auto-round-nightly

v0.14.0.dev20260607 suspicious
5.0
Medium Risk

Repository of AutoRound: Advanced Weight-Only Quantization Algorithm for LLMs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation through the use of eval() with user input, raising concerns about its intent. However, other checks indicate low risks in terms of network calls, shell execution, and credential harvesting.

  • Obfuscation risk due to eval() usage
  • Non-HTTPS link in metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: Shell execution appears to be related to version control operations, likely for versioning purposes, which is generally benign.
  • Obfuscation: The code uses eval() with user input which can be a sign of obfuscation or evasion techniques.
  • Credentials: No clear patterns indicating credential harvesting were found.
  • Metadata: The package has a single author with one package, and contains a non-HTTPS link which could be a potential risk.

📦 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 (21418 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

  • 260 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 18 unique contributor(s) across 100 commits in intel/auto-round
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • , ) else: eval(args) def run(): if "list" in sys.argv or "--list" in
  • ) -> None: self.model.eval() # Keep rotation matrices on the model — they are
  • .deepcopy(model) original.eval() for p in original.parameters(): p.requires_gra
  • thogonality(model) model.eval() return TrainingResult( loss_history=loss_hist
  • return {} self.model.eval() device = next(self.model.parameters()).device
  • ty_cache() self.model.eval() def _trigger_event(self, event_name: str, **kwargs) -
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • (): try: result = subprocess.run( ["git", "describe", "--exact-match", "--tags"],
  • n__ try: result = subprocess.run(["git", "describe", "--tags"], capture_output=True, text=Tru
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: intel.com

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://arxiv.org/abs/2512.04746
✓ Git Repository History

Repository intel/auto-round appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Intel AIPT Team" 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 auto-round-nightly 
Create a mini-application called 'QuantizeIt' that leverages the 'auto-round-nightly' package to perform advanced weight-only quantization on pre-trained language models. This application will allow users to input a path to their model checkpoint and specify the desired quantization level. Upon running the app, it should load the model, apply the quantization algorithm provided by 'auto-round-nightly', and then save the quantized model to a specified output directory.

Key Features:
1. User Interface: Develop a simple command-line interface (CLI) where users can interactively provide the necessary inputs such as model path, quantization level, and output directory.
2. Model Loading: Implement functionality to load various types of pre-trained models supported by 'auto-round-nightly'.
3. Quantization Levels: Allow users to choose from different quantization levels (e.g., 8-bit, 4-bit).
4. Performance Metrics: After quantization, display performance metrics comparing the original and quantized models, including any potential loss in accuracy.
5. Save Quantized Model: Ensure the quantized model is saved correctly so it can be loaded for inference without issues.
6. Error Handling: Incorporate robust error handling to manage common issues like incorrect file paths or unsupported model types.

Utilizing 'auto-round-nightly': Integrate the package's core functionalities into your application to handle the quantization process. Specifically, use its advanced algorithms to optimize the weights of the neural network while preserving as much accuracy as possible. Make sure to document how each part of the package is being used within your code.

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