AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate risks due to its external network calls and obfuscated code practices, raising concerns about potential unauthorized data transmission and code injection vulnerabilities.
- network risk
- obfuscation risk
Per-check LLM notes
- Network: The observed network calls suggest the package communicates externally, which could be legitimate but warrants further investigation to ensure it's not engaging in unauthorized data transmission.
- Shell: No shell execution patterns detected, suggesting low risk of direct system command execution.
- Obfuscation: The use of eval and dynamic imports suggests potential code injection risks.
- Credentials: No clear patterns indicative of credential harvesting were detected.
- Metadata: Suspicious non-HTTPS link and single contributor with few commits indicate potential risk.
Heuristic Checks
Outbound Network Calls
score 9.0
Found 6 network call pattern(s)
, } request = urllib.request.Request(url=url, data=data, headers=headers, method="POST")est_with_retry(self, request: urllib.request.Request) -> Dict[str, Any]: """ Send a non-stry: with urllib.request.urlopen(request, timeout=self.timeout) as response:stream_request(self, request: urllib.request.Request) -> Iterator[Dict[str, Any]]: """ Sttry: response = urllib.request.urlopen(request, timeout=self.timeout) except urllib, } request = urllib.request.Request(url=url, headers=headers, method="GET") try:
Code Obfuscation
score 4.0
Found 2 obfuscation pattern(s)
y: widget_type = eval(settings['type']) widget = window.findChild(widultralytics_module = __import__(module_name) switch_widget = "Main" if isinstance(windo
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
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://127.0.0.1:8000/v1
Git Repository History
score 2.5
Git history flags: Single contributor with only 4 commit(s) — possibly throwaway account
Single contributor with only 4 commit(s) — possibly throwaway account
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 VibeFlux
Create a mini-application called 'PhotoEnhancer' using the VibeFlux Python package. This application will allow users to enhance their photos with various effects, such as brightness adjustment, contrast enhancement, and applying filters. Additionally, it should include a feature to detect faces in the photo and highlight them with bounding boxes. Users should be able to save their enhanced photos directly from the application. Steps: 1. Set up the basic structure of the application using VibeFlux's pre-built UI widgets and PySide6. 2. Implement a file upload functionality where users can select an image from their local storage. 3. Use OpenCV and Pillow to process the uploaded image and apply the selected enhancements. 4. Integrate the lightweight OpenAI-compatible LLM client to suggest enhancement options based on the image content. 5. Implement face detection using OpenCV and highlight detected faces with bounding boxes. 6. Allow users to preview the changes before saving the enhanced image locally. 7. Utilize VibeFlux's SQLite helper to log user actions and preferences for future reference. Features: - Upload and display images from local storage. - Adjust brightness and contrast levels. - Apply filters like grayscale, sepia, and vintage. - Detect and highlight faces within the image. - Preview changes before saving. - Log user interactions and preferences using SQLite.