🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential network and shell execution activities, which require closer scrutiny to ensure they do not pose a threat.

Network calls suggest external data retrieval
Indications of shell command execution

Per-check LLM notes

Network: The network calls suggest external data retrieval and potentially user IP information gathering, which could be legitimate but requires further investigation into the package's purpose.
Shell: The shell execution patterns indicate the package may execute external commands, which is not inherently malicious but could pose risks if commands are not properly sanitized or controlled.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Metadata: The maintainer has only one package, indicating a potentially new or less active account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

try: urllib.request.urlretrieve(remote_filepath, local_filepath)
sh): try: urllib.request.urlretrieve(remote_filepath, local_filepath) obs
lit("_")) response = requests.post(url, json=type(self).run_stats, headers=headers) if
y: response = requests.get(f"https://ipinfo.io/{ip_address}/json") if r
url {url}") res = requests.get(url, headers=self.request_header).json() valid_a
) request = requests.get(self.request_url, headers=self._headers) if not

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

self.result = subprocess.run(self.cmd, capture_output) except subprocess.Call
_output: result = subprocess.run(cmd, capture_output=True, text=True, check=True)
err else: subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL) retu
# run codeml subprocess.run( args=[codeml_cmd], cwd=work_dir,
onf.write(codon_data) subprocess.run( [yn00_cmd], cwd=workdir,
EP sequences try: subprocess.run( cmd_cds, check=True, capture_output=True, text=

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "hyli360" 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 GeneVue

Create a web-based gene expression analysis tool using the Python package 'GeneVue'. This tool will allow users to upload their gene expression data in CSV format, visualize it through interactive plots, and perform basic statistical analyses such as differential expression analysis between two conditions. Here’s a detailed breakdown of the steps and features you need to implement:

1. **Setup Environment**: Begin by setting up a Python environment with necessary packages including GeneVue, Flask for web development, pandas for data manipulation, and Matplotlib for plotting.
2. **User Interface**: Develop a simple yet intuitive user interface using HTML/CSS/JavaScript that allows users to upload their gene expression data files. Ensure the UI provides clear instructions and feedback messages.
3. **Data Upload and Processing**: Implement functionality within your application to accept CSV file uploads containing gene expression data. Use pandas to read and process the uploaded data, ensuring it is cleaned and formatted correctly for further analysis.
4. **Visualization with GeneVue**: Utilize GeneVue to generate interactive visualizations of the gene expression data. Users should be able to select different genes and conditions to view their expression patterns over time or across different samples.
5. **Statistical Analysis**: Integrate functions that perform differential expression analysis between specified conditions in the dataset. Provide options for users to select conditions and view results both numerically and visually.
6. **Results Display**: Design a section in your web app where users can view the results of their analyses, including any plots generated by GeneVue and statistical test outcomes.
7. **Documentation and Testing**: Write comprehensive documentation detailing how to use the tool and what each feature does. Also, ensure thorough testing of all functionalities to guarantee reliability and accuracy.

This project aims to provide researchers and students with an accessible way to analyze and understand complex gene expression datasets without requiring extensive programming knowledge.