abstractvision

v0.3.20 suspicious
6.0
Medium Risk

Model-agnostic generative vision abstractions (image/video) for the Abstract ecosystem

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows elevated risks in shell execution and credential handling, which could potentially be exploited for malicious purposes. However, there is no concrete evidence of malicious intent.

  • Elevated shell risk
  • High credential risk
Per-check LLM notes
  • Network: Network calls may be used for version checking or fetching metadata, but the use of external APIs and file downloads should be justified within the package's functionality.
  • Shell: Shell execution patterns can indicate legitimate operations like system checks or attribute management, but direct execution commands without clear purpose might pose a risk.
  • Obfuscation: The use of base64 decoding without validation may indicate obfuscation but could also be part of normal operations involving encoded data.
  • Credentials: The presence of getpass.getpass suggests potential handling of sensitive input such as passwords, indicating a higher risk for credential harvesting.
  • Metadata: The package has a non-secure external link and an author with limited information, suggesting potential risks but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • rn True try: with socket.create_connection((host, int(port)), timeout=0.5): return True
  • eleases/latest" req = urllib.request.Request( api_url, headers={
  • }, ) with urllib.request.urlopen(req, timeout=30) as r: meta = json.loads
  • cpp.zip" dl_req = urllib.request.Request(selected_url, headers={"User-Agent": "abstractvision
  • actvision"}) with urllib.request.urlopen(dl_req, timeout=60) as r, zip_path.open("wb") as f:
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • raw + ("=" * pad) return base64.b64decode(raw, validate=False) def _first_data_item(resp: Dict[str,
  • raw + ("=" * pad) return base64.b64decode(raw, validate=False) def _require_store(vm: VisionManager)
  • .mock import patch PNG_1X1 = base64.b64decode( "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQV
  • p4") self.assertEqual(base64.b64decode(seen["body"]["image_b64"]), b"image-bytes") self.ass
  • (0, str(SRC_DIR)) PNG_1X1 = base64.b64decode( "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQV
  • T}). " f"(python={__import__('sys').executable})" ) from e # AutoPipeline classes
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • out_path)] proc = subprocess.run(cmd, capture_output=True, text=True) if proc.ret
  • try: proc = subprocess.run( ["sysctl", "-n", "hw.memsize"],
  • try: subprocess.run( ["xattr", "-rd", "com.apple.quarantine"
  • ision. proc = subprocess.Popen( cmd, stdout=subproc
  • else: subprocess.run( cmd, check=True,
  • platform == "darwin": subprocess.run(["open", str(p)], check=False) return if sys.pla
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • try: entered = str(getpass.getpass(prompt)).strip() except (EOFError, KeyboardInterrupt):
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: abstractcore.ai>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8091/vision_playground.html`.
✓ Git Repository History

Repository lpalbou/abstractvision appears legitimate

⚠ 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 abstractvision 
Develop a creative mini-application that leverages the 'abstractvision' package to generate and manipulate visual content in real-time. Your application should serve as a user-friendly interface where users can input textual descriptions or select pre-defined templates, and the app will generate corresponding images or videos based on those inputs. The goal is to showcase the versatility and power of 'abstractvision' by allowing users to experiment with different styles, themes, and visual elements.

### Step-by-Step Guide:
1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary libraries such as 'abstractvision'. You may also need additional packages like Flask for the web interface or PyQt for a desktop application.
2. **Design User Interface**: Create an intuitive user interface where users can input their requests or select from a variety of predefined templates. This could be a simple form for text inputs or a dropdown menu for template selection.
3. **Implement Core Functionality**: Utilize 'abstractvision' to process the user inputs and generate the requested visual content. Explore the documentation to understand how to pass parameters for style, theme, and other visual adjustments.
4. **Integrate Real-Time Preview**: Implement a feature that allows users to see a real-time preview of the generated content as they adjust settings or input new data.
5. **Save and Share Options**: Provide options for users to save their generated content locally or share it via social media platforms.
6. **Testing and Feedback**: Test your application thoroughly and gather feedback from users to refine the experience and functionality.

### Suggested Features:
- **Text-to-Image Generation**: Allow users to describe an image in text and generate an image that matches their description.
- **Video Creation**: Extend the application to support video generation based on user inputs or selected themes.
- **Style Customization**: Offer various styles and customization options so users can tweak the output to fit their preferences.
- **Template Library**: Include a library of pre-defined templates for quick and easy content creation.
- **Collaboration Tools**: Enable users to collaborate on projects, sharing and editing visual content together in real-time.

### Utilizing 'abstractvision':
'abstractvision' provides a model-agnostic approach to generating and manipulating visual content, making it ideal for this project. You'll use its functions to parse user inputs, generate images/videos, and apply various transformations. Refer to the official documentation for specific APIs and examples that can guide you through integrating these functionalities into your application.