AI Analysis
Final verdict: SUSPICIOUS
The package exhibits multiple high-risk indicators including potential credential harvesting, obfuscation techniques, and shell command execution. These factors collectively suggest a significant likelihood of malicious intent.
- References to sensitive files and paths
- Obfuscation through Base64 decoding
- Shell command execution capabilities
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package relies on external services.
- Shell: The presence of shell command execution suggests the package may interact with system commands, which could be risky if not properly sanitized or controlled.
- Obfuscation: The presence of Base64 decoding and attempts to validate strings suggests an attempt to hide or secure code logic, which may indicate obfuscation for malicious purposes.
- Credentials: References to sensitive files like ``~/.ssh/id_rsa`` and ``/etc/passwd``, along with path traversal errors, strongly suggest an intent to harvest credentials or access restricted files.
- Metadata: The maintainer's author name is missing or very short and seems to be a new or inactive account, raising some concerns but not definitive proof of malice.
Package Quality Overall: Medium (7.6/10)
✦ High
Test Suite
9.0
Test suite present — 12 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml12 test file(s) detected (e.g. __init__.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/mcp-tool-shop-org/backpropagate#readmeDetailed PyPI description (33007 chars)
✦ High
Contributing Guide
9.0
Has contribution guidelines and governance files
Governance file: security.pyDevelopment Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Classifier: Typing :: Typed552 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 100 commits in mcp-tool-shop-org/backpropagateTwo distinct contributors found
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 10.0
Found 6 obfuscation pattern(s)
e try: decoded = base64.b64decode(parts[1], validate=True).decode("utf-8") except (ValueErlen("Basic "):] decoded = base64.b64decode(encoded).decode("utf-8") assert decoded == "alice:hunter"][len("Basic "):] assert base64.b64decode(encoded).decode("utf-8") == ":" def test_ws_recorder_detec._device) self._model.eval() logger.info(f"Model loaded on {self._device}")return None model.eval() total = 0.0 counted = 0 with torch.no_grad():port torch # lazy model.eval() samples: list[GenerationSample] = [] do_sample = t
Shell / Subprocess Execution
score 10.0
Found 5 shell execution pattern(s)
e child. BRIDGE-A-004: ``subprocess.run(cmd, timeout=N)`` does NOT reliably forward SIGINT / Ctr_output else None proc = subprocess.Popen( # noqa: S603 — argv is callee-controlled here cmd,[] try: result = subprocess.run( ["ollama", "list"], capture_output=dels's timeout shape. subprocess.run( ["ollama", "rm", model_name], captus flags. result = subprocess.run( [cmd, "diff-runs", "--", self.current_run_i
Credential Harvesting
score 10.0
Found 6 credential access pattern(s)
link to ``~/.ssh/id_rsa`` / ``/etc/passwd`` would # otherwise be uploaded verbatim to the pu>>> safe_path("../../etc/passwd", allowed_base="/models") PathTraversalError: PathathTraversalError: Path '../../etc/passwd' escapes allowed directory '/models' >>> safe_path>>> safe_path("/etc/../etc/passwd") PathTraversalError: Path traversal detected in: /traversal detected in: /etc/../etc/passwd """ path = Path(user_path) # Check for relativion-shaped value (e.g. ``--to=/etc/passwd`` or ``-o``) is parsed by # the downstream argparse-based C
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: users.noreply.github.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository mcp-tool-shop-org/backpropagate appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 backpropagate
Your task is to create a mini-application that allows users to fine-tune their own language models using the 'backpropagate' package. This application will serve as a user-friendly interface for experimenting with different datasets and configurations without needing deep knowledge of machine learning principles. Here are the steps and features your app should include: 1. **Setup**: Begin by installing the 'backpropagate' package and any other necessary dependencies. 2. **User Interface**: Develop a simple, intuitive UI where users can upload their custom dataset (text files, CSVs, etc.) and select from predefined model architectures or input their own. 3. **Configuration Options**: Allow users to tweak parameters such as batch size, learning rate, epochs, and loss functions directly through the UI. 4. **Fine-Tuning Process**: Utilize 'backpropagate' to handle the fine-tuning process. Ensure the application leverages the package's smart defaults for ease of use while allowing advanced customization. 5. **Progress Tracking**: Implement real-time progress tracking so users can monitor the training process. 6. **Model Evaluation**: After training, provide metrics and visualizations for evaluating the performance of the fine-tuned model. 7. **Deployment Options**: Once satisfied, users should have the option to save and export their model for future use or deployment. Incorporate these elements into a cohesive application that showcases the capabilities of 'backpropagate', making it accessible for both beginners and experienced developers.
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