AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network, shell, obfuscation, and credential handling. However, the metadata risk score and the recent changes indicate some level of uncertainty.
- Low activity and new maintainer increase metadata risk.
- Further investigation into network calls is recommended.
Per-check LLM notes
- Network: The observed network calls appear to be standard API interactions and may be legitimate depending on the functionality of 'aris-sdk'. Further investigation into the SDK's intended use is recommended.
- Shell: No shell execution patterns were detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: Low activity and new maintainer suggest potential risk, but no clear malicious indicators.
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
5.0
Some documentation present
Detailed PyPI description (5889 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
29 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 100 commits in sid-stack/aris-registryTwo distinct contributors found
Heuristic Checks
Outbound Network Calls
score 9.0
Found 6 network call pattern(s)
try: resp = requests.get( f"{self.registry_url}/balance",try: resp = requests.get( f"{self.registry_url}/usage",. Discover resp = requests.get( f"{self.registry_url}/discover",) pay_resp = requests.post( f"{self.registry_url}/handshake",try: response = requests.post( f"{self.target_endpoint}/generate",try: response = requests.post( f"{self.target_endpoint}/chat",
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: aris.ai
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Sid" 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 aris-sdk
Create a decentralized machine learning model training platform using the 'aris-sdk' Python package. This platform will allow users to submit their datasets and specify machine learning models they wish to train. The system will then distribute the dataset across a network of nodes managed by the 'aris-sdk', each node training a portion of the data in parallel. Upon completion, the trained models from all nodes will be aggregated into a single, cohesive model that retains the benefits of decentralization while providing robust performance. Key Features: 1. User Interface: Develop a simple web-based interface where users can upload datasets and select from a variety of machine learning models (e.g., Linear Regression, Neural Networks). 2. Model Training: Utilize 'aris-sdk' to manage the decentralized network of nodes. Each node will receive a segment of the dataset and train its own instance of the selected model. 3. Aggregation Algorithm: Implement an algorithm to merge the trained models from all nodes into one final model. This could involve averaging weights, voting on predictions, or other methods depending on the model type. 4. Security and Privacy: Ensure that data remains private and secure during the training process by leveraging the cryptographic capabilities provided by 'aris-sdk'. 5. Performance Monitoring: Provide real-time monitoring of the training progress and resource usage across all nodes. 6. Results Delivery: Once the training is complete, deliver the final model back to the user along with performance metrics and visualizations. How to Use 'aris-sdk': - Initialize the network of nodes using 'aris-sdk' to ensure seamless communication and coordination between nodes. - Distribute the dataset and model specifications to each node via 'aris-sdk'. - Monitor the status of each node and handle any errors or issues that arise. - Aggregate the results from all nodes using 'aris-sdk' functions designed for merging and consolidating data.
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