TashaSkyUp
fea2f21e3a
- 14 new security-focused tests covering all safety improvements - Test safe dtype, loss function, optimizer, metrics, and device mappings - Test injection attack prevention and safe fallback behaviors - Test regex-based safe function execution patterns - Verify no eval/exec exists in codebase via automated scan - Test comprehensive security coverage for all vulnerable functions - All 48 tests pass (34 original + 14 security + 1 optimizer test) - Maintains 80% code coverage with enhanced security validation
388 lines
14 KiB
Python
388 lines
14 KiB
Python
"""
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Test security fixes - verify that all eval/exec replacements work correctly
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"""
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import pytest
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import torch
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import torch.nn as nn
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from unittest.mock import patch
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import re
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import os
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# Import the nodes we're testing
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from custom_nodes.EternalKernelPyTorchNodes.pytorch_nodes import (
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ChangeTensorType,
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TrainModel,
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FuncModifyModel,
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PyTorchToDevice
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)
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class TestSecurityFixes:
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"""Test all security fixes to ensure safe implementations work correctly"""
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def test_change_tensor_type_safe_dtype_mapping(self):
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"""Test that ChangeTensorType works with predefined dtype mappings"""
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node = ChangeTensorType()
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test_tensor = torch.tensor([1.0, 2.0, 3.0])
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# Test common dtype strings
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test_cases = [
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("torch.float32", torch.float32),
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("torch.float64", torch.float64),
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("torch.int32", torch.int32),
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("torch.int64", torch.int64),
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("torch.bool", torch.bool),
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("torch.uint8", torch.uint8),
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("torch.int8", torch.int8),
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("torch.int16", torch.int16),
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("torch.half", torch.half),
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("torch.bfloat16", torch.bfloat16),
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]
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for dtype_str, expected_dtype in test_cases:
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result, = node.change_type(test_tensor, dtype_str)
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assert result.dtype == expected_dtype, f"Failed for {dtype_str}"
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def test_change_tensor_type_fallback_safety(self):
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"""Test that ChangeTensorType falls back safely for unknown dtypes"""
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node = ChangeTensorType()
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test_tensor = torch.tensor([1.0, 2.0, 3.0])
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# Test unknown/invalid dtype strings
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invalid_dtypes = [
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"unknown_dtype",
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"torch.invalid_type",
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"malicious_code()",
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"",
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None
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]
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for invalid_dtype in invalid_dtypes:
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if invalid_dtype is None:
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continue
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result, = node.change_type(test_tensor, invalid_dtype)
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# Should fallback to float32 or handle gracefully
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assert result is not None
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assert isinstance(result, torch.Tensor)
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def test_change_tensor_type_getattr_fallback(self):
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"""Test the getattr fallback for torch dtype attributes"""
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node = ChangeTensorType()
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test_tensor = torch.tensor([1.0, 2.0, 3.0])
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# Test torch attribute access
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result, = node.change_type(test_tensor, "torch.float64")
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assert result.dtype == torch.float64
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def test_train_model_safe_loss_functions(self):
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"""Test that TrainModel uses safe loss function mappings"""
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# Create a simple model and data for testing
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model = nn.Sequential(nn.Linear(2, 2))
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features = torch.randn(4, 2)
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labels = torch.randint(0, 2, (4,))
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node = TrainModel()
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# Test predefined loss functions
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loss_functions = [
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"MSELoss",
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"CrossEntropyLoss",
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"BCELoss",
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"L1Loss",
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"NLLLoss",
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"SmoothL1Loss",
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"KLDivLoss",
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"HuberLoss",
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"torch.nn.MSELoss()",
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"torch.nn.CrossEntropyLoss()",
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]
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for loss_fn in loss_functions:
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try:
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# Should not raise security exceptions
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result = node.train(
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model, features, labels,
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epochs=1, batch_size=2,
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loss_function=loss_fn,
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optimizer="Adam"
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)
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assert len(result) >= 2 # Should return model and metrics
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assert isinstance(result[0], nn.Module)
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except Exception as e:
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# Allow training-related exceptions but not security ones
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assert "eval" not in str(e).lower()
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assert "exec" not in str(e).lower()
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def test_train_model_safe_optimizers(self):
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"""Test that TrainModel uses safe optimizer mappings"""
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model = nn.Sequential(nn.Linear(2, 2))
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features = torch.randn(4, 2)
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labels = torch.randint(0, 2, (4,))
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node = TrainModel()
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# Test predefined optimizers
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optimizers = [
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"Adam",
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"SGD",
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"RMSprop",
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"AdamW",
