austenite

v0.1.4 suspicious
6.0
Medium Risk

Bayesian end-to-end materials engineering — chemistry to compliance, with citations.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant obfuscation and lacks detailed metadata, raising concerns about its legitimacy and purpose.

  • High obfuscation risk
  • Sparse or missing repository and author information
Per-check LLM notes
  • Network: The use of HTTPX clients suggests the package is designed to make network calls, likely for API interactions, which is not inherently suspicious but should be reviewed for the legitimacy of the API endpoints.
  • Shell: No shell execution patterns were detected, indicating no immediate risk from command execution.
  • Obfuscation: The code shows signs of intentional obfuscation which may hinder understanding and analysis, suggesting potential malicious intent.
  • Credentials: No clear evidence of credential harvesting patterns is present in the provided code snippet.
  • Metadata: The repository is not found, and the author's information is sparse, suggesting potential risk.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.austenite.org
  • Detailed PyPI description (7679 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

  • 391 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • ) self._httpx = httpx.Client( base_url=self._api_url, timeout=sel
  • ) self._httpx = httpx.AsyncClient( base_url=self._api_url, timeout=sel
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • at = 0.0 # T⁻⁹ term def eval(self, T: float) -> float: ln_T = math.log(T)
  • .Thigh: return iv.eval(T) # outside range — clamp to nearest edge if T < in
  • : return intervals[0].eval(intervals[0].Tlow + 1e-6) last = intervals[-1] retur
  • intervals[-1] return last.eval(last.Thigh - 1e-6) # -------------------------------------
  • try: mod = __import__(pkg) v = getattr(mod, "__version__", None)
✓ 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 score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 austenite 
Create a materials science mini-app that allows users to input the chemical composition of an alloy and receive predictions about its properties and compliance with certain standards, all backed by scientific citations. The app should utilize the 'austenite' Python package to handle the Bayesian modeling and materials engineering calculations.

Steps:
1. Design a user-friendly interface where users can input the percentages of different elements in their alloy.
2. Use the 'austenite' package to perform Bayesian inference on the input data to predict material properties such as strength, ductility, and corrosion resistance.
3. Integrate a feature that checks if the predicted properties meet common industry standards (e.g., ASTM, ISO).
4. Display the results with visual aids like charts or graphs.
5. Include citations from scientific literature that support the predictions made by the model.
6. Ensure the app is interactive, allowing users to adjust input parameters and see real-time changes in predictions.

Features:
- Input validation for chemical compositions.
- Real-time feedback during input to guide users.
- Detailed explanations of the Bayesian models used.
- Export options for results in PDF format with embedded citations.
- User authentication to save and track multiple alloy designs.

💬 Discussion Feed

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