# Phishing & Domain Security Detector (`ntriqpro/phish-radar`) Actor

Check whether a domain is a phishing site or legitimate: threat-feed lookups, fraud pattern checks and look-alike domain detection, returned as a clear verdict per URL. Built for SOC analysts, compliance teams and brand-protection monitoring.

- **URL**: https://apify.com/ntriqpro/phish-radar.md
- **Developed by:** [Daniel Carter](https://apify.com/ntriqpro) (community)
- **Categories:** Developer tools, AI, Automation
- **Features:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $26.00 / 1,000 phishing detection scans

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Phish Radar - Phishing Detection & Brand Monitoring MCP Server

Real-time phishing detection and brand monitoring system for AI agents. Analyzes URLs and domains for phishing indicators, typosquatting, homoglyphs, and lookalike domains.

### Features

#### 1. **Phishing Detection** (`phishing_detect`)

Comprehensive URL/domain analysis with:

- **Typosquatting detection** - Identifies domains similar to known brands (Levenshtein distance)
- **Homoglyph detection** - Catches visually similar character substitutions (0/O, 1/l/I, etc.)
- **Domain age analysis** - Detects recently registered suspicious domains
- **SSL certificate inspection** - Flags free certificates (Let's Encrypt) when combined with brand similarity
- **DNS records** - Checks for MX, SPF, DKIM, DMARC records
- **Hosting location** - Identifies suspicious geography patterns

**Score: 0-100 phishing confidence**

- **PHISHING (70+)**: Block immediately
- **SUSPICIOUS (40-69)**: Review recommended
- **SAFE (<40)**: Low risk

#### 2. **Domain Trust Score** (`domain_trust`)

Evaluates domain trustworthiness (0-100) based on:

- Email infrastructure (MX, SPF, DKIM, DMARC) - 25 points
- SSL certificate type (EV > OV > DV) - 20 points
- TLD reputation - 20 points
- Nameserver reputation - 15 points
- DNS records - 5 points

**Levels:**

- **HIGHLY_TRUSTED (80+)**: Enterprise-grade domain
- **TRUSTED (60-79)**: Legitimate domain
- **NEUTRAL (40-59)**: Unproven domain
- **SUSPICIOUS (20-39)**: Warning signs present
- **UNTRUSTED (<20)**: High-risk domain

#### 3. **Brand Monitor** (`brand_monitor`)

Detects lookalike domains targeting your brand:

- **Typosquats** - Single character swaps/deletions (paypa1.com vs paypal.com)
- **Homoglyphs** - Visually confusing substitutions
- **Keyword additions** - Security-related additions (paypal-secure.com)
- **TLD variations** - Same domain on different extensions (paypal.xyz)

Returns high-risk domains that actually exist with full risk assessment.

### Usage

Give the Actor a list of domains or URLs in `targets` and pick a `mode`. Up to 50 targets per run; anything beyond that is reported in a notice row instead of being dropped silently.

Targets may be bare domains (`stripe.com`) or full URLs (`https://www.stripe.com/login`) — URLs are reduced to the hostname before lookup. If your client sends the list under a different key, it is still read: `target`, `url`, `urls`, `startUrls`, `domain`, `domains`, `brand_domain`, `query`. The legacy MCP shape `{"toolName": ..., "input": {...}}` keeps working unchanged.

#### Example 1: Full report (default)

`full` runs phishing detection and trust scoring on the same target and merges them into one row. The console example run ("Example run input") prefills 12 targets so a fresh start returns a useful multi-row report rather than a single sample; pass your own list in `targets` to override it.

```json
{
  "targets": ["paypa1-secure.com", "stripe.com", "github.com"],
  "mode": "full"
}
```

**One row per target:**

```json
{
  "tool": "full",
  "url": "paypa1-secure.com",
  "domain": "paypa1-secure.com",
  "verdict": "SUSPICIOUS",
  "confidence": 0.95,
  "phishing_score": 60,
  "signals": {
    "typosquat_of": "paypal.com",
    "typosquat_distance": 1,
    "homoglyph_detected": false,
    "homoglyph_count": 0,
    "domain_age_days": null,
    "ssl_issuer": null,
    "ssl_free_cert": false,
    "has_mx_records": false,
    "suspicious_tld": false,
    "dns_a_records": [],
    "hosting_country": "UNKNOWN",
    "domain_exists": false,
    "has_ssl": false
  },
  "action": "REVIEW",
  "details": "Suspicious domain characteristics detected (score: 60). Resembles legitimate domain paypal.com.",
  "trust_score": 20,
  "trust_level": "SUSPICIOUS",
  "trust_factors": {
    "has_mx": false,
    "has_spf": false,
    "has_dkim": false,
    "has_dmarc": false,
    "ssl_type": "NONE",
    "ssl_issuer": "None",
    "has_ssl": false,
    "tld_risk": "LOW",
    "nameservers": [],
    "a_records_count": 0,
    "resolves": false
  },
  "timestamp": "2026-09-10T07:46:54.982Z"
}
```

`domain_age_days` is always `null` — the Actor does not query WHOIS. `hosting_country` is a coarse IP-range guess, not a GeoIP lookup.

