🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant shell risk due to the use of os.system, which could potentially be exploited for malicious activities such as automated deployments. While there are no clear signs of credential theft or overtly malicious behavior, the combination of risks warrants further investigation.

High shell risk from os.system usage
Potential obfuscation techniques

Per-check LLM notes

Network: The network calls appear to be related to fetching images and making API requests, which could be legitimate depending on the package's functionality.
Shell: The use of os.system for executing shell commands suggests potential risks as it can be used to perform actions like packaging and uploading code to PyPI, indicating possible automated deployment processes that might bypass security checks.
Obfuscation: The observed patterns suggest base64 decoding of images and other data, which could be legitimate for handling binary data but may also indicate an attempt to hide code logic.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The maintainer has a single package and the repository is not found, raising some suspicion but not conclusive evidence of malice.

📦 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 (35207 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

71 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 9.0

Found 6 network call pattern(s)

response = requests.get(image) if response.status_code == 200:
=model response = requests.post(url, headers=headers,files=files, data=payload)
//')): response = requests.get(url) image_bytes = io.BytesIO(response.content)
1) resp = requests.get(source, timeout=30) resp.raise_for_statu
" response = requests.post( self.API_URL, headers=headers, json=pay
poll_resp = requests.get(poll_url, headers=headers, timeout=30) p

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

image = BytesIO(base64.b64decode(image)) image = Image.open(image)
image_bytes = io.BytesIO(base64.b64decode(base64_data)) # If the input is bytes, use
content=base64.b64decode(b64_data), content_type=mime_typ
inue raw_bytes = base64.b64decode(b64_data) artifacts.append( Imag
continue raw = base64.b64decode(b64) artifacts.append( ImageArti
image_bytes=base64.b64decode(inline_data["data"]), mime_type=inline_d

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

rsal) distribution…") os.system("{0} setup.py sdist bdist_wheel --universal".format(sys.exec
to PyPI via Twine…") os.system("twine upload dist/*") self.status("Pushing git tag
("Pushing git tags…") os.system("git tag v{0}".format(about["__version__"])) os.syst
bout["__version__"])) os.system("git push --tags") sys.exit() # Where the magic h

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: 23v.co

✓ 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 2.0

1 maintainer concern(s) found

Author "Ashish Tilekar" 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 aibridgecore

Create a versatile mini-app named 'AIChatBridge' that serves as a communication bridge between different Large Language Models (LLMs). This app will allow users to input a query and receive responses from multiple LLMs simultaneously, providing a comparative analysis of responses. The goal is to demonstrate the unique capabilities and limitations of each LLM, helping users understand which model might be best suited for their needs. Here’s a detailed breakdown of the app’s functionalities and steps to implement it using the 'aibridgecore' package:

1. **Project Setup**: Start by setting up your Python environment and installing the necessary packages including 'aibridgecore'. Ensure you have API keys or access tokens for the LLMs you plan to integrate.

2. **User Interface Design**: Design a simple yet effective user interface where users can enter their queries. This could be a basic web app using Flask or Django, or even a command-line interface if simplicity is preferred.

3. **Integration with 'aibridgecore'**: Utilize 'aibridgecore' to connect with various LLMs. Configure the settings within 'aibridgecore' to authenticate and communicate with each LLM effectively.

4. **Query Processing**: Implement logic within 'AIChatBridge' to process user inputs and send these queries to all connected LLMs through 'aibridgecore'. Ensure that each query is sent in a standardized format recognized by the LLMs.

5. **Response Handling & Display**: Once responses from the LLMs are received, handle them appropriately within 'AIChatBridge'. Analyze the responses to highlight similarities and differences, and present these findings to the user in a clear, understandable manner. Consider using charts or graphs to visualize the data if applicable.

6. **Feedback Mechanism**: Include a feedback mechanism within 'AIChatBridge' where users can rate the relevance and quality of responses from each LLM. Collect this data over time to improve the matching algorithm that suggests the most suitable LLM for future queries.

7. **Testing & Optimization**: Rigorously test 'AIChatBridge' with various types of queries to ensure reliability and accuracy. Optimize the performance based on user feedback and observed patterns in response quality.

By following these steps and utilizing 'aibridgecore', you'll create a powerful tool that not only bridges the gap between different AI models but also enhances user understanding and satisfaction.