abstractassistant

v0.4.8 suspicious
5.0
Medium Risk

A sleek (macOS) system tray application providing instant access to LLMs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential unreliability due to metadata issues but lacks clear indicators of malicious behavior.

  • Metadata risk due to non-HTTPS external link
  • Author information is incomplete
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting no attempt to steal secrets or credentials.
  • Metadata: The package contains a non-HTTPS external link and has an author with no name and only one package, suggesting potential unreliability.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • "application/json" req = urllib.request.Request(url, data=data, method=method.upper(), headers=h)
  • ders=h) try: with urllib.request.urlopen(req, timeout=timeout_s) as resp: raw = r
  • fg.auth_token)} req = urllib.request.Request(url, headers=headers, method="GET") timeout
  • try: with urllib.request.urlopen(req, timeout=timeout) as resp: parse
  • fg.auth_token)} req = urllib.request.Request(self._url("/api/gateway/attachments/upload"), data=b
  • try: with urllib.request.urlopen(req, timeout=timeout) as resp: raw =
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • '"' ) subprocess.run(["osascript", "-e", script], check=False)
  • eturn None return subprocess.Popen(["afplay", str(path)], stdout=subprocess.DEVNULL, stderr=sub
  • "paplay"): return subprocess.Popen(["paplay", str(path)], stdout=subprocess.DEVNULL, stderr=sub
  • ("aplay"): return subprocess.Popen(["aplay", str(path)], stdout=subprocess.DEVNULL, stderr=subp
  • "ffplay"): return subprocess.Popen( ["ffplay", "-nodisp", "-autoexit", str(path
  • "mpg123"): return subprocess.Popen(["mpg123", str(path)], stdout=subprocess.DEVNULL, stderr=sub
✓ Credential Harvesting

No credential harvesting patterns detected

✓ 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://127.0.0.1:8080
✓ Git Repository History

Repository lpalbou/abstractassistant 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 abstractassistant 
Create a sleek macOS system tray application named 'QuickQuery' using the Python package 'abstractassistant'. This application should provide users with instant access to Language Learning Models (LLMs) through a simple, intuitive interface. The application will serve as a quick reference tool for users to query information, receive translations, or perform basic language processing tasks without needing to open a web browser or another application.

### Core Features:
- **System Tray Icon:** Display a customizable icon in the macOS system tray. Users should be able to click on the icon to reveal a small menu or window.
- **Text Input Field:** Include a text input field where users can type their queries or commands.
- **Send Query Button:** Provide a button next to the input field to send the query to the LLM. Upon clicking, the application should display the response from the LLM either below the input field or in a separate output area.
- **Customizable Prompts:** Allow users to customize prompts or templates for common queries, such as 'Translate this sentence to Spanish', 'What is the capital of [country]', etc.
- **Settings Menu:** Implement a settings menu accessible via the system tray icon, allowing users to configure various options like preferred LLM, theme color, and custom prompts.
- **Notifications:** Integrate macOS notifications to alert users when a response is ready, especially if the user has the main window minimized.

### Additional Features (Optional):
- **History Log:** Keep a log of previous queries and responses within the application, allowing users to review past interactions.
- **Voice Input:** Incorporate voice recognition technology to allow users to dictate their queries instead of typing them.
- **Dark Mode Support:** Ensure the application supports dark mode, automatically adjusting based on the system settings.

### Utilization of 'abstractassistant':
- Use 'abstractassistant' to handle the integration with LLMs, managing connections, sending queries, and receiving responses efficiently.
- Leverage any built-in functionalities of 'abstractassistant' to enhance the user experience, such as caching responses, handling different types of LLMs, or optimizing performance.
- Customize the appearance and behavior of the 'abstractassistant' components to match the design and functionality of your 'QuickQuery' application.

### Development Steps:
1. Set up the development environment with Python and necessary libraries including 'abstractassistant'.
2. Design the UI for the system tray application, focusing on simplicity and ease of use.
3. Implement the core functionalities as described above, ensuring smooth interaction between the user interface and the LLMs via 'abstractassistant'.
4. Test the application thoroughly, paying special attention to error handling and responsiveness.
5. Add optional features based on user feedback and time availability.
6. Package and distribute the application for macOS users to enjoy.