abmptools

v2.0.0 suspicious
6.0
Medium Risk

Pre/post-processing and analysis toolkit for ABINIT-MP Fragment Molecular Orbital calculations

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks due to its use of subprocess execution and eval(), which could lead to command injection and code execution. However, there is no evidence of direct malicious intent or credential harvesting.

  • High shell risk due to subprocess execution
  • High obfuscation risk due to use of eval()
Per-check LLM notes
  • Network: Network calls may be legitimate for fetching remote resources, but the lack of context suggests potential misuse.
  • Shell: Subprocess execution poses a significant risk if not properly sanitized, indicating potential for command injection or unintended system impact.
  • Obfuscation: The code uses eval() which can execute arbitrary code, indicating potential obfuscation or evasion techniques.
  • Credentials: No clear patterns of credential harvesting are present.
  • Metadata: The author's information is incomplete and they may be new or inactive, but no other red flags were found.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • bChem GET %s", url) req = urllib.request.Request(url, headers={"User-Agent": _DEFAULT_UA}) try:
  • LT_UA}) try: with urllib.request.urlopen(req, timeout=timeout) as resp: return re
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • # if type(eval(item2)) != list: # self.tgt1frag = [
  • self.tgt1frag = eval(item2) else: se
  • if type(eval(item2)) != list: # self.tgt1
  • self.tgt1frag = [eval(item2)] # else: #
  • self.tgt1frag = eval(item2) else: sel
  • # self.tgt2frag = [eval(item3)] else: se
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • ", str(sdf)] try: subprocess.run(cmd, check=True, capture_output=True, text=True) except
  • ) as inp_fh: result = subprocess.run( [packmol_bin], stdin=inp_fh,
  • : %s", cmd_str) result = subprocess.run( cmd_list, cwd=cwd, capture_output=c
✓ 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 kojioku/abmptools appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 abmptools 
Develop a mini-application called 'ABINIT-Analyzer' using the Python package 'abmptools'. This tool will serve as a user-friendly interface for researchers working with ABINIT-MP Fragment Molecular Orbital (FMO) calculations. The application should perform the following key functions:

1. **File Importation**: Allow users to import FMO calculation files generated by ABINIT-MP. Ensure that the application supports various file formats commonly used in FMO calculations.
2. **Pre-processing**: Implement basic pre-processing capabilities such as data cleaning, error checking, and normalization of input data to prepare it for further analysis.
3. **Analysis Tools**: Provide advanced analytical tools leveraging 'abmptools' functionalities to extract meaningful insights from the imported data. These could include molecular orbital energy analysis, interaction energy calculations, and visualization of molecular structures.
4. **Visualization**: Integrate visualization components to graphically represent the analyzed data. Users should be able to view molecular structures, energy levels, and other critical information in a visually appealing manner.
5. **Exporting Results**: Enable users to export their analyses and visualizations in various formats (e.g., CSV, PNG, PDF) for reporting or further research.
6. **User Interface**: Design a simple yet effective GUI using libraries like PyQt or Tkinter, ensuring the application is accessible and easy to use for non-expert users.
7. **Documentation & Help**: Include comprehensive documentation and a help section within the application to guide users through its features and usage.

To achieve these goals, you will need to utilize several key features of 'abmptools', including but not limited to:
- Reading and writing FMO calculation files
- Performing post-processing tasks like data filtering and transformation
- Conducting in-depth analysis of molecular orbitals and interactions
- Generating high-quality plots and graphs for visual representation of results

Your task is to outline the architecture of this application, detail the implementation steps for each feature, and discuss how 'abmptools' integrates into the workflow. Additionally, suggest any additional functionalities or improvements that could enhance the utility of 'ABINIT-Analyzer' for the scientific community.