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

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

Categories

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"
      }
    ]
  }
}

Redaction

A detection record itself never carries the value that triggered it — only the type, category, severity and location are stored.

Masked payloads

A detection is stored alongside the call it came from, and that call's arguments and response are kept for debugging. Serving those verbatim would mean the screen that warns you about a credential also prints it, in cleartext, next to a Copy button — into every screenshot, screen-share and exported page.

So MCPProxy masks them server-side, in the REST layer, before the record leaves the process:

• GET /api/v1/activity and GET /api/v1/activity/{id} replace every detected value inside arguments, response and error_message with a recognisable preview — AKIAIOSFODNN7EXAMPLE becomes AKIA…****. Enough to tell you which credential to rotate, not enough to use. A private key is replaced whole, envelope and body: its pattern matches only the -----BEGIN …----- line, and masking that alone would leave the key readable.
• Masking applies to records the detector flagged (has_sensitive_data: true); an unflagged payload is served unchanged. Detection runs asynchronously just after the call is recorded, so a record read in that sub-second window is not yet flagged and is served unmasked.
• The live event stream (GET /events, and therefore the tray and mcpproxy activity watch) masks unconditionally: those events are emitted at completion time, before the detector has a verdict to gate on.
• GET /api/v1/tool-calls and friends — a separate store with no detection metadata — mask unconditionally too.
• Internal _auth_* arguments (the server-edition identity MCPProxy injects into a call) are stripped from every browsing response.
• The Web UI's activity drawer and mcpproxy activity show both read the activity endpoints, so both are masked.

Full values remain reachable through one deliberate, separately-flagged surface for incident response and compliance:

mcpproxy activity export --include-bodies

That export deliberately keeps _auth_* as well: attributing a call to a user is the point of a compliance export.

Masking follows the same sensitive_data_detection categories as detection — a category you exclude is neither flagged nor masked. It does not follow the enabled flag: records flagged while detection was on stay masked after you turn it off, so disabling the feature never starts serving old credentials.

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)
• Timestamp and duration
• The request arguments and response body, with every detected value masked (AKIA…****) and both panels badged Masked — see Masked payloads

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:

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