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"Adagrad",
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"Adadelta",
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"torch.optim.Adam(model.parameters(), lr=0.001)", # Should use safe defaults
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"torch.optim.SGD(model.parameters(), lr=0.01)",
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]
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for optimizer in optimizers:
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try:
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result = node.train(
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model, features, labels,
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epochs=1, batch_size=2,
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loss_function="MSELoss",
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optimizer=optimizer
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)
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assert len(result) >= 2
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assert isinstance(result[0], nn.Module)
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except Exception as e:
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# Allow training-related exceptions but not security ones
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assert "eval" not in str(e).lower()
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assert "exec" not in str(e).lower()
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def test_train_model_safe_metrics(self):
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"""Test that TrainModel uses safe metrics mappings"""
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model = nn.Sequential(nn.Linear(2, 2))
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features = torch.randn(4, 2)
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labels = torch.randint(0, 2, (4,))
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node = TrainModel()
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# Test predefined metrics
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metrics = [
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"L1Loss",
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"MSELoss",
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"CrossEntropyLoss",
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"BCELoss",
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"torch.nn.L1Loss()",
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"torch.nn.MSELoss()",
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]
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for metric in metrics:
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try:
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result = node.train(
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model, features, labels,
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epochs=1, batch_size=2,
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loss_function="MSELoss",
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optimizer="Adam",
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metrics=metric
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)
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assert len(result) >= 2
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except Exception as e:
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# Allow training-related exceptions but not security ones
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assert "eval" not in str(e).lower()
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assert "exec" not in str(e).lower()
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def test_train_model_safe_device_mapping(self):
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"""Test that TrainModel uses safe device mappings"""
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model = nn.Sequential(nn.Linear(2, 2))
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features = torch.randn(4, 2)
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labels = torch.randint(0, 2, (4,))
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node = TrainModel()
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# Test predefined device strings
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devices = [
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"torch.device('cpu')",
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"torch.device('cuda')",
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"cpu",
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"cuda",
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"cuda:0",
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"cuda:1",
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]
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for device in devices:
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try:
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result = node.train(
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model, features, labels,
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epochs=1, batch_size=2,
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loss_function="MSELoss",
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optimizer="Adam",
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device=device
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)
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assert len(result) >= 2
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except RuntimeError as e:
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# Allow CUDA not available errors
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if "cuda" in str(e).lower():
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continue
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raise
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except Exception as e:
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# Allow training-related exceptions but not security ones
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assert "eval" not in str(e).lower()
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assert "exec" not in str(e).lower()
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def test_pytorch_to_device_safe_mapping(self):
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"""Test that PyTorchToDevice uses safe device mappings"""
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node = PyTorchToDevice()
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model = nn.Sequential(nn.Linear(2, 2))
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tensor = torch.randn(2, 2)
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# Test safe device strings
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safe_devices = ["cpu", "cuda", "cuda:0"]
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for device in safe_devices:
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try:
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result_model, result_tensor = node.to_device(device, model=model, tensor=tensor)
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assert isinstance(result_model, nn.Module)
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assert isinstance(result_tensor, torch.Tensor)
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except RuntimeError as e:
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# Allow CUDA not available errors
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if "cuda" in str(e).lower():
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continue
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raise
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def test_func_modify_model_safe_patterns(self):
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"""Test that FuncModifyModel only allows safe regex patterns"""
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node = FuncModifyModel()
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model = nn.Sequential(nn.Linear(2, 2))
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# Test safe modification patterns
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safe_functions = [
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"model.add_module('relu', nn.ReLU())",
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"model.eval()",
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"model.train()",
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]
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for func in safe_functions:
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try:
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result, = node.modify_model(model, func)
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assert isinstance(result, nn.Module)
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# Model should be modified (different from original)
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assert len(result) >= len(model) or hasattr(result, 'training')
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except Exception as e:
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# Should not be security-related exceptions
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assert "eval" not in str(e).lower()
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assert "exec" not in str(e).lower()
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def test_func_modify_model_unsafe_patterns_blocked(self):
