axolotl

v0.17.0 suspicious
6.0
Medium Risk

LLM Trainer

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits elevated risks due to shell and network interactions, suggesting potential for unauthorized command execution and data fetching activities. However, there's insufficient evidence to conclusively label it as malicious.

  • High shell risk due to unvalidated subprocess calls
  • Moderate network risk with ambiguous purposes
Per-check LLM notes
  • Network: The network calls could be legitimate for fetching configuration or API data, but the lack of context increases suspicion.
  • Shell: The use of subprocess calls without proper validation or sanitization is risky and may indicate potential execution of arbitrary commands.
  • Obfuscation: The observed patterns may indicate some level of code obfuscation but do not strongly suggest malicious intent without additional context.
  • Credentials: No clear indicators of credential harvesting or secret stealing activities were found.
  • Metadata: The package shows some low-effort signs but lacks clear malicious indicators.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://docs.axolotl.ai/
  • 2 documentation file(s) (e.g. generate_config_docs.py)
  • Detailed PyPI description (16898 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

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

Active multi-contributor project

  • 17 unique contributor(s) across 100 commits in axolotl-ai-cloud/axolotl
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • try: response = requests.get(config, timeout=30) response.raise_for_status() # C
  • e try: response = requests.get(raw_url, timeout=30) response.raise_for_status()
  • e with timeout response = requests.get(api_url, timeout=30) response.raise_for_status() tre
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • print("=" * 80) model.eval() with torch.no_grad(): if is_diffusion:
  • l_tokens=True) model.eval() with torch.no_grad(): generation_confi
  • e_lora" vllm_serve_main = __import__(serve_module, fromlist=["main"]).main tensor_parallel_size = 1 data_parallel_size =
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • bprocess. if exit_code := subprocess.call(cmd.split(), cwd=run_folder): # nosec exit(exit_cod
  • m subprocess. exit_code = subprocess.call(cmd.split(), cwd=run_folder, env=sp_env) # nosec if exi
  • py" ) subprocess.run( # nosec B603 B607 ["truss", "train", "push
  • bprocess. if exit_code := subprocess.call( # nosec B603 cmd.split(), cwd=run_folder, env=new_
  • try: manifest = subprocess.check_output( # nosec ["docker", "manifest", "inspect",
  • and(base_cmd, kwargs) subprocess.run(cmd, check=True) # nosec B603 else: from axolot
✓ 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

Repository axolotl-ai-cloud/axolotl appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • 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 axolotl 
Create a personalized language model trainer application using the 'axolotl' package. This app will allow users to train their own custom language models based on specific datasets they provide. The application should have a user-friendly interface where users can upload their dataset, select training parameters, and start the training process. Upon completion, the trained model should be saved and made available for further use or download.

Key Features:
1. Dataset Upload: Users should be able to upload text files or other supported formats as their training data.
2. Training Parameters Configuration: Options for setting epochs, batch size, learning rate, and other relevant parameters.
3. Training Process Visualization: A progress bar or chart showing the training process and performance metrics.
4. Model Saving: After successful training, the model should be saved locally or uploaded to a cloud storage service.
5. Error Handling and Feedback: Provide informative error messages and guidance if something goes wrong during the training process.

How 'axolotl' Package is Utilized:
- Use 'axolotl' to preprocess the uploaded dataset into a format suitable for training.
- Leverage 'axolotl's training capabilities to fine-tune a pre-trained language model on the provided dataset.
- Employ 'axolotl's evaluation tools to monitor and display the model's performance throughout the training process.
- Utilize 'axolotl's saving functionalities to store the trained model for future use.

💬 Discussion Feed

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