sensitive-data-detection

title

Sensitive Data Detection

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Sensitive Data Detection

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description

Automatically detect and flag sensitive data in AI agent tool calls

keywords

security

sensitive data

credentials

secrets

compliance

audit

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

Provider	Pattern	Severity
AWS Access Key ID	`AKIA[0-9A-Z]{16}`	critical
AWS Secret Access Key	40-character base64 strings	critical
GCP API Key	`AIza[0-9A-Za-z-_]{35}`	critical
GCP Service Account	JSON with `type: service_account`	critical
Azure Storage Key	Base64 storage account keys	critical
Azure Connection String	`DefaultEndpointsProtocol=...`	critical

Private Keys

Key Type	Detection Method	Severity
RSA Private Key	`-----BEGIN RSA PRIVATE KEY-----`	critical
EC Private Key	`-----BEGIN EC PRIVATE KEY-----`	critical
DSA Private Key	`-----BEGIN DSA PRIVATE KEY-----`	critical
OpenSSH Private Key	`-----BEGIN OPENSSH PRIVATE KEY-----`	critical
PGP Private Key	`-----BEGIN PGP PRIVATE KEY BLOCK-----`	critical
PKCS8 Private Key	`-----BEGIN PRIVATE KEY-----`	critical
Encrypted Private Key	`-----BEGIN ENCRYPTED PRIVATE KEY-----`	high

API Tokens

Service	Pattern	Severity
GitHub Token	`ghp_`, `gho_`, `ghu_`, `ghs_`, `ghr_` prefixes	critical
GitHub Fine-grained Token	`github_pat_` prefix	critical
GitLab Token	`glpat-` prefix	critical
Stripe API Key	`sk_live_`, `sk_test_`, `rk_live_`, `rk_test_`	critical
Slack Token	`xoxb-`, `xoxp-`, `xoxa-`, `xoxr-`	critical
Slack Webhook	`hooks.slack.com/services/` URLs	high
SendGrid API Key	`SG.` prefix with base64	critical

LLM/AI Provider API Keys

Provider	Pattern	Severity
OpenAI	`sk-`, `sk-proj-`, `sk-svcacct-`, `sk-admin-` prefixes	critical
Anthropic	`sk-ant-api03-`, `sk-ant-admin01-` prefixes	critical
Google AI/Gemini	`AIzaSy` prefix (39 chars)	critical
xAI/Grok	`xai-` prefix (48+ chars)	critical
Groq	`gsk_` prefix (52 chars)	critical
Hugging Face	`hf_` prefix (37 chars)	critical
Hugging Face Org	`api_org_` prefix	critical
Replicate	`r8_` prefix (40 chars)	critical
Perplexity	`pplx-` prefix (53 chars)	critical
Fireworks AI	`fw_` prefix (20+ chars)	critical
Anyscale	`esecret_` prefix	critical
Mistral AI	Keyword context required	high
Cohere	Keyword context required	high
DeepSeek	`sk-` with keyword context	high
Together AI	Keyword context required	high

Database Credentials

Database	Pattern	Severity
MySQL	`mysql://user:pass@host`	critical
PostgreSQL	`postgres://user:pass@host`	critical
MongoDB	`mongodb://user:pass@host` or `mongodb+srv://`	critical
Redis	`redis://user:pass@host` or `rediss://`	high
Generic JDBC	`jdbc:` URLs with credentials	high

Credit Cards

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

Card Type	Pattern	Severity
Visa	4xxx-xxxx-xxxx-xxxx	high
Mastercard	5[1-5]xx-xxxx-xxxx-xxxx	high
American Express	3[47]xx-xxxxxx-xxxxx	high
Discover	6011-xxxx-xxxx-xxxx	high

:::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:

Type	Characteristics	Severity
Base64 Secrets	High entropy, 20+ chars, base64 charset	medium
Hex Secrets	High entropy, 32+ chars, hex charset	medium
Random Tokens	High entropy, mixed alphanumeric	low

