AI Analysis
Final verdict: SUSPICIOUS
The package exhibits signs of obfuscation which may indicate an attempt to conceal functionality, however, there are no clear signs of malicious activity such as network or shell risks. Given the low activity level of the maintainer's account, further scrutiny is advised.
- Obfuscation risk at 7/10
- Single package from a less active maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating the package does not execute external commands.
- Obfuscation: The observed patterns suggest potential obfuscation of code, possibly to hide functionality or make reverse engineering more difficult.
- Credentials: No clear indicators of credential harvesting or secret handling were found in the provided snippets.
- Metadata: The maintainer has only one package, indicating a new or less active account which could be suspicious but lacks other red flags.
Package Quality Overall: Low (4.8/10)
✦ High
Test Suite
9.0
Test suite present — 9 test file(s) found
9 test file(s) detected (e.g. test_circuits_3arch.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (13901 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
67 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 10.0
Found 6 obfuscation pattern(s)
type=torch.float32) model.eval() def tokenize_fn(texts): return tok(texts, rettype=torch.float32) model.eval() backend = Backend.for_model(model, hint=arch) # Amodel = model.to(device).eval() backend = Backend.for_model(model, hint=arch) retpt.step() self.probe.eval() with torch.no_grad(): train_logits = sscores.""" self.probe.eval() with torch.no_grad(): return torch.sigdtype=torch.float32); pythia.eval() mamba_tok = AutoTokenizer.from_pretrained(MAMBA_NAME)
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 2.0
1 maintainer concern(s) found
Author "Juan Cruz Dovzak" 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 archscope
Create a mini-application that leverages the 'archscope' Python package to perform mechanistic interpretability experiments on small machine learning models across different architectures. This application should allow users to upload their own small model (e.g., a simple neural network), specify the target architecture(s) for the experiment (e.g., CPU vs GPU), and run interpretability analyses to understand how the model makes predictions. Key Features: 1. Model Upload: Users should be able to upload a pre-trained small model (e.g., a simple neural network). 2. Architecture Selection: Provide options for selecting different hardware architectures (e.g., CPU, GPU) for running the model. 3. Interpretability Analysis: Implement various interpretability techniques provided by 'archscope', such as layer-wise relevance propagation (LRP) or activation analysis, to help users understand the model's decision-making process. 4. Visualization: Display the results of the interpretability analysis in a user-friendly manner, including heatmaps or bar charts to highlight important features or layers. 5. Report Generation: Automatically generate a report summarizing the findings from the interpretability analysis, which can be downloaded by the user. Utilization of 'archscope': - Use 'archscope' to set up the environment for cross-architecture experiments. - Leverage 'archscope' functions to perform the selected interpretability analysis on the uploaded model. - Integrate 'archscope' visualization tools into the application for displaying analysis results. Your task is to design and implement this application using Python, ensuring it is user-friendly and efficient. The application should showcase the power of 'archscope' in providing insights into small model architectures.
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