🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe with a moderate risk score due to its primary function of running AI agents within Docker containers, which poses a higher shell risk. However, the lack of network, obfuscation, and credential risks, along with no evidence of malicious intent, suggests it is not a supply-chain attack.

Moderate shell risk due to execution of shell commands.
Low risk in other categories such as network, obfuscation, and credentials.

Per-check LLM notes

Network: Network calls to standard URLs like PyPI and Docker registries are generally benign, but unusual destinations could indicate potential issues.
Shell: Executing shell commands can be risky if not properly sanitized or controlled, potentially allowing unauthorized access or command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author has only one package and lacks PyPI classifiers, indicating potential low effort or inexperience.

📦 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

Detailed PyPI description (7549 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

109 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 6.0

Found 4 network call pattern(s)

L) try: request = urllib.request.Request(_PYPI_URL, headers={"Accept": "application/json"})
lication/json"}) with urllib.request.urlopen(request, timeout=PYPI_VERSION_CHECK_TIMEOUT_SECONDS)
ing[str, str]]: request = urllib.request.Request(url, headers=headers or {}) try: with ur
or {}) try: with urllib.request.urlopen(request, timeout=DOCKER_REGISTRY_REQUEST_TIMEOUT_SEC

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

r() try: result = subprocess.run( _UPGRADE_COMMAND.split(), check=Fal
tes]: try: return subprocess.run( command, check=check, s
tes]: try: return subprocess.run( command, check=check, c
] try: process = subprocess.run( run_command, check=False,

✓ 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 "Stefan Kuhn" 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 aicage

Create a versatile code assistance tool called 'CodeSailor' using the Python package 'aicage'. CodeSailor will serve as a local development environment where users can run coding assistants inside Docker containers to help them write, debug, and optimize their code. This tool aims to provide on-demand, context-aware coding support directly within a user's development workflow.

Step 1: Set up the project structure and install necessary dependencies including 'aicage'.
Step 2: Design a simple user interface (CLI) where users can interact with CodeSailor. This UI should allow users to specify the type of coding assistant they want to run (e.g., Python debugger, code formatter, linter).
Step 3: Implement functionality to launch Docker containers using 'aicage' based on user requests. Each container should run a specific coding assistant tailored to the task requested by the user.
Step 4: Integrate communication between the main application and the Docker containers so that users can send code snippets or files to the assistant and receive feedback or processed results.
Step 5: Add error handling and logging to ensure that any issues encountered during container execution are properly managed and logged for debugging purposes.

Suggested Features:
- Support for multiple programming languages through different coding assistants.
- Ability to save and load previous sessions or configurations.
- Option to customize Docker images used by 'aicage' for more advanced users.
- Real-time feedback from coding assistants to improve user experience.
- Integration with version control systems like Git for seamless workflow management.

How 'aicage' is Utilized:
'aicage' simplifies the process of running coding assistants inside Docker containers by abstracting away much of the complexity involved in container management. It allows developers to focus on defining the tasks their coding assistants should perform rather than worrying about the underlying infrastructure. By leveraging 'aicage', CodeSailor can dynamically instantiate coding assistants based on user input, ensuring that each request is handled efficiently and securely within isolated environments.