AI Analysis
Final verdict: SAFE
The package appears legitimate with minimal risks identified. It primarily interacts with expected external services and does not exhibit signs of malicious behavior.
- Low network, shell, obfuscation, and credential risks.
- Metadata risk slightly elevated due to a single-package maintainer profile.
Per-check LLM notes
- Network: The network call pattern indicates the package is likely making API calls to AlphaFold, which is expected if it's fetching data from an AlphaFold service.
- Shell: No shell execution patterns were detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malintent.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
tMap) -> None: async with aiohttp.ClientSession() as session: tasks = [alphafold_api_coroutine(sessi
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository mansanlab/alphafoldfetch appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Edgar Manriquez-Sandoval" 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 AlphaFoldFetch
Create a web-based mini-application named 'ProteinStructureExplorer' using Python and Flask framework. This application will leverage the AlphaFoldFetch package to fetch and display protein structures based on user input of either UniProt IDs or FASTA sequences. The application should have a simple yet intuitive UI, allowing users to enter their query and view the resulting protein structure in a visually appealing format. Additionally, the app should include features such as error handling for invalid inputs, caching mechanisms to avoid redundant downloads, and integration with a database to store and quickly retrieve previously fetched structures. Users should also be able to save their favorite structures for future reference. Your task is to outline the steps for developing this application, including setting up the environment, integrating AlphaFoldFetch, designing the frontend, implementing backend logic, and deploying the application. Provide specific instructions on how AlphaFoldFetch is utilized within the application, from fetching data to displaying it to the user.