  • 231 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 17 commits in amlalabs/amla-sandbox
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • response = await httpx.get(f"https://api.example.com/{method}") return
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • data", "") data = base64.b64decode(data_b64) if data_b64 else b"" if stream == 1:
  • ses: chunk_data = base64.b64decode(resp["result"]["data"]) reassembled += chunk_dat
  • t raise decoded = base64.b64decode(chunk_b64) assert len(decoded) > 0 def test
  • chunk_data = base64.b64decode(resp["result"]["data"]) reassembled += chunk
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • ate_call("fs/read", {"path": "/etc/passwd"}) with pytest.raises(CapabilityError):
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: amlalabs.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository amlalabs/amla-sandbox 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 amla-sandbox 
Create a secure, sandboxed environment for experimenting with AI agent interactions using the 'amla-sandbox' Python package. Your task is to develop a mini-application that allows users to safely run and test AI agents without risking their system security. This application will serve as a playground for developers and researchers interested in AI agent behavior and interaction within controlled conditions.

Step 1: Setup the Project
- Initialize a new Python virtual environment.
- Install the 'amla-sandbox' package and any other necessary dependencies.

Step 2: Design the Application Structure
- Create a main module that initializes the sandbox environment.
- Develop a command-line interface (CLI) for user interaction.

Step 3: Implement Core Functionality
- Use 'amla-sandbox' to create isolated environments for each AI agent.
- Allow users to upload AI agent code snippets through the CLI.
- Execute these code snippets in the sandboxed environment and monitor their behavior.
- Ensure that each agent runs in isolation from others, preventing interference and security breaches.

Suggested Features:
- Logging of all agent interactions and outputs for analysis.
- A scoring system to evaluate the performance of different agents based on predefined criteria.
- Integration with a simple visualization tool to display agent interactions graphically.
- Support for multiple concurrent agent instances, each running in its own sandbox.
- User-friendly error messages for common issues such as syntax errors or sandbox escape attempts.

How to Utilize 'amla-sandbox':
- Leverage the sandboxing capabilities provided by 'amla-sandbox' to ensure that each AI agent operates in a secure, isolated environment.
- Use the package's APIs to manage the lifecycle of these sandboxes, including creation, execution, and destruction.
- Employ the sandboxing mechanisms to enforce strict resource limits and prevent unauthorized access or modifications to the host system.

Your final deliverable should include a fully functional CLI application, comprehensive documentation, and examples showcasing how to use the sandboxed environment for testing various types of AI agents.

💬 Discussion Feed

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