SceneVLM

v0.0.1 suspicious
4.0
Medium Risk

Vision-language model tools for SceneAPI

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of direct malicious activity but has high metadata risk due to its recent creation and lack of community engagement. This combination suggests caution.

  • High metadata risk due to recent creation and minimal activity
  • No detected malicious activities such as network calls or obfuscation
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's recent creation, lack of community engagement, single commit, and new maintainer suggest potential risk.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 10.0

Git history flags: Repository created very recently: 5 day(s) ago (2026-06-01T05:15:03Z)

  • Repository created very recently: 5 day(s) ago (2026-06-01T05:15:03Z)
  • Repository has zero stars and zero forks
  • Very few commits: 1 total
  • Single contributor with only 1 commit(s) — possibly throwaway account
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "SceneAPI" 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 SceneVLM 
Create a visually engaging and interactive mini-application using the Python package 'SceneVLM'. This application will serve as a user-friendly interface where users can upload images and receive detailed scene descriptions along with relevant objects identified within those scenes. The goal is to showcase the capabilities of SceneVLM in understanding and describing complex visual content through natural language interaction.

Step 1: Set up your development environment. Ensure you have Python installed, then install SceneVLM and any other necessary libraries like Flask for web application development.

Step 2: Design the user interface. It should allow users to upload an image file from their device. Once uploaded, display the image on the page and provide buttons for actions such as 'Analyze Image' and 'Clear Image'.

Step 3: Implement the backend functionality using SceneVLM. When the user clicks 'Analyze Image', use SceneVLM to process the uploaded image and generate a description of the scene, including recognized objects and their relationships within the image.

Step 4: Display the analysis results back to the user in a readable format. This could include a textual description of the scene, highlighted objects, and even suggestions based on the context (e.g., 'It looks like a sunny day; perhaps you were at a beach?').

Suggested Features:
- User authentication for saving and retrieving previous analyses.
- Integration with social media platforms to share analyzed images directly.
- A gallery feature showcasing various types of scenes and their analyses.
- Voice-to-text functionality allowing users to describe images verbally instead of uploading them.

How to Utilize SceneVLM:
- Use SceneVLM's API endpoints for processing images and generating scene descriptions.
- Leverage SceneVLM's ability to understand and describe complex visual scenes in natural language to enhance user engagement and interaction within the application.