🤖 AI Analysis

Final verdict: SAFE

The package appears safe with low risks across most categories. The slight increase in obfuscation risk due to base64 decoding does not significantly elevate the overall threat level.

Low network and shell execution risks
Potential obfuscation through base64 decoding
No evidence of credential harvesting

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution patterns detected, reducing risk of local system compromise.
Obfuscation: The use of base64 decoding might indicate an attempt to obfuscate code, but it could also be a legitimate practice for data encoding and decoding.
Credentials: No clear evidence of credential harvesting patterns detected.
Metadata: The author has only one package, suggesting a new or less active account which could indicate potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

try: data = base64.b64decode(data_str) return ImageData(data=data, mime_type=

✓ 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 1StepMore/Omni_Pre_Processor appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "OPP Contributors" 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 Omni-Pre-Processor

Create a document management system called 'DocMaster' using Python, which leverages the 'Omni-Pre-Processor' package for its core functionalities. DocMaster should be designed to handle various types of documents, extract relevant information from them, and store this data in a structured format for easy retrieval and analysis. The system should support multiple file formats including PDFs, Word documents, Excel spreadsheets, and plain text files. Additionally, it should offer features such as keyword search, document classification based on content, and the ability to generate summaries of the extracted data.

Step-by-step guide:
1. Setup your development environment with Python and install necessary packages including 'Omni-Pre-Processor'.
2. Design the user interface where users can upload their documents.
3. Integrate 'Omni-Pre-Processor' into your application to automatically process the uploaded documents, extracting key content like titles, dates, authors, tables, etc.
4. Implement a database to store the extracted metadata and full-text content.
5. Develop a search functionality allowing users to find specific documents or content within documents using keywords.
6. Add document classification capabilities using machine learning models trained on the extracted content.
7. Create a feature that generates concise summaries of each document, highlighting important points.
8. Ensure the application is user-friendly and efficient, providing quick access to stored information.
9. Test the application thoroughly with different types of documents to ensure robustness and accuracy of the extracted data.
10. Deploy the application either locally or on a cloud service for wider accessibility.