alchemize-ai

v0.1.0 suspicious
4.0
Medium Risk

Alchemize: an AI agent that transpiles between any computational framework via LLM

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some unusual behaviors that raise suspicion, particularly concerning its metadata and obfuscation. However, without clear evidence of malicious intent, it cannot be definitively labeled as malicious.

  • Low-effort metadata
  • Unusual obfuscation patterns
Per-check LLM notes
  • Network: No network calls detected, which is normal.
  • Shell: Shell executions appear to be related to building Rust binaries, suggesting legitimate package functionality rather than malicious intent.
  • Obfuscation: The observed patterns seem to be related to charting or plotting code with unusual formatting, possibly indicating some form of obfuscation or minification.
  • Credentials: No clear indicators of credential harvesting were detected.
  • Metadata: The package shows signs of low effort and potentially suspicious maintainer behavior, but there's no clear evidence of malice.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • 7 test file(s) detected (e.g. test_mlx.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

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

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • ize=11) ax.set_ylabel("us/eval (lower is better)", fontsize=11) ax.set_title(title, font
  • ) ax.set_ylabel("Delta us/eval (improvement)", fontsize=11) ax.set_title(title, fontsize
  • ize=11) ax.set_ylabel("us/eval (benchmarks) / event type", fontsize=11) ax.set_title(tit
  • sh}] {rec.us_per_eval:.3f} us/eval (delta: {delta:+.3f}, {rec.description})") prev_us =
  • ch_logp] {us_per_eval:.3f} us/eval ({n_evals:,} evaluations)") state.optimization_log.appen
  • nchmark: {us_per_eval:.3f} us/eval ({n_evals:,} evaluations, {1e6 / us_per_eval:,.0f} evals/sec)
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ing Rust sampler...") subprocess.run( ["cargo", "build", "--release", "--bin", "sampl
  • rt = time.time() result = subprocess.run( [str(binary)], cwd=build_dir, captu
  • r shared library...") subprocess.run( ["cargo", "build", "--release"], cw
  • ench binary...") result = subprocess.run( ["cargo", "build", "--release", "--bin", "bench"],
  • n{param_str}\n" result = subprocess.run( [str(binary)], cwd=build_dir, input
  • """ try: result = subprocess.run( ["nvidia-smi"], capture_output=True
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: pymc-labs.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" 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 alchemize-ai 
Create a mini-application called 'Framework Transformer' that leverages the 'alchemize-ai' package to enable users to convert code written in one computational framework into another. This application should allow users to input their code along with the source and target frameworks (e.g., TensorFlow to PyTorch). Additionally, it should provide real-time feedback on the conversion process and offer suggestions for optimization in the new framework. Here are the steps and features your application should include:

1. **User Interface**: Develop a simple web interface where users can paste their code and select the source and target frameworks from a dropdown menu.
2. **Code Parsing**: Utilize 'alchemize-ai' to parse the user-submitted code and understand its structure and purpose within the original framework.
3. **Transformation Process**: Implement the core functionality of 'alchemize-ai' to transpile the parsed code into the selected target framework. Ensure that the transformation preserves the intended functionality as closely as possible.
4. **Real-Time Feedback**: As the code is being transformed, display real-time updates about the progress and any potential issues or optimizations that could be applied in the new framework.
5. **Optimization Suggestions**: After the transformation, present users with recommendations for optimizing their code in the new framework, such as performance improvements or better practices specific to the target framework.
6. **Error Handling**: Include robust error handling to manage cases where the transformation might not be possible due to framework limitations or unsupported operations.
7. **Testing and Validation**: Provide a feature that allows users to test the transformed code directly within the application, ensuring it works as expected before downloading or copying it.
8. **Documentation and Support**: Offer comprehensive documentation and support resources within the application to help users understand how to use the tool effectively and troubleshoot common issues.

This application aims to simplify the process of switching between different computational frameworks, making it easier for developers to leverage the strengths of each while maintaining productivity.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!