Sensitive Data Detection

MCPProxy includes automatic sensitive data detection to identify potential credential leakage, secrets exposure, and other security risks in AI agent tool calls. This feature helps protect against Tool Poisoning Attacks (TPA) and provides compliance auditing capabilities.

Overview

When AI agents interact with MCP tools, they may inadvertently expose sensitive information such as:

• Credentials passed in tool arguments or returned in responses
• API tokens leaked through error messages or debug output
• Private keys embedded in configuration data
• Database connection strings with embedded passwords

MCPProxy scans all tool call arguments and responses for sensitive data patterns, logging detections in the activity log for security review and compliance auditing.

Supported Detection Types

Cloud Credentials

Private Keys

API Tokens

LLM/AI Provider API Keys

Database Credentials

Credit Cards

Credit card numbers are detected using pattern matching combined with Luhn algorithm validation:

:::note Luhn Validation Credit card detection includes Luhn checksum validation to reduce false positives from random 16-digit numbers. :::

High-Entropy Strings

Strings with high Shannon entropy that may indicate secrets:

Sensitive File Paths

Detection of file paths that typically contain sensitive data:

Detection Categories and Severities

Categories

Severities

Activity Log Integration

When sensitive data is detected, it is recorded in the activity log metadata:

{
  "id": "01JFXYZ123ABC",
  "type": "tool_call",
  "server_name": "filesystem-server",
  "tool_name": "read_file",
  "status": "success",
  "timestamp": "2025-01-15T10:30:00Z",
  "metadata": {
    "sensitive_data_detected": true,
    "sensitive_data": [
      {
        "type": "aws_access_key",
        "category": "cloud_credentials",
        "severity": "critical",
        "location": "response",
        "context": "AKIA...XXXX (redacted)"
      },
      {
        "type": "private_key",
        "category": "private_key",
        "severity": "critical",
        "location": "response",
        "context": "RSA PRIVATE KEY detected"
      }
    ]
  }
}

:::caution Redaction Detected sensitive values are automatically redacted in the activity log to prevent secondary exposure. Only the type, category, and partial context are stored. :::

Web UI Usage

The Activity Log page in the web UI provides filtering and visualization for sensitive data detections.

Filtering by Sensitive Data

1. Navigate to Activity Log in the sidebar
2. Use the Sensitive Data filter dropdown to show only activities with detections
3. Filter by severity level (critical, high, medium, low)
4. Click on an activity row to view detection details

Detection Indicators

Activities with sensitive data detections are marked with visual indicators:

• Red shield icon for critical severity
• Orange warning icon for high severity
• Yellow info icon for medium severity
• Gray info icon for low severity

Detail View

Clicking on an activity with detections shows:

• List of all detected sensitive data types
• Location (arguments or response)
• Redacted context for verification
• Timestamp and duration

CLI Usage

List Activities with Sensitive Data

# Show all activities with sensitive data detections
mcpproxy activity list --sensitive-data

# Filter by severity
mcpproxy activity list --sensitive-data --severity critical

# Combine with other filters
mcpproxy activity list --sensitive-data --server github-server --status success

View Detection Details

# Show full details including sensitive data metadata
mcpproxy activity show 01JFXYZ123ABC

# JSON output for scripting
mcpproxy activity show 01JFXYZ123ABC --output json

Export for Compliance

# Export activities with sensitive data for security review
mcpproxy activity export --sensitive-data --output security-audit.jsonl

# Export critical severity only
mcpproxy activity export --sensitive-data --severity critical --output critical-findings.jsonl

Summary Statistics

# Show sensitive data detection summary
mcpproxy activity summary --period 24h

# Output includes detection counts by category and severity

Configuration

Sensitive data detection is enabled by default. Configure via mcp_config.json:

{
  "sensitive_data_detection": {
    "enabled": true,
    "scan_arguments": true,
    "scan_responses": true,
    "severity_threshold": "low",
    "categories": {
      "cloud_credentials": true,
      "private_key": true,
      "api_token": true,
      "auth_token": true,
      "sensitive_file": true,
      "database_credential": true,
      "high_entropy": true,
      "credit_card": true
    }
  }
}

See Configuration for complete reference.

Cross-Platform Support

Sensitive file path detection adapts to the operating system:

Path detection normalizes separators and expands home directory references for consistent cross-platform detection.

Security Best Practices

Compliance Auditing

Use sensitive data detection for regular security audits:

# Weekly security audit export
mcpproxy activity export \
  --sensitive-data \
  --start-time "$(date -v-7d +%Y-%m-%dT00:00:00Z)" \
  --output weekly-security-audit.jsonl

# Generate summary report
mcpproxy activity summary --period 7d --output json > weekly-summary.json

Real-time Monitoring

Monitor for critical detections in real-time:

# Watch for sensitive data detections
mcpproxy activity watch --sensitive-data --severity critical

Integration with SIEM

Export activity logs for integration with Security Information and Event Management (SIEM) systems:

# Continuous export for SIEM ingestion
mcpproxy activity export --format json --output - | \
  your-siem-forwarder --input -

Incident Response

When a critical detection is identified:

1. Review the activity: mcpproxy activity show <id>
2. Identify the source: Check server name and tool name
3. Assess impact: Determine if credentials were exposed externally
4. Rotate credentials: If exposed, rotate the affected credentials immediately
5. Investigate root cause: Review how sensitive data entered the tool call

Prevention Recommendations

1. Use Docker isolation for untrusted servers with network_mode: "none"
2. Enable quarantine for new servers added by AI agents
3. Review tool descriptions for potential data exfiltration patterns
4. Set up alerts for critical severity detections
5. Regular audits of activity logs for security compliance

Related Features

• Activity Log - Core activity logging functionality
• Security Quarantine - Protection against Tool Poisoning Attacks
• Docker Isolation - Container-based server isolation
• Intent Declaration - Track operation types and data sensitivity