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CVE-2018-1000518

Websockets v4 improper handling of compressed data DoS via memory exhaustion

  • CVSS 7.5
  • CWE-400 Uncontrolled Resource Consumption
  • Resource Management
  • Remote

aaugustin websockets version 4 contains a CWE-409: Improper Handling of Highly Compressed Data (Data Amplification) vulnerability in Servers and clients, unless configured with compression=None that can result in Denial of Service by memory exhaustion. This attack appear to be exploitable via Sending a specially crafted frame on an established connection. This vulnerability appears to have been fixed in 5.

CVSS base score
7.5
Published
2018-06-26
OWASP
A06 Vulnerable and Outdated Components
Orthogonal defect classification
Function
Code defect classification
Incorrect Functionality
Category
Resource Management
Subcategory
Resource Exhaustion
Accessibility scope
Remote
Impact
Denial of Service (DoS)
Fixed by upgrading
Yes

Solution

Update to aaugustin websockets version 5 or higher.

Vulnerable code sample

def process_user_data(user_input):
    """Vulnerable function that demonstrates security issues."""
    # VULNERABLE: No input validation or sanitization
    # Allows various security attacks through malicious input
    
    # Direct processing without security checks
    result = str(user_input)
    
    # Unsafe operations that could be exploited
    if "admin" in str(user_input):
        return "Admin access granted"
    
    return f"Processed: {result}"

def handle_data(data):
    """Vulnerable data handler."""
    # DANGEROUS: No validation of data structure or content
    return process_user_data(data)

# Example of vulnerable usage:
# process_user_data("<script>alert('XSS')</script>")  # XSS attack
# process_user_data("'; DROP TABLE users; --")  # SQL injection attempt
# handle_data(malicious_payload)  # Various attacks possible

Patched code sample

import html
import re
import json
from typing import Any, Dict, Union

class SecureDataProcessor:
    """Secure data processing with comprehensive input validation and sanitization."""
    
    def __init__(self):
        self.max_input_size = 10000
        self.allowed_types = (str, int, float, bool)
        self.dangerous_patterns = [
            '<script', 'javascript:', 'vbscript:', 'data:',
            'on\w+\s*=', '__import__', 'eval\s*\(',
            'DROP\s+TABLE', 'DELETE\s+FROM', 'INSERT\s+INTO',
            'UPDATE\s+SET', 'UNION\s+SELECT'
        ]
    
    def process_user_data(self, user_input: Any) -> Dict[str, Any]:
        """Secure function that processes user data with comprehensive validation."""
        
        # Type validation
        if not isinstance(user_input, self.allowed_types):
            raise ValueError("Input type not allowed")
        
        # Convert to string for processing
        input_str = str(user_input)
        
        # Size validation
        if len(input_str) > self.max_input_size:
            raise ValueError("Input too large")
        
        # Security validation
        if self._contains_dangerous_patterns(input_str):
            raise ValueError("Input contains potentially dangerous content")
        
        # Sanitize input
        sanitized_input = self._sanitize_input(input_str)
        
        # Role-based processing with proper validation
        if self._is_admin_request(sanitized_input):
            # Proper admin validation would go here
            return {
                'success': True,
                'result': 'Admin request detected - requires proper authentication',
                'processed_data': sanitized_input[:100],  # Limit output
                'message': 'Admin access requires additional validation'
            }
        
        return {
            'success': True,
            'result': f"Securely processed: {sanitized_input[:100]}",
            'processed_data': sanitized_input,
            'message': 'Data processed securely'
        }
    
    def handle_data(self, data: Any) -> Dict[str, Any]:
        """Secure data handler with proper validation and error handling."""
        
        try:
            # Validate data structure
            if data is None:
                raise ValueError("Data cannot be None")
            
            # Process data securely
            result = self.process_user_data(data)
            
            return {
                'success': True,
                'result': result,
                'data_type': type(data).__name__,
                'message': 'Data handled securely'
            }
            
        except ValueError as e:
            return {
                'success': False,
                'error': str(e),
                'message': 'Data validation failed'
            }
        except Exception as e:
            return {
                'success': False,
                'error': 'Processing error',
                'message': 'Unexpected error during data processing'
            }
    
    def _contains_dangerous_patterns(self, content: str) -> bool:
        """Check for dangerous patterns in input content."""
        
        content_lower = content.lower()
        for pattern in self.dangerous_patterns:
            if re.search(pattern, content_lower, re.IGNORECASE):
                return True
        return False
    
    def _sanitize_input(self, input_str: str) -> str:
        """Sanitize input string to remove dangerous content."""
        
        # HTML escape to prevent XSS
        sanitized = html.escape(input_str, quote=True)
        
        # Remove script tags
        sanitized = re.sub(r'<script[^>]*>.*?</script>', '', sanitized, flags=re.IGNORECASE | re.DOTALL)
        
        # Remove event handlers
        sanitized = re.sub(r'on\w+\s*=\s*["'][^"']*["']', '', sanitized, flags=re.IGNORECASE)
        
        # Remove SQL injection patterns
        sanitized = re.sub(r'(DROP|DELETE|INSERT|UPDATE|UNION)\s+(TABLE|FROM|INTO|SET|SELECT)', 
                          '', sanitized, flags=re.IGNORECASE)
        
        # Limit length
        if len(sanitized) > 1000:
            sanitized = sanitized[:1000]
        
        return sanitized.strip()
    
    def _is_admin_request(self, content: str) -> bool:
        """Safely check if content contains admin-related keywords."""
        
        admin_keywords = ['admin', 'administrator', 'root', 'superuser']
        content_lower = content.lower()
        
        return any(keyword in content_lower for keyword in admin_keywords)
    
    def validate_input_safety(self, input_data: Any) -> Dict[str, Any]:
        """Validate input for safety and provide detailed feedback."""
        
        issues = []
        warnings = []
        
        input_str = str(input_data)
        
        # Check size
        if len(input_str) > 5000:
            warnings.append('Large input size')
        
        # Check for dangerous patterns
        if self._contains_dangerous_patterns(input_str):
            issues.append('Dangerous patterns detected')
        
        # Check for suspicious characters
        if re.search(r'[<>"\'`]', input_str):
            warnings.append('Special characters detected')
        
        return {
            'is_safe': len(issues) == 0,
            'issues': issues,
            'warnings': warnings,
            'input_size': len(input_str)
        }

# Example of secure usage:
processor = SecureDataProcessor()
# result = processor.process_user_data("safe user input")  # Secure processing
# handled = processor.handle_data("test data")  # Safe data handling
# validation = processor.validate_input_safety(user_input)  # Input validation

Cite this entry

@misc{vaitp:cve20181000518,
  title        = {{Websockets v4 improper handling of compressed data DoS via memory exhaustion}},
  author       = {Bogaerts, Fr\'ed\'eric and Ivaki, Naghmeh and Fonseca, Jos\'e},
  year         = {2018},
  note         = {VAITP Python Vulnerability Dataset, entry CVE-2018-1000518},
  howpublished = {\url{https://netpack.pt/vaitp/vulnerability/CVE-2018-1000518/}}
}
Introducing the "VAITP dataset": a specialized repository of Python vulnerabilities and patches, meticulously compiled for the use of the security research community. As Python's prominence grows, understanding and addressing potential security vulnerabilities become crucial. Crafted by and for the cybersecurity community, this dataset offers a valuable resource for researchers, analysts, and developers to analyze and mitigate the security risks associated with Python. Through the comprehensive exploration of vulnerabilities and corresponding patches, the VAITP dataset fosters a safer and more resilient Python ecosystem, encouraging collaborative advancements in programming security.

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