🤖 AI Analysis

Final verdict: SUSPICIOUS

The package MONSDA v1.4.0 has a moderate risk score due to its potential for shell execution which can be exploited for malicious activities. However, other risks are minimal.

Shell execution capability detected
Low maintenance and effort observed in metadata

Per-check LLM notes

Network: No network calls detected, indicating no direct data exfiltration or C2 communication observed.
Shell: Shell execution is present and could potentially be used to execute arbitrary commands, suggesting a higher risk of malicious activity.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some signs of low maintenance and effort, but there's no clear evidence of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

S')}.json", ) os.system(f"cp {file} {bakfile}") if project.subname: con
f ".gz" in fcountgtf: os.system( "zcat " + out_file + "|
f ) else: os.system( "sed s/aggregate_gene/gene/g " + ou
) # return subprocess.run(jobtorun, shell=True, universal_newlines=True, capture_outpu
# python >= 3.7 job = subprocess.Popen( run_cmd, shell=True, un
o -O \"partition\"" res = subprocess.run(cmd, check=True, shell=True, stdout

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://bioconda.github.io/recipes/monsda/README.html

✓ Git Repository History

Repository jfallmann/MONSDA appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Joerg Fallmann" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with MONSDA

Create a small bioinformatics data analysis tool using the MONSDA package. Your application will be designed to streamline the process of analyzing high-throughput sequencing (HTS) data using either Nextflow or Snakemake workflows. Here’s a detailed plan on how to build this application:

1. **Project Setup**: Start by setting up your Python environment. Ensure you have MONSDA installed along with other necessary dependencies such as Nextflow or Snakemake.
2. **Data Ingestion**: Develop a feature within the application that allows users to upload HTS datasets. This could include options for direct file uploads or specifying paths to directories containing the datasets.
3. **Workflow Selection**: Provide users with the option to choose between Nextflow and Snakemake as their workflow engine. This choice should dictate the subsequent steps in the analysis pipeline.
4. **Pipeline Configuration**: Implement a configuration module where users can customize parameters specific to their chosen workflow engine. This might include specifying the type of analysis (e.g., RNA-seq, ChIP-seq), selecting tools, and setting up computational resources.
5. **Execution & Monitoring**: Use MONSDA to manage the execution of the selected workflow. The application should provide real-time monitoring capabilities so users can track the progress of their analysis.
6. **Results Presentation**: Once the analysis completes, the application should present the results in a user-friendly manner. This could involve generating summary statistics, visualizations, and links to raw output files.
7. **Documentation & Support**: Include comprehensive documentation that guides users through each step of the process. Additionally, provide a support section where users can report issues or seek help.

**Utilization of MONSDA**: Throughout the development process, leverage MONSDA’s core functionalities to organize, manage, and optimize the HTS data analysis workflows. Specifically, use MONSDA to handle the modular organization of different components of the analysis pipeline, ensuring flexibility and scalability.