AI Analysis
Final verdict: SUSPICIOUS
The package shows signs of potential code injection due to the use of eval and exec, despite having low risks in other categories such as network and shell execution.
- High obfuscation risk due to eval and exec usage
- Lack of maintainer information and GitHub repository
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell executions detected, indicating the package does not perform system-level operations.
- Obfuscation: The use of eval and exec with compiled code suggests potential for code injection and obfuscation, increasing the risk of malicious activity.
- Credentials: No clear patterns indicating credential harvesting were detected.
- Metadata: The package lacks a maintainer's name and has no associated GitHub repository, which raises some suspicion but does not conclusively indicate malicious activity.
Package Quality Overall: Low (3.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (6331 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
268 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 6.0
Found 3 obfuscation pattern(s)
result = bool(eval(self._compiled_eval, namespace)) if result:self._compiled_code = compile(self._python_code, "<PythonTrainingAction>", "exec") def _get_state_data(self, phase: TrainingPhaseProtocself._compiled_exec = compile(self._python_code, "<PythonPredicate>", "exec") self._use_exec = True def evaluate(self,
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
No GitHub repository linked
No GitHub repository link found
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 auto-trainer-training
Create a mini-application named 'MouseAutotrainer' using the Python package 'auto-trainer-training'. This application will simulate a training environment for a mouse in a simplified version of the GYM framework, where the mouse learns to navigate through a maze to reach a reward. The goal is to demonstrate the training protocol objects and implementations provided by the 'auto-trainer-training' package. Step 1: Set up the environment - Install necessary packages including 'auto-trainer-training' - Create a virtual environment for Python Step 2: Define the Maze Environment - Use 'auto-trainer-training' to define the maze layout - Implement a simple 2D grid-based maze with obstacles and a goal Step 3: Implement the Training Protocol - Utilize 'auto-trainer-training' to set up a training session for the mouse - Define the rewards and penalties system - Implement different levels of difficulty for the maze Step 4: Train the Mouse - Integrate reinforcement learning algorithms from 'auto-trainer-training' - Simulate multiple training sessions to observe learning progress - Analyze performance metrics such as time taken to complete the maze and success rate Suggested Features: - User interface to visualize the maze and the mouse's path - Adjustable parameters for maze complexity and training settings - Save and load training sessions - Visualize training progress over time The application should demonstrate the ability of the mouse to learn and improve its navigation skills through repeated training sessions, showcasing the effectiveness of the training protocols implemented with 'auto-trainer-training'.
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