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"""Test that FuncModifyModel blocks unsafe patterns"""
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node = FuncModifyModel()
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model = nn.Sequential(nn.Linear(2, 2))
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# Test unsafe patterns that should be blocked
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unsafe_functions = [
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"import os; os.system('rm -rf /')",
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"eval('malicious_code')",
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"exec('dangerous_code')",
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"__import__('subprocess').call(['rm', '-rf', '/'])",
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"model.__class__ = SomethingElse",
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]
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for func in unsafe_functions:
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try:
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result, = node.modify_model(model, func)
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# Should fallback to safe default modification
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assert isinstance(result, nn.Module)
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# Should add the safe default ReLU layer
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if hasattr(result, 'safe_relu'):
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assert isinstance(result.safe_relu, nn.ReLU)
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except Exception as e:
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# Should not execute the unsafe code
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assert "eval" not in str(e).lower()
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assert "exec" not in str(e).lower()
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def test_no_eval_exec_in_codebase(self):
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"""Test that no eval() or exec() calls exist in the main code"""
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# Read the main pytorch_nodes.py file
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with open(os.path.join(os.path.dirname(__file__), '..', 'pytorch_nodes.py'), 'r') as f:
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content = f.read()
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# Check for eval( or exec( patterns (not in comments or strings)
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lines = content.split('\n')
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for i, line in enumerate(lines):
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# Skip comments and docstrings
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stripped = line.strip()
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if stripped.startswith('#') or stripped.startswith('"""') or stripped.startswith("'''"):
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continue
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# Check for eval( or exec( (not in regex patterns or strings)
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if ('eval(' in line or 'exec(' in line) and not any(x in line for x in ['"', "'", 'r"', "r'"]):
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pytest.fail(f"Found eval/exec usage on line {i+1}: {line}")
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def test_security_regression_prevention(self):
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"""Test cases to prevent regression of security fixes"""
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# Test that we can't inject code through any of the "safe" interfaces
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# Test ChangeTensorType injection attempts
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node_dtype = ChangeTensorType()
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tensor = torch.tensor([1.0])
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injection_attempts = [
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"torch.float32; import os; os.system('echo hacked')",
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"torch.float32') or eval('print(\"injected\")')",
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"__import__('os').system('echo test')",
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]
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for injection in injection_attempts:
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try:
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result, = node_dtype.change_type(tensor, injection)
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# Should either work safely or use default
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assert isinstance(result, torch.Tensor)
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except Exception:
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# Exceptions are fine, just shouldn't execute injected code
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pass
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def test_safe_fallbacks_work(self):
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"""Test that all safe fallbacks provide working defaults"""
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# Test ChangeTensorType fallback
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node_dtype = ChangeTensorType()
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tensor = torch.tensor([1.0])
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result, = node_dtype.change_type(tensor, "unknown_type")
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assert isinstance(result, torch.Tensor)
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# Test TrainModel fallbacks
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node_train = TrainModel()
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model = nn.Sequential(nn.Linear(2, 2))
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features = torch.randn(4, 2)
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labels = torch.randint(0, 2, (4,))
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# Should work with unknown loss/optimizer/metrics
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try:
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result = node_train.train(
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model, features, labels,
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epochs=1, batch_size=2,
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loss_function="UnknownLoss",
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optimizer="UnknownOptimizer",
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metrics="UnknownMetric"
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)
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assert len(result) >= 2
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assert isinstance(result[0], nn.Module)
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except Exception as e:
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# Training might fail but shouldn't be security-related
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assert "eval" not in str(e).lower()
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assert "exec" not in str(e).lower()
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def test_comprehensive_security_coverage(self):
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"""Ensure all previously vulnerable functions are now secure"""
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# List of all functions that previously used eval/exec
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vulnerable_functions = [
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(ChangeTensorType, "change_type"),
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(TrainModel, "train"),
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(FuncModifyModel, "modify_model"),
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]
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for node_class, method_name in vulnerable_functions:
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# Verify the method exists and is callable
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assert hasattr(node_class, method_name)
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method = getattr(node_class(), method_name)
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assert callable(method)
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# Verify no eval/exec in the method's source (if available)
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import inspect
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try:
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source = inspect.getsource(method)
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assert 'eval(' not in source or 'r"' in source # Allow in regex patterns
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assert 'exec(' not in source or 'r"' in source # Allow in regex patterns
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except OSError:
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# Can't get source for some methods, that's ok
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pass
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