🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits potential obfuscation techniques and low maintainer activity, raising concerns about its legitimacy and purpose.

Use of __import__ for dynamic module imports
Signs of low maintainer activity

Per-check LLM notes

Obfuscation: The use of __import__ to dynamically import modules and UUID generation suggests obfuscation techniques, potentially hiding code logic.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The package shows signs of low maintainer activity and metadata quality, which may indicate low effort or potential 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

Detailed PyPI description (6582 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

93 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 9.0

Found 6 network call pattern(s)

try: response = requests.post(API_URL, headers=headers, json=payload, timeout=30)
try: with httpx.Client(timeout=timeout_s) as client: resp = client.
try: with httpx.Client(timeout=timeout_s, follow_redirects=True) as client:
try: with httpx.Client(timeout=timeout_s) as client: files = {"file
ny], httpx.Headers]: with httpx.Client(timeout=timeout_s, headers=_auth_header(api_key)) as client:
mime};base64,{b64}" with httpx.Client(timeout=timeout_s, headers={"Authorization": f"Bearer {api_k

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

file=__import__("sys").stderr, ) if 200 <= res
filename = f"{__import__('uuid').uuid4().hex}{suffix}" if suffix else __import__('uuid').uui
.hex}{suffix}" if suffix else __import__('uuid').uuid4().hex if debug:
file=__import__("sys").stderr, ) if d
file=__import__("sys").stderr, ) r

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

try: result = subprocess.run([py, '-c', 'import requests'],
table.split('/')[-1]: subprocess.call([found_python] + sys.argv) sys.exit(0) else:

✓ 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 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 ai302

Create a command-line tool called 'AI302Manager' that leverages the functionalities of the 'ai302' package to manage and interact with 302.ai services. This tool will enable users to perform various operations such as deploying models, managing datasets, and monitoring service status directly from their terminal. Here’s a detailed plan on how to develop this application:

1. **Setup Project Environment**: Start by setting up a Python virtual environment and installing the 'ai302' package. Ensure your development environment is configured to support Python packages and CLI tools.

2. **Design Command Structure**: Design the CLI commands to be intuitive and user-friendly. Commands should include options for authentication, deployment, dataset management, and status checks.

3. **Authentication Module**: Implement an authentication module that allows users to log in and out of their 302.ai accounts. This will involve handling API keys securely and ensuring that users can switch between different accounts if needed.

4. **Deployment Module**: Develop a feature that enables users to deploy machine learning models to the 302.ai platform. Users should be able to specify model details, including name, version, and input/output configurations.

5. **Dataset Management**: Integrate functionality for uploading, downloading, and listing datasets stored on the 302.ai platform. Ensure that users have the ability to manage permissions and access levels for these datasets.

6. **Status Monitoring**: Provide real-time status updates for deployed models and datasets. Users should be able to check the health, performance metrics, and any alerts related to their resources.

7. **User Interface**: Create a clean and responsive CLI interface that provides clear instructions and feedback. Include help and documentation within the CLI itself.

8. **Testing and Documentation**: Write comprehensive tests to ensure all features work as expected. Also, create detailed documentation that explains how to install, configure, and use 'AI302Manager'.

Throughout the development process, utilize the 'ai302' package’s core features to streamline interactions with the 302.ai services. Your goal is to make 'AI302Manager' a go-to tool for anyone looking to efficiently manage their 302.ai resources.