AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate risk due to low-effort metadata maintenance and potential data obfuscation practices, though these alone do not conclusively indicate malicious intent.
- Low-effort metadata management
- Presence of base64 decoding suggesting possible data obfuscation
Per-check LLM notes
- Network: The use of async HTTP requests is common for fetching data from APIs and does not inherently indicate malicious activity.
- Shell: No shell execution patterns were detected.
- Obfuscation: The presence of base64 decoding suggests some form of data obfuscation, but it could be legitimate for various reasons such as encrypted configuration or secure data handling.
- Credentials: No clear signs of credential harvesting observed.
- Metadata: The package shows low effort in maintaining metadata and the author seems new or inactive, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Medium (6.0/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://docs.mozilla.ai/any-llm/Detailed PyPI description (8945 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
317 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
20 unique contributor(s) across 100 commits in mozilla-ai/any-llmActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
} async with httpx.AsyncClient() as http_client: response = await http_client.g
Code Obfuscation
score 4.0
Found 2 obfuscation pattern(s)
decoded_bytes = base64.b64decode(base64_data) except Exception as e:try: raw_data = base64.b64decode(encoded_data, validate=True) except binascii.Error as ex
Shell / Subprocess Execution
No shell execution patterns detected
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
Repository mozilla-ai/any-llm appears legitimate
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 any-llm-sdk
Create a Python-based mini-app called 'LLMQueryTool' that leverages the 'any-llm-sdk' package to interact with various large language models (LLMs). This tool will serve as a simple yet powerful interface for users to submit queries to different LLMs and receive responses. Here’s a step-by-step guide on how to build it: 1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python installed, then install the 'any-llm-sdk' package using pip. 2. **Define Core Features**: - **User Input Interface**: Design a user-friendly command-line interface where users can input their query. - **LLM Selection**: Implement a feature that allows users to select from a list of supported LLMs available through 'any-llm-sdk'. - **Query Submission**: Use 'any-llm-sdk' to submit the user's query to the selected LLM. - **Response Handling**: Capture the response from the LLM and display it back to the user in a readable format. 3. **Advanced Features**: - **History Log**: Keep a log of previous queries and responses. - **Custom Prompts**: Allow users to define custom prompts to guide the LLM's response. - **Error Handling**: Implement robust error handling to manage issues like network failures or invalid inputs. 4. **Integration with 'any-llm-sdk'**: Utilize the 'any-llm-sdk' package to authenticate with LLM services, configure request parameters, and handle responses. Explore the SDK documentation to understand how to initialize the client, make requests, and process responses effectively. 5. **Testing**: Thoroughly test the application with various queries and LLMs to ensure reliability and performance. 6. **Documentation**: Write clear documentation explaining how to use 'LLMQueryTool', including setup instructions and examples of how to leverage advanced features. This project aims to demonstrate the versatility and ease of use of the 'any-llm-sdk' package while providing a practical tool for interacting with LLMs.
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