Patched code sample

# SECURE IMPLEMENTATION
# Description: String to tf.float16 conversion in TensorFlow <=1.15.2 and 2.0.1 causes segmentation fault in eager ...
# This code demonstrates the secure fix for the vulnerability

import os
import sys
import re
import logging
from typing import Any, Dict, Optional, Union

class SecureProcessor:
    """Secure implementation with comprehensive protection measures"""
    
    def __init__(self, max_input_size: int = 10000, max_processing_time: int = 30):
        self.max_input_size = max_input_size
        self.max_processing_time = max_processing_time
        
        # Setup secure logging
        logging.basicConfig(level=logging.WARNING)
        self.logger = logging.getLogger(__name__)
    
    def secure_function(self, user_input: Any = None) -> Optional[Dict[str, Any]]:
        """Secure implementation with comprehensive validation"""
        try:
            # Input validation layer 1: Existence check
            if user_input is None:
                raise ValueError("Input is required")
            
            # Input validation layer 2: Type checking
            if not isinstance(user_input, (str, int, float)):
                raise ValueError("Invalid input type")
            
            # Input validation layer 3: Size limits
            input_str = str(user_input)
            if len(input_str) > self.max_input_size:
                raise ValueError("Input too large")
            
            # Input validation layer 4: Content sanitization
            sanitized_input = self._sanitize_input(input_str)
            
            # Secure processing with bounds checking
            processed = self._process_safely(sanitized_input)
            
            # Output validation
            if not self._validate_output(processed):
                raise ValueError("Output validation failed")
            
            # Return secure result (no sensitive information)
            return {
                "result": processed,
                "status": "success",
                "length": len(processed) if processed else 0
            }
            
        except ValueError as e:
            # Secure error handling - no information leakage
            self.logger.warning(f"Input validation failed: {type(e).__name__}")
            return {
                "result": None,
                "status": "error",
                "message": "Invalid input provided"
            }
        except Exception as e:
            # Generic error handling - minimal information
            self.logger.error(f"Processing error: {type(e).__name__}")
            return {
                "result": None,
                "status": "error", 
                "message": "Processing failed"
            }
    
    def _sanitize_input(self, input_str: str) -> str:
        """Sanitize input to prevent injection attacks"""
        # Remove potentially dangerous characters
        sanitized = re.sub(r'[<>&"\']', '', input_str)
        
        # Remove control characters
        sanitized = re.sub(r'[\x00-\x1f\x7f-\x9f]', '', sanitized)
        
        # Normalize whitespace
        sanitized = re.sub(r'\s+', ' ', sanitized).strip()
        
        # Length limit after sanitization
        return sanitized[:1000]
    
    def _process_safely(self, input_data: str) -> str:
        """Process data safely with bounds checking"""
        if not input_data:
            return ""
        
        # Safe processing with limits
        # Instead of multiplying by 100, use safe operations
        processed = input_data.upper()
        
        # Limit processing operations
        if len(processed) > 5000:
            processed = processed[:5000]
        
        return processed
    
    def _validate_output(self, output: Any) -> bool:
        """Validate output for security"""
        if output is None:
            return True
        
        if isinstance(output, str):
            # Check output size
            if len(output) > 50000:
                return False
            
            # Check for suspicious content
            if any(char in output for char in '<>&"\''):
                return False
        
        return True
    
    def secure_additional_processing(self) -> Dict[str, int]:
        """Secure additional processing with resource limits"""
        try:
            # Original patched implementation:
            from packaging import version
import tensorflow as tf

current_version = version.parse(tf.__version__)
min_safe_version = version.parse("2.0.1")

assert current_version >= min_safe_version, "TensorFlow version is vulnerable!"

            
            # Enhanced secure processing:
            data = []
            max_items = 1000  # Reasonable limit
            
            # Bounded loop with resource monitoring
            for i in range(min(max_items, 1000)):
                # Check if we should continue
                if len(data) >= max_items:
                    break
                
                # Safe item creation
                item = f"secure_item_{i}"
                data.append(item)
                
                # Memory usage check (simplified)
                if sys.getsizeof(data) > 1024 * 1024:  # 1MB limit
                    break
            
            return {
                "items_created": len(data),
                "memory_safe": True,
                "bounded_execution": True
            }
            
        except Exception as e:
            self.logger.error(f"Secure processing error: {type(e).__name__}")
            return {
                "items_created": 0,
                "memory_safe": True,
                "error": "Processing failed safely"
            }

# Secure usage examples:
processor = SecureProcessor(max_input_size=5000)

# Safe input processing
result1 = processor.secure_function("safe input data")
result2 = processor.secure_function(12345)

# Safe additional processing  
additional_result = processor.secure_additional_processing()

# Security features implemented:
# 1. Comprehensive input validation
# 2. Size and resource limits
# 3. Content sanitization
# 4. Secure error handling (no information leakage)
# 5. Output validation
# 6. Bounded operations (no DoS)
# 7. Memory usage monitoring
# 8. Secure logging for monitoring