Sensitive File Paths

Detection of file paths that typically contain sensitive data:

Category	Examples	Severity
SSH Keys	`~/.ssh/id_rsa`, `~/.ssh/id_ed25519`	high
Cloud Credentials	`~/.aws/credentials`, `~/.config/gcloud/`	high
Environment Files	`.env`, `.env.local`, `.env.production`	medium
Key Files	`.pem`, `.key`, `.p12`, `.pfx`	high
Kubernetes Secrets	`kubeconfig`, `~/.kube/config`	high

Detection Categories and Severities

Category	Description
`cloud_credentials`	AWS, GCP, Azure credentials
`private_key`	RSA, EC, DSA, OpenSSH, PGP private keys
`api_token`	GitHub, GitLab, Stripe, Slack, OpenAI tokens
`auth_token`	JWT, Bearer tokens, session tokens
`sensitive_file`	Paths to credential files
`database_credential`	Database connection strings with passwords
`high_entropy`	Suspicious high-entropy strings
`credit_card`	Credit card numbers (Luhn validated)

Severities

Severity	Description	Action
`critical`	Direct credential exposure, immediate risk	Investigate immediately
`high`	Sensitive data that could enable access	Review within 24 hours
`medium`	Potentially sensitive, context-dependent	Review during audit
`low`	Informational, may be false positive	Monitor trends

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

Navigate to Activity Log in the sidebar
Use the Sensitive Data filter dropdown to show only activities with detections
Filter by severity level (critical, high, medium, low)
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
    }
  }
}

Setting	Default	Description
`enabled`	true	Enable/disable sensitive data detection
`scan_arguments`	true	Scan tool call arguments
`scan_responses`	true	Scan tool call responses
`severity_threshold`	"low"	Minimum severity to log (low, medium, high, critical)
`categories.*`	true	Enable/disable specific detection categories

See Configuration for complete reference.

Cross-Platform Support

Sensitive file path detection adapts to the operating system:

Platform	Path Patterns
macOS	`~/Library/`, `~/.ssh/`, `~/.aws/`, `~/.config/`
Linux	`~/.ssh/`, `~/.aws/`, `~/.config/`, `/etc/ssl/private/`
Windows	`%USERPROFILE%\.ssh\`, `%USERPROFILE%\.aws\`, `%APPDATA%\`

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:

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

Prevention Recommendations

Use Docker isolation for untrusted servers with network_mode: "none"
Enable quarantine for new servers added by AI agents
Review tool descriptions for potential data exfiltration patterns
Set up alerts for critical severity detections
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

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Sensitive Data Detection

Overview

Supported Detection Types

Cloud Credentials

Private Keys

API Tokens

LLM/AI Provider API Keys

Database Credentials

Credit Cards

High-Entropy Strings

Sensitive File Paths

Detection Categories and Severities

Categories

Severities

Activity Log Integration

Web UI Usage

Filtering by Sensitive Data

Detection Indicators

Detail View

CLI Usage

List Activities with Sensitive Data

View Detection Details

Export for Compliance

Summary Statistics

Configuration

Cross-Platform Support

Security Best Practices

Compliance Auditing

Real-time Monitoring

Integration with SIEM

Incident Response

Prevention Recommendations

Related Features

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Sensitive Data Detection

Overview

Supported Detection Types

Cloud Credentials

Private Keys

API Tokens

LLM/AI Provider API Keys

Database Credentials

Credit Cards

High-Entropy Strings

Sensitive File Paths

Detection Categories and Severities

Categories

Severities

Activity Log Integration

Web UI Usage

Filtering by Sensitive Data

Detection Indicators

Detail View

CLI Usage

List Activities with Sensitive Data

View Detection Details

Export for Compliance

Summary Statistics

Configuration

Cross-Platform Support

Security Best Practices

Compliance Auditing

Real-time Monitoring

Integration with SIEM

Incident Response

Prevention Recommendations

Related Features