AI Analysis
Final verdict: SUSPICIOUS
The package shows a high obfuscation risk due to the use of pickle.loads, which can be indicative of hidden code execution. However, there are no other significant risks detected.
- High obfuscation risk due to pickle.loads usage
- No other major risks detected
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities.
- Obfuscation: The use of pickle.loads on read bytes may indicate an attempt to hide code execution, which is suspicious.
- Credentials: No clear patterns indicating credential harvesting were detected.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
fname).read_bytes() obj = pickle.loads(raw) if not isinstance(obj, tuple) or len(obj) < 2:
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 MuteJester/GenAIRR appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Ayelet Peres" 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 GenAIRR
Develop a comprehensive mini-application that leverages the GenAIRR package to simulate synthetic immune-receptor sequences for research purposes. This application will serve as a benchmarking tool for evaluating the performance of various alignment models and sequence analysis methods. Here’s a step-by-step guide on how to proceed: 1. **Project Setup**: Begin by setting up a Python environment and installing necessary packages including GenAIRR, numpy, pandas, matplotlib, and seaborn. 2. **Application Design**: Design the application to include a user-friendly interface where users can input parameters such as sequence length, number of sequences, and types of mutations. 3. **Simulation Engine**: Utilize GenAIRR to generate synthetic immune-receptor sequences based on user inputs. Ensure the simulation engine supports different types of immune receptors (e.g., T-cell receptors, B-cell receptors). 4. **Sequence Analysis Tools**: Integrate tools within the application to analyze the simulated sequences. These tools should include basic statistics, frequency analysis, and mutation rates. 5. **Alignment Benchmarking**: Implement a feature that allows users to upload their own alignment models and test them against the generated sequences. Provide metrics such as accuracy, speed, and sensitivity. 6. **Visualization**: Use matplotlib and seaborn to create visual representations of the analysis results, including histograms, scatter plots, and heatmaps. 7. **Report Generation**: Enable the application to generate detailed reports summarizing the simulation and analysis outcomes, which can be exported in PDF or Excel formats. 8. **Documentation & User Guide**: Prepare comprehensive documentation and a user guide to help researchers understand how to use the application effectively. This project aims to provide a robust platform for researchers and developers to evaluate and improve their sequence analysis techniques using realistic yet controllable synthetic data.