#### Example 2: Domain trust only

```json
{
  "targets": ["github.com"],
  "mode": "domain_trust"
}
```

**Response row:**

```json
{
  "tool": "domain_trust",
  "domain": "github.com",
  "trust_score": 100,
  "trust_level": "HIGHLY_TRUSTED",
  "factors": {
    "has_mx": true,
    "has_spf": true,
    "has_dkim": true,
    "has_dmarc": true,
    "ssl_type": "DV",
    "ssl_issuer": "Sectigo Public Server Authentication CA DV E36",
    "has_ssl": true,
    "tld_risk": "LOW",
    "nameservers": ["ns-1283.awsdns-32.org", "dns1.p08.nsone.net"],
    "a_records_count": 4,
    "resolves": true
  },
  "timestamp": "2026-09-10T07:48:11.204Z"
}
```

#### Example 3: Monitor brand lookalikes

`brand_monitor` returns **one row per live lookalike domain**, sorted with the highest risk first.

```json
{
  "targets": ["stripe.com"],
  "mode": "brand_monitor",
  "lookalikeLimit": 20
}
```

**Response rows:**

```json
{
  "tool": "brand_monitor",
  "brand": "stripe.com",
  "brand_name": "stripe",
  "lookalikes_found": 8,
  "domain": "5tripe.com",
  "risk": "HIGH",
  "squat_type": "homoglyph",
  "tld_risk": "LOW",
  "exists": true,
  "timestamp": "2026-09-10T07:52:03.771Z"
}
```

Only domains that currently resolve in DNS are returned — parked and unregistered candidates are dropped.

#### Legacy MCP format

The original nested format still works unchanged:

```json
{
  "toolName": "domain_trust",
  "input": { "domain": "example.com" }
}
```

### Implementation Details

#### No External API Dependencies

- **DNS Queries**: Native Node.js `dns/promises` module
- **SSL Certificates**: Node.js `tls` module for direct connections
- **WHOIS**: Direct TCP port 43 protocol (future enhancement)
- **GeoIP**: Simplified IP-to-country mapping (for production, integrate MaxMind)

#### Known Brands Database

Includes 80+ major brands across:

- Financial Services (PayPal, Stripe, Square, etc.)
- Tech Companies (Apple, Google, Microsoft, etc.)
- Cryptocurrency (Coinbase, Kraken, Binance, etc.)
- Cloud Providers (AWS, Azure, Google Cloud, etc.)
- E-commerce (Amazon, eBay, Shopify, etc.)

Easily extensible in `src/data/known_brands.js`.

#### Suspicious TLDs

Monitors 40+ high-risk TLDs commonly used in phishing:

- Cheap registration TLDs: `.xyz`, `.top`, `.click`, `.bid`
- Free TLDs: `.tk`, `.ml`, `.ga`, `.cf`
- Cryptocurrency scams: `.btc`, `.eth`, `.crypto`
- Sanctioned regions: `.ru`, `.ir`, `.kp`

### Phishing Score Calculation

#### High-Risk Signals (Add Points)

- Typosquat distance 1: +35 points
- Typosquat distance 2: +25 points
- Homoglyph detected: +20 points
- Suspicious TLD: +15 points
- Free SSL + typosquat: +15 points
- No MX + typosquat: +10 points
- Hosting in RU + typosquat: +10 points

#### Low-Risk Signals (Subtract Points)

- Has MX records: -5 points
- Trusted CA cert: -5 points

**Final Score Clamped: 0-100**

### Confidence Levels

- **70+ (PHISHING)**: 0.70-0.99 confidence → **BLOCK**
- **40-69 (SUSPICIOUS)**: 0.50-0.95 confidence → **REVIEW**
- **<40 (SAFE)**: 0.95+ confidence → **ALLOW**

### Pricing

- **$0.05 per target analyzed** — one `tool-call` event per entry in `targets`.
- A run with 10 domains costs $0.50, whatever mode you choose.
- `brand_monitor` charges **per brand**, not per lookalike: one brand returning 20 lookalike rows is still $0.05.
- Status rows (for example "no target supplied") are never charged.
- Each target gets 25 seconds. A target that does not answer DNS or TLS in time returns an `ERROR` row saying so, and that row is charged like any other delivered result — it keeps one dead target from starving the rest of your list.

### Implementation Notes

#### Security Considerations

- **No WHOIS PII collection**: Personal information (owner names, emails) excluded per GDPR
- **No certificate validation bypass**: Uses system trust stores
- **Rate limiