SQL Injection Scanning

AxonFlow provides built-in SQL injection (SQLi) detection for MCP connector responses to protect against data exfiltration and manipulation attacks.

Overview

SQL injection scanning monitors responses from MCP connectors for patterns that indicate SQL injection attempts. This protects against scenarios where:

• Malicious data injected into databases is returned in query results
• LLM-generated queries inadvertently include injection payloads
• External data sources contain compromised content

Scanning Modes

AxonFlow offers three scanning modes to balance security and performance:

Mode	Description	Edition	Performance
off	Scanning disabled	Community	N/A
basic	Pattern-based regex detection	Community	Single-digit ms
advanced	Heuristic analysis with confidence scoring	Enterprise	<10ms

Basic Mode (Community)

Basic mode uses compiled regex patterns to detect common SQL injection techniques:

• Union-based injection - UNION SELECT statements for data extraction
• Boolean-blind injection - OR 1=1 and similar always-true conditions
• Time-based blind injection - SLEEP(), WAITFOR DELAY, PG_SLEEP()
• Stacked queries - DROP TABLE, DELETE FROM, INSERT INTO
• Comment injection - SQL commands after --, /**/, or #
• Error-based injection - EXTRACTVALUE, UPDATEXML

Advanced Mode (Enterprise)

Advanced mode extends basic detection with:

• Heuristic analysis for obfuscation detection
• Confidence scoring (0.0-1.0) to reduce false positives
• Context-aware detection that recognizes documentation/code blocks
• Multi-stage pipeline for comprehensive analysis

Enterprise Edition includes additional heuristic analysis capabilities. Contact Sales for details.

Configuration

Default Configuration

sqli:
  input_mode: basic      # Scan user inputs
  response_mode: basic   # Scan connector responses
  block_on_detection: false  # Monitoring mode (set true to enforce blocking)
  log_detections: true
  audit_trail_enabled: true
  max_content_length: 1048576  # 1MB

Monitoring Mode

By default, SQL injection scanning runs in monitoring mode - detections are logged but responses are not blocked. This allows you to validate detection accuracy in your environment before enabling enforcement. Set block_on_detection: true to enable blocking.

Per-Connector Configuration

sqli:
  response_mode: basic
  connector_overrides:
    # Disable for trusted internal cache
    redis_cache:
      enabled: false
    # Use advanced mode for sensitive data (Enterprise)
    postgresql_main:
      response_mode: advanced

Programmatic Configuration

import "axonflow/platform/agent/sqli"

cfg := sqli.DefaultConfig().
    WithResponseMode(sqli.ModeBasic).
    WithBlockOnDetection(true).
    WithConnectorOverride("redis", sqli.ConnectorConfig{
        Enabled: false,
    })

middleware, err := sqli.NewScanningMiddleware(
    sqli.WithMiddlewareConfig(cfg),
)

Detection Categories

Category	Severity	Description
stacked_queries	Critical	Can modify or delete data
dangerous_query	Critical	DDL operations (DROP, TRUNCATE, ALTER, CREATE USER, GRANT)
union_based	High	Can extract sensitive data
time_based	High	Confirms SQL injection vulnerability
boolean_blind	Medium	May have false positives
error_based	Medium	Error message extraction
comment_injection	Medium	Query manipulation
generic	Low	General suspicious patterns

Response Handling

When SQL injection is detected:

1. Monitoring Mode (default): Logs detection but allows response through

   sqli:
     block_on_detection: false  # Default

2. Blocking Mode: Returns HTTP 403 Forbidden

   sqli:
     block_on_detection: true

   {
     "success": false,
     "error": "Response blocked: potential SQL injection detected (pattern: union_select)"
   }

Audit Trail

When audit_trail_enabled: true, detections are logged for compliance:

{
  "type": "sqli_detection",
  "timestamp": "2025-12-14T15:30:00Z",
  "severity": "high",
  "connector_name": "postgresql_main",
  "scan_type": "response",
  "pattern": "union_select",
  "category": "union_based",
  "confidence": 0.95,
  "blocked": true
}

Compliance Integration

SQL injection scanning supports compliance requirements:

• EU AI Act Art. 15: Accuracy logging for AI system outputs
• RBI FREE-AI: Data integrity monitoring for financial AI
• SEBI AIF: Security audit trail for investment platforms

Best Practices

1. Enable scanning by default - Use basic mode for all connectors initially
2. Tune per-connector - Disable for trusted internal services
3. Use advanced mode for sensitive data stores (Enterprise)
4. Monitor audit logs for detection patterns and false positives
5. Set appropriate thresholds - Adjust confidence threshold for advanced mode

Performance Impact

Mode	Average Latency	P99 Latency	Throughput Impact
off	0ms	0ms	0%
basic	<1ms	2ms	<1%
advanced	3-5ms	10ms	2-5%

Metrics

The middleware exposes metrics for monitoring:

metrics := sqli.GetGlobalMiddleware().GetMetrics()
// metrics.ScansTotal - Total scans performed
// metrics.DetectionsTotal - Total detections
// metrics.BlockedTotal - Total blocked responses

Troubleshooting

False Positives

If legitimate SQL content is being blocked:

1. Check if content contains SQL-like patterns (documentation, logs)
2. Consider using advanced mode for better context awareness (Enterprise)
3. Disable scanning for specific connectors with known safe data

Performance Issues

If scanning adds unacceptable latency:

1. Reduce max_content_length to limit scanned data
2. Disable scanning for high-throughput connectors
3. Use basic mode instead of advanced

Related Documentation

• PII Detection
• Security Best Practices
• MCP Connector Configuration
• Compliance Overview