AI Analysis
Final verdict: SUSPICIOUS
The package shows some signs of potential risk, particularly due to its use of external script execution and ambiguous metadata. However, it does not exhibit clear indicators of malicious activity.
- High shell risk due to Rscript execution
- Ambiguous metadata and author details
Per-check LLM notes
- Network: The network call appears to be an HTTP request to a specific base URL, which could be legitimate if the package interacts with an API like OpenAlex.
- Shell: Executing external scripts via Rscript can pose risks if not properly controlled and vetted, suggesting potential for unintended behavior or execution of arbitrary code.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The presence of a non-HTTPS link and an author with minimal information suggests potential risks, but there's no clear indication of malicious intent.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
lse {} self._client = httpx.AsyncClient( base_url=OPENALEX_BASE, timeout=sel
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
) try: proc = subprocess.run( ["Rscript", "--vanilla", str(_STATCHECK_R_SCRIP
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: albertogerli.it>
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://127.0.0.1:8000/
Git Repository History
Repository albertogerli/Agentic_Paper appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 agentic-paper
Develop a peer review management system using the 'agentic-paper' Python package. This system will streamline the process of submitting papers, assigning reviewers, and managing feedback in an academic setting. Here are the key functionalities your application should include: 1. **Paper Submission**: Users should be able to submit their research papers through a simple web interface. Each submission should include basic metadata like title, authors, abstract, and keywords. 2. **Reviewer Assignment**: Based on the paper's topic and keywords, the system should automatically suggest potential reviewers from a predefined list of experts. Reviewers should receive notifications via email when assigned to a new paper. 3. **Feedback Management**: Once a reviewer has been assigned, they should be able to provide structured feedback on the paper through the same web interface. Feedback should be categorized into sections such as 'Introduction', 'Methodology', 'Results', and 'Conclusion'. 4. **Decision Making**: After receiving feedback from all assigned reviewers, the system should generate a summary report and recommendations for the author, including whether the paper should be accepted, revised, or rejected. 5. **Communication Tools**: Provide tools for direct communication between authors and reviewers, allowing them to discuss specific points or clarify doubts. 6. **Analytics Dashboard**: Offer an analytics dashboard for administrators to track the progress of each paper, view overall statistics on submissions and reviews, and identify bottlenecks in the review process. Utilize the 'agentic-paper' package to handle the orchestration of multiple agents involved in the peer review process. This includes automating the assignment of reviewers based on expertise, coordinating feedback collection, and summarizing the outcomes. Ensure that the system is user-friendly and secure, protecting the privacy of all parties involved.