aksu

v1.1.0a0 suspicious
6.0
Medium Risk

Neural Turkish Morphological Atomizer

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to obfuscated code and lack of maintainer history, although there are no direct indications of malicious activities.

  • Obfuscation risk noted
  • Low maintainer activity and history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell execution detected, indicating no direct command execution risks.
  • Obfuscation: The code shows signs of obfuscation with unusual formatting and missing parts, but it doesn't appear to be extreme or indicative of malicious intent based on the provided snippets.
  • Credentials: No clear patterns of credential harvesting are detected in the given code snippets.
  • Metadata: The maintainer's lack of history and the repository's low activity suggest potential risk.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/melikkul/Aksu#readme
  • Detailed PyPI description (18269 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 227 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 71 commits in melikkul/Aksu
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • model_state_dict"]) model.eval() logger.info("Loaded model: %d params", model.count_par
  • (str(berturk_path)) model.eval() embeddings: list[np.ndarray] = [] batch_size = 32
  • end(bald) self.model.eval() return sum(bald_scores) / max(len(bald_scores), 1)
  • tate, strict=False) model.eval() return model def _load_ensemble(tag_vocab_size: int)
✓ 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 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 aksu 
Create a Python-based web application that leverages the 'akus' package to analyze Turkish text for educational purposes. Your application should allow users to input any Turkish sentence or paragraph and receive a detailed breakdown of each word into its morphological components. The app should display each word, its root form, and all suffixes applied, along with their meanings and grammatical roles. Additionally, implement features such as:

1. A user-friendly interface built using Flask or Django for the frontend.
2. An API endpoint that accepts POST requests with Turkish text and returns the morphological analysis in JSON format.
3. Integration with a database to save user queries and results for future reference.
4. Interactive visualizations showing the frequency of different grammatical forms in the analyzed text.
5. A feature that suggests alternative sentences using the same root words but with different suffixes to demonstrate how meaning changes.

Use the 'akus' package to process the Turkish text and extract morphological information. Ensure your application is well-documented and includes clear instructions on how to install dependencies and run the app.

💬 Discussion Feed

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