🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows significant risks related to shell execution and network calls, indicating potential vulnerabilities or malicious activities. While there is no strong evidence of credential theft, the overall pattern warrants further investigation.

High risk associated with shell execution
Potential data exfiltration via network calls

Per-check LLM notes

Network: The network call pattern suggests potential data exfiltration without clear documentation or purpose.
Shell: Shell execution is risky due to the direct use of os.system which can lead to command injection if not properly sanitized.
Obfuscation: The use of eval() with string parameters to cast them into integers is suspicious and may indicate obfuscation, but it could also be used for legitimate purposes such as handling configuration settings.
Credentials: No obvious patterns indicative of credential harvesting have been detected.
Metadata: The presence of non-secure links raises some concern, but there's no evidence of typosquatting or other malicious intent.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (44274 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

213 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

8 unique contributor(s) across 100 commits in deepmodeling/APEX
Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

k_type, task) ) res = requests.post(url, data=open(file_name).read()) print("Successful uplo

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

fp_params["gamma_only"] = int(eval(fp_params["gamma_only"])) assert (
fp_params["symmetry"] = int(eval(fp_params["symmetry"])) assert (
fp_params["cal_force"] = int(eval(fp_params["cal_force"])) assert (
fp_params["cal_stress"] = int(eval(fp_params["cal_stress"])) assert (
"] = int( eval(fp_params["deepks_out_labels"]) )
fp_params["deepks_scf"] = int(eval(fp_params["deepks_scf"])) assert (

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

y_disp.yaml") os.system(self.phonopy_setup_command("-f task.0*/OUT.ABACUS/running_sc
be created') os.system('phonopy band.conf --abacus') os.system('pho
nf --abacus') os.system('phonopy-bandplot --gnuplot band.yaml > band.dat')
ot found" os.system(self.phonopy_setup_command("--fc vasprun.xml"))
created" os.system('phonopy --dim="%s %s %s" -c POSCAR-unitcell band.conf' % (
size[2])) os.system('phonopy-bandplot --gnuplot band.yaml > band.dat')

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: outlook.com

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

Non-HTTPS external link: http://127.0.0.1:8050/
Non-HTTPS external link: http://127.0.0.1:8060/`

✓ Git Repository History

Repository deepmodeling/APEX appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Zhuoyuan Li, Tongqi Wen" 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 apex-flow

Create a mini-application called 'AlloySimulator' that leverages the 'apex-flow' package to simulate and explore the properties of various alloys. The goal of this application is to provide a user-friendly interface where users can input different alloy compositions and see how varying these compositions affects the alloy's properties such as strength, ductility, and thermal conductivity. Here’s a detailed breakdown of the steps and features you should include:

1. **Setup**: Start by installing the 'apex-flow' package along with any necessary dependencies. Ensure your environment is set up properly to handle scientific computations.
2. **User Interface**: Develop a simple but intuitive GUI using a library like Tkinter or PyQt. This interface should allow users to select from a predefined list of alloys or manually input alloy compositions.
3. **Simulation Engine**: Use 'apex-flow' to model the selected alloys. Your application should be able to run simulations based on the inputs provided by the user. Focus on simulating key properties such as mechanical strength, thermal conductivity, and ductility.
4. **Visualization**: Implement visualization tools within the application to display the results of the simulations. Graphs and charts can help illustrate how changing alloy compositions affect their properties.
5. **Documentation & Help**: Include comprehensive documentation and a help section within the application that explains the significance of each property and how the simulation works.
6. **Advanced Features**: Consider adding advanced features such as saving simulation results, comparing multiple alloys side-by-side, and exporting data for further analysis.

Your task is to create a fully-functional mini-app that not only demonstrates the capabilities of 'apex-flow' but also serves as a practical tool for material scientists and engineers exploring alloy properties.