archgene

v0.4.1 suspicious
6.0
Medium Risk

Design, verify, and generate LLM architectures before you waste GPU compute

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple indicators of potential risk, including high shell execution risk, moderate network and obfuscation risks, and concerning metadata indicators. While there is no definitive evidence of malicious behavior, the combination of these factors warrants further investigation.

  • High Shell Execution Risk
  • Moderate Network and Obfuscation Risks
  • Concerning Metadata Indicators
Per-check LLM notes
  • Network: Network calls to external APIs may be part of the package's functionality but should be reviewed for necessity and legitimacy.
  • Shell: Executing shell commands like 'huggingface-cli whoami' and 'docker --version' can be benign if related to package functionality, but poses higher risk due to potential for unauthorized system access.
  • Obfuscation: The code shows signs of obfuscation which could indicate an attempt to hide the actual functionality, but without more context, it's hard to determine malicious intent.
  • Credentials: No clear patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer's lack of history and the repository's low activity suggest potential risk.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_core.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3426 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

  • 86 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 20 commits in Tejas163/ArchGene
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • ation" response = requests.get(api_url, timeout=10) if response.status_code ==
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • hitecture(gene) model.eval() if sample_input is None: batc
  • erval == 0: model.eval() val_loss = 0.0 val_steps = 0
  • timestamp=__import__("datetime").datetime.now().isoformat(), notes=notes
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • ) try: subprocess.run( ["huggingface-cli", "whoami"],
  • ult: try: subprocess.run( ["docker", "--version"], ca
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 archgene 
Create a mini-application named 'ArchitectAI' that leverages the 'archgene' Python package to streamline the process of designing, verifying, and generating Large Language Model (LLM) architectures before deploying them on GPU hardware. This application should serve as a tool for researchers and developers to optimize their models efficiently.

Step 1: Define the Application Structure
- ArchitectAI should have a user-friendly command-line interface (CLI).
- It should support basic operations like listing available model architectures, creating new designs, and validating existing ones.

Step 2: Implement Core Functionality Using 'archgene'
- Utilize 'archgene' to design different types of LLM architectures based on user inputs.
- Integrate 'archgene' verification tools to check if the designed architectures meet specific performance criteria.
- Use 'archgene' generation capabilities to output optimized model configurations ready for deployment.

Step 3: Enhance with Additional Features
- Include a feature to compare multiple architectures side-by-side in terms of complexity, expected performance, and resource usage.
- Provide options for users to save and load their custom architectures.
- Offer a tutorial mode that guides users through the process of designing their first architecture using 'archgene'.

Step 4: Test and Optimize
- Thoroughly test each functionality to ensure reliability.
- Gather feedback from initial users and make necessary adjustments.
- Continuously update the application to reflect improvements in the 'archgene' package.

Your task is to outline the complete workflow for developing 'ArchitectAI', including setting up the environment, integrating 'archgene', implementing the CLI, adding extra features, and ensuring everything works seamlessly together.

💬 Discussion Feed

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