AI Analysis
Final verdict: SUSPICIOUS
The package shows medium risk due to network and shell execution vulnerabilities, despite having no signs of obfuscation or credential theft.
- High network risk due to potential data exfiltration.
- High shell risk due to possible arbitrary command execution.
Per-check LLM notes
- Network: The use of urllib to download content suggests potential for data exfiltration or updating malicious code.
- Shell: Subprocess execution can be leveraged for arbitrary command execution, posing significant security risks.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository is not found and the maintainer has a single package, which may indicate a new or less active account.
Package Quality Overall: Low (4.2/10)
◈ 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 (226919 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
Classifier: Typing :: Typed
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
th(tmp.name) with urllib.request.urlopen(url) as resp: tmp.write(resp.read())
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
rt subprocess return subprocess.run(args, check=False).returncode # POSIX: replace the proce
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
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Bemarking AI S.A.S." 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 axon-lang
Create a fully-functional mini-application that leverages the 'axon-lang' package to manage and execute simple cognitive tasks. This application will serve as a basic task manager where users can define tasks using the AXON language, schedule them, and monitor their completion status. ### Features: - **Task Definition**: Users can write tasks in AXON, specifying conditions, actions, and constraints. - **Task Scheduling**: Tasks can be scheduled for execution at specific times or intervals. - **Task Monitoring**: A dashboard to view the status of tasks (queued, running, completed). - **Error Handling**: Graceful handling of errors during task execution. - **User Interface**: A simple command-line interface (CLI) for user interaction. ### Steps to Build the Application: 1. **Setup Environment**: Ensure 'axon-lang' is installed (`pip install axon-lang`). 2. **Define Task Model**: Create a model to represent tasks, including attributes like name, description, schedule, and status. 3. **Task Parsing**: Implement functionality to parse AXON scripts into executable tasks using 'axon-lang'. This involves calling the appropriate 'axon-lang' commands from within your application. 4. **Scheduling Mechanism**: Develop a scheduling system to handle when tasks should be executed based on user-defined schedules. 5. **Execution Loop**: Write a loop or background process that periodically checks the schedule and executes tasks accordingly. 6. **Status Updates**: Keep track of each task's status (queued, running, completed) and update the dashboard accordingly. 7. **CLI Interface**: Design a CLI that allows users to add new tasks, view the current schedule, and see the status of all tasks. 8. **Testing**: Thoroughly test the application to ensure all components work as expected. 9. **Documentation**: Provide clear documentation on how to use the application and any limitations of the current implementation. ### Utilization of 'axon-lang': - Use 'axon-lang' to compile and execute AXON scripts provided by the user. For example, calling `axon check script.axon` to validate the syntax of a given task definition before execution. - Integrate 'axon-lang' to dynamically generate and run tasks based on user input, ensuring the application can interpret complex logical conditions and actions specified in AXON. This project will demonstrate the power and flexibility of 'axon-lang' in managing complex cognitive tasks within a real-world application.
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