ascend-forge

v0.1.3 suspicious
6.0
Medium Risk

NPU operator build & test framework

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation and lacks clear maintainer information, raising concerns about its legitimacy and intentions.

  • High obfuscation risk
  • Lack of maintainer metadata
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Git commands are used for version control operations, suggesting package maintenance activities rather than malicious intent.
  • Obfuscation: The obfuscation patterns may indicate an attempt to hide code logic, which is suspicious but not conclusive without more context.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The package has suspicious links and lacks maintainer information, indicating potential risk.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 23 test file(s) found

  • Test runner config found: pyproject.toml
  • 23 test file(s) detected (e.g. test_analyze_benchmark_csv.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (27904 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

  • 195 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 4.0

Found 2 obfuscation pattern(s)

  • dir / "demo.json").write_text(__import__("json").dumps(cfg)) captured = {} class FakeRunne
  • original_make_timestamp = __import__('forge.tester', fromlist=['_make_timestamp'])._make_timestamp def mock_timestamp(): c
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • _path, good, bad): proc = subprocess.run(["git", "rev-list", "--reverse", f"{good}..{bad}"], cwd=repo
  • commit {commit[:8]}...") subprocess.run(["git", "checkout", commit], cwd=repo_path, capture_output=T
  • nt_ref(repo_path): proc = subprocess.run(["git", "symbolic-ref", "--short", "HEAD"], cwd=repo_path, c
  • roc.stdout.strip() proc = subprocess.run(["git", "rev-parse", "HEAD"], cwd=repo_path, capture_output=
  • tuple[str, bool]: proc = subprocess.run(["git", "rev-parse", "--abbrev-ref", "HEAD"], cwd=repo_path,
  • riginal_branch}") proc = subprocess.run(["git", "status", "--porcelain"], cwd=repo_path, capture_out
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8765
  • Non-HTTPS external link: http://proxy.example.com:8080
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 ascend-forge 
Your task is to develop a small yet powerful application using the 'ascend-forge' Python package. This package provides a robust framework for building and testing operators on NPUs (Neural Processing Units), making it ideal for developers working on AI and machine learning projects. Your goal is to create a utility that simplifies the process of benchmarking different operators on an NPU, allowing users to easily compare performance metrics such as latency and throughput.

The application should have the following features:
1. User Interface: Develop a simple command-line interface (CLI) that allows users to input parameters for the operators they want to benchmark.
2. Operator Selection: Implement a feature where users can choose from a predefined set of operators (e.g., convolution, matrix multiplication) or input custom operators.
3. Benchmarking: Use 'ascend-forge' to compile and run the selected operators on an NPU. Collect performance data including execution time and throughput.
4. Data Visualization: Integrate a basic plotting library (such as matplotlib) to display the benchmark results graphically, comparing the performance of different operators.
5. Report Generation: Generate a report summarizing the benchmark results, highlighting the best-performing operators based on user-defined criteria (e.g., lowest latency).

To utilize 'ascend-forge', you will need to:
- Install the package using pip if not already installed.
- Import the necessary modules from 'ascend-forge' to define and compile your operators.
- Use 'ascend-forge' functions to execute the operators on an NPU and retrieve performance metrics.
- Ensure your code is well-documented and includes error handling for robustness.

This project will not only showcase the capabilities of 'ascend-forge' but also provide a practical tool for developers looking to optimize their NPU-based applications.

💬 Discussion Feed

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