acatrack-pdf-parser-rs

v0.1.1 suspicious
4.0
Medium Risk

High-performance, native parallel Rust PDF parser engine for VTU provisional results

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network, shell, obfuscation, and credential risks. However, the metadata risk score is notably high due to suspicious commit history and lack of maintainer details, raising concerns about potential supply-chain attacks.

  • High metadata risk score
  • Lack of maintainer details
  • Rapid commit history
Per-check LLM notes
  • Network: No network calls suggest the package is not designed to communicate externally.
  • Shell: No shell executions indicate that the package does not perform any system-level command operations.
  • Obfuscation: No obfuscation patterns detected, suggesting legitimate use without malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of sensitive information.
  • Metadata: Suspicious activity includes rapid commit history and lack of maintainer details.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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 score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • All 9 commits happened within 24 hours
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 acatrack-pdf-parser-rs 
Create a Python-based utility named 'VTUResultParser' that leverages the 'acatrack-pdf-parser-rs' package to extract and process VTU (Visvesvaraya Technological University) provisional results from PDF files. This utility will serve as a time-saving tool for students and faculty who need to quickly analyze their results without manually navigating through each page of a PDF document.

### Key Features:
1. **PDF Parsing**: Utilize 'acatrack-pdf-parser-rs' to efficiently parse PDF files containing VTU provisional results. Ensure the parsing is robust enough to handle different formatting styles within the PDFs.
2. **Data Extraction**: Extract relevant student information such as roll number, name, and marks from parsed data. Store these details in a structured format like a dictionary or a list of dictionaries for easy access.
3. **Analysis Tools**: Implement basic analysis tools such as calculating total marks, percentage, and grade based on the extracted marks.
4. **User Interface**: Develop a simple command-line interface (CLI) where users can input the path to their PDF file and receive the parsed and analyzed results.
5. **Error Handling**: Add comprehensive error handling to manage issues like invalid file paths, unsupported PDF formats, or missing data fields.
6. **Output Options**: Allow users to choose between displaying the results directly in the CLI or exporting them to a CSV file for further processing.

### Steps to Build the Utility:
1. **Setup Environment**: Begin by setting up your Python environment and installing necessary packages including 'acatrack-pdf-parser-rs'. Ensure you have the required dependencies installed and configured correctly.
2. **PDF Parsing Functionality**: Write functions to interact with 'acatrack-pdf-parser-rs', passing in the path to a PDF file and returning structured data containing student results.
3. **Data Processing**: Create classes or functions to process the extracted data. This includes cleaning any raw data, performing calculations, and storing results in a usable format.
4. **CLI Development**: Use Python's built-in modules to create a user-friendly CLI. Design prompts that guide users through the process of selecting PDF files and viewing results.
5. **Testing & Validation**: Test your utility thoroughly using various PDF files containing VTU provisional results. Validate the accuracy of data extraction and analysis against known values.
6. **Enhancements & Documentation**: Once the base functionality is complete, consider adding more advanced features or improving existing ones based on user feedback. Document your code and provide instructions for other developers to contribute or use your utility.