AI Analysis
Final verdict: SAFE
The package acl-anthology v1.2.0 exhibits minimal risk indicators with no signs of malicious intent or obfuscation. It has legitimate network calls and no shell execution or credential harvesting risks.
- Low network, shell, obfuscation, and credential risks.
- Maintainer has only one package, indicating possible new or less active account.
Per-check LLM notes
- Network: The package makes legitimate network calls, likely for fetching resources or updates, which is not uncommon and does not inherently indicate malicious activity.
- Shell: No shell execution patterns detected, indicating a low risk of the package executing arbitrary commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious activities are flagged.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
lready downloaded r = requests.get(self.url, timeout=timeout) r.raise_for_status() # J
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: bollmann.me>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Marcel Bollmann" 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 acl-anthology
Your task is to develop a command-line tool using Python that allows users to search and retrieve information from the ACL Anthology database. This tool will utilize the 'acl-anthology' package to interact with the database efficiently. Below are the detailed steps and features you need to implement for this project: 1. **Setup Environment**: Ensure your development environment is set up correctly with Python and the 'acl-anthology' package installed. 2. **User Interface**: Design a simple and intuitive command-line interface where users can input their queries. 3. **Search Functionality**: Implement a search function that allows users to look up papers by title, author, year, or any other relevant metadata fields supported by the ACL Anthology. 4. **Retrieve Information**: Once a paper is found, provide options for users to view details such as abstracts, keywords, and citation information directly through the command line. 5. **Download Capability**: Allow users to download selected papers in PDF format if available, saving them to a specified directory on their local machine. 6. **Help/Usage Guide**: Include a help option that explains how to use each feature of the tool, including examples of valid search queries. 7. **Error Handling**: Ensure robust error handling to manage cases where no results are found or when there are issues downloading files. 8. **Customization Options**: Consider adding customization options like setting default directories for downloads or specifying preferred output formats. By completing these steps, you'll create a powerful yet user-friendly tool that leverages the capabilities of the 'acl-anthology' package to make research in natural language processing and computational linguistics more accessible.