aipass

v2.5.0 suspicious
6.0
Medium Risk

A local multi-agent framework where your AI agents keep their memory, work together, and never ask you to re-explain context

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of unusual obfuscation and potential shell command execution, raising concerns about its legitimacy and intent.

  • Unusual obfuscation patterns
  • Potential shell command execution
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network interaction.
  • Shell: The shell execution patterns suggest the package may be invoking external commands or scripts, which could indicate legitimate functionality but also potential risks like executing arbitrary code.
  • Obfuscation: The obfuscation pattern is unusual and may indicate an attempt to hide code execution paths, but it could also be used for legitimate purposes like managing imports.
  • Credentials: No clear patterns of credential harvesting detected.
  • Metadata: The author's information is incomplete, suggesting potential unreliability.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. hook_test.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (13324 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

  • 161 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in AIOSAI/AIPass
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • sys.path.insert(0, str(__import__('pathlib').Path(__file__).resolve().parent)) from hook_log imp
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • """ try: result = subprocess.run( ["claude", "-p", prompt, "--model", model],
  • ra) try: result = subprocess.run( ["python3", str(script_path)], inpu
  • s.executable result = subprocess.run( [system_python, "-c", _DBUS_SCRIPT, source, ico
  • ify-send.""" try: subprocess.run(["notify-send", "-i", icon, title, body], capture_output=Tru
  • e try: process = subprocess.Popen( monitor_cmd, stdout=subprocess.DEVN
  • e() try: result = subprocess.run(["pgrep", "-x", "claude"], capture_output=True, text=True, t
✓ 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

Repository AIOSAI/AIPass 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 aipass 
Develop a mini-app called 'SmartTaskMaster' using the Python package 'aipass'. This application will help users manage their daily tasks more efficiently by leveraging a local multi-agent system that maintains its own memory and collaborates seamlessly without needing repeated user input for context. Here’s how it works:

1. **Task Creation**: Users can create tasks with various attributes such as priority, due date, and associated notes.
2. **Agent Interaction**: The app includes multiple AI agents designed to handle different aspects of task management. For example, one agent focuses on sorting tasks by priority, another tracks deadlines, and a third suggests optimal times for task execution based on user availability and historical data.
3. **Memory Retention**: Each agent retains its own memory, allowing it to remember past interactions and decisions, improving its performance over time without the need for repetitive instructions from the user.
4. **Collaborative Planning**: Agents work together to plan out the day/week optimally, considering factors like task importance, deadlines, and user preferences.
5. **User Interface**: Provide a simple, intuitive UI where users can view their planned tasks, modify them, and receive recommendations from the agents.
6. **Feedback Loop**: Implement a mechanism for users to provide feedback on the effectiveness of the suggested plans, which helps refine the agents’ decision-making processes.
7. **Customization Options**: Allow users to customize the behavior of individual agents according to their specific needs or preferences.

**How 'aipass' is Utilized**: 
The 'aipass' package is crucial in enabling the creation and management of these intelligent agents within a local environment. It facilitates the agents' ability to maintain their own memories, collaborate effectively, and operate independently of constant user intervention. Use 'aipass' to define the agents, set up their communication protocols, and manage their interactions to ensure smooth operation of the 'SmartTaskMaster' application.