Sujit-Tokenizer

v1.0.0 suspicious
4.0
Medium Risk

Custom Byte-Level BPE Tokenizer built from scratch in Python

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks associated with network, shell execution, obfuscation, and credential handling. However, the metadata risk score is elevated due to the repository being new with little to no activity, which raises suspicion about its authenticity and trustworthiness.

  • Repository is new with no prior activity
  • Low trustworthiness due to lack of community engagement or history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository is new with no activity indicators, suggesting low trustworthiness but not necessarily malicious intent.

🔬 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 created very recently: 7 day(s) ago (2026-05-30T07:51:55Z)

  • Repository created very recently: 7 day(s) ago (2026-05-30T07:51:55Z)
  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Sujit Maity" 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 Sujit-Tokenizer 
Your task is to develop a mini-application named 'ByteBuddy' which will serve as a simple yet powerful tool for processing and tokenizing text data using the 'Sujit-Tokenizer' package. This application aims to demonstrate the capabilities of the Sujit-Tokenizer in handling various types of text data, including but not limited to English, Spanish, and French languages.

### Core Features:
1. **Text Input**: Users should be able to input text through a command-line interface or a simple GUI. The application will accept text data from the user and process it accordingly.
2. **Tokenization**: Utilize the Sujit-Tokenizer to tokenize the input text into its byte-level components. Display the tokenized output to the user.
3. **Language Detection**: Implement a feature to automatically detect the language of the input text. If the detected language is supported, display a message indicating the detected language.
4. **Token Statistics**: Provide statistics about the tokenization process such as total number of tokens, average token length, etc.
5. **Visualization**: For educational purposes, create a simple visualization (e.g., bar chart) showing the frequency distribution of tokens.
6. **Export Option**: Allow users to export the tokenized text and statistics to a CSV file for further analysis.

### Steps to Build the Application:
1. **Setup Environment**: Ensure your development environment is set up with Python and install necessary packages including Sujit-Tokenizer.
2. **Input Handling**: Design the UI/UX for inputting text data. Consider both CLI and GUI options for user interaction.
3. **Integration with Sujit-Tokenizer**: Integrate the Sujit-Tokenizer into your application. Use its functions to tokenize the input text.
4. **Language Detection**: Research and implement a method for detecting the language of the input text. This could involve using an external library like langdetect or implementing a basic solution based on common words in different languages.
5. **Data Analysis**: Calculate and display statistics related to the tokenization process. Ensure these metrics provide valuable insights into the structure of the input text.
6. **Visualization**: Create a simple visualization using matplotlib or any other plotting library to show the frequency distribution of tokens.
7. **Export Functionality**: Implement functionality to save the tokenized text and statistics into a CSV file.
8. **Testing and Validation**: Test your application thoroughly to ensure all features work as expected. Validate the accuracy of tokenization and language detection.
9. **Documentation**: Write clear documentation explaining how to use your application, including setup instructions and usage examples.

This project will not only showcase the power of Sujit-Tokenizer but also provide a practical tool for anyone interested in exploring text data at a byte-level.