AI Analysis
Final verdict: SUSPICIOUS
The package exhibits a notable network risk due to its external API calls, raising concerns about potential data exfiltration. However, other risks such as shell execution, obfuscation, and credential harvesting are minimal.
- High network risk
- Single package maintainer
Per-check LLM notes
- Network: Detected network calls to external APIs suggest potential data exfiltration or unauthorized communication.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account.
Package Quality Overall: Low (4.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/byeolki/ashi#readmeDetailed PyPI description (12076 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
56 type-annotated function signatures detected in source
○ Low
Multiple Contributors
2.0
Single-author or unverifiable project
1 unique contributor(s) across 12 commits in byeolki/ashiSingle author with few commits — possibly a personal or throwaway project
Heuristic Checks
Outbound Network Calls
score 9.0
Found 6 network call pattern(s)
eout: int) -> str: resp = requests.post( "https://api.openai.com/v1/chat/completions",try: resp = requests.post(self._url, json=payload, timeout=self._timeout)as f: resp = requests.post( self._url, data={"ptry: resp = requests.post( self._url, json={"attachmentry: resp = requests.post( f"{self._base}/sendMessage",as f: resp = requests.post( f"{self._base}/sendDocument",
Code Obfuscation
No obfuscation patterns detected
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
All external links appear legitimate
Git Repository History
Repository byeolki/ashi appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "byeolki" 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 ahsi
Create a mini-application called 'MultiLog' that leverages the 'ahsi' package to streamline logging of machine learning training events across multiple platforms. This application should allow users to configure which logging destinations they want to use and customize the format of the log messages. MultiLog should support real-time logging during training, ensuring that all specified platforms receive the same event information simultaneously. Additionally, the application should provide a simple command-line interface for setting up and managing the logging process. Here’s a detailed breakdown of the steps and features: 1. **Setup Environment**: Install the required packages including 'ahsi'. 2. **Configuration Management**: Develop a configuration file where users can specify which platforms they wish to use for logging (e.g., Discord, Slack, etc.) and customize the message format. 3. **Logging Functionality**: Implement the core functionality using 'ahsi' to send training events to the selected platforms in real-time. 4. **Command-Line Interface**: Create a CLI tool that allows users to start, stop, and manage the logging process easily. 5. **Custom Events**: Allow users to define custom events such as training start, end, epoch completion, etc., and map these events to specific actions or messages on the logging platforms. 6. **Testing & Validation**: Ensure that the application works correctly by testing it with different configurations and logging destinations. 7. **Documentation**: Provide comprehensive documentation detailing how to install, configure, and use MultiLog effectively.