🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderately high obfuscation and credential risks, alongside potential network activity, raising concerns about its true purpose and reliability.

High obfuscation risk due to base64 decoding
High credential risk suggesting attempts to restrict access to sensitive data

Per-check LLM notes

Network: The observed network call patterns suggest the package may be making HTTP requests to external URLs, which is not inherently suspicious but could indicate data exchange or communication with remote servers.
Shell: No shell execution patterns were detected, indicating low risk of direct system command execution from this package.
Obfuscation: The use of base64 decoding to write bytes suggests an attempt to hide the true nature of the data being written.
Credentials: Restricting access to sensitive system files and directories indicates potential intent to prevent interference with credential harvesting activities.
Metadata: The repository is not found, and the maintainer's information is sparse, indicating potential unreliability.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

Test runner config found: pyproject.toml
1 test file(s) detected (e.g. demo_smoke_test.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (21247 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

327 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

se_url self._client = httpx.AsyncClient(timeout=120) self._sandbox_id: str | None = None
rip("/") self._http = httpx.AsyncClient( base_url=self.base_url, headers={
sh_binary_url) async with httpx.AsyncClient(follow_redirects=True) as client: resp = await clien

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

Path(sys.argv[1]).write_bytes(base64.b64decode(sys.argv[2]))" ) return " ".join( [
Path(sys.argv[1]).write_bytes(base64.b64decode(sys.argv[2]))" ) node_writer = ( "require('n
try: decoded = base64.b64decode(stdout.encode("ascii"), validate=True) except Except

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

at"]}, {"deny": ["/etc/shadow", "/etc/gshadow", "/etc/sudoers", "/etc/sudoers.d/**"]},
{"allow": [ "/etc/hosts", "/etc/resolv.conf", "/etc/ssl/**", "/etc/
"/etc/nsswitch.conf", "/etc/passwd", "/etc/group", "/etc/fuse.conf", "/etc/gai
{"deny": ["/etc/hostname", "/etc/hosts"], "ops": ["write", "create"]}, {"deny": ["/var
{"deny": ["/etc/hostname", "/etc/hosts"], "ops": ["write", "create", "delete"]}, {"den
{"allow": [ "/etc/hosts", "/etc/resolv.conf", "/etc/ssl/certs/**",

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: canyonroad.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 agentsh-secure-sandbox

Your task is to develop a mini-application named 'SecureAIExplorer' using the Python package 'agentsh-secure-sandbox'. This application will serve as a secure environment for developers and researchers to experiment with AI models without exposing their local system to potential security risks. Here’s a step-by-step guide on what your application should achieve and how you can utilize the 'agentsh-secure-sandbox' package:

1. **Setup Environment**: Begin by setting up a virtual environment and installing the necessary packages, including 'agentsh-secure-sandbox'. Use pip for installation.
2. **Configuration**: Configure 'agentsh-secure-sandbox' to work within a chosen AI sandbox provider (e.g., Google Colab, AWS SageMaker). Ensure that the configuration allows for secure data transfer and model execution.
3. **User Interface**: Develop a simple command-line interface (CLI) that allows users to interact with the application. The CLI should support commands like 'start', 'stop', 'upload', 'download', and 'execute'.
4. **Secure Execution**: Implement functionality that leverages 'agentsh-secure-sandbox' to securely execute AI models within the sandbox environment. This includes uploading models, running inference, and downloading results.
5. **Data Management**: Integrate features that manage data securely. Users should be able to upload datasets to the sandbox, run AI tasks on them, and download the processed data back to their local machine.
6. **Logging and Monitoring**: Add logging capabilities to track operations performed within the sandbox. This could include logs of uploaded files, executed commands, and any errors encountered during execution.
7. **Documentation**: Provide comprehensive documentation detailing how to install and use SecureAIExplorer. Include examples and best practices for secure AI experimentation.

By following these steps, you'll create a powerful yet easy-to-use tool that promotes safe and efficient AI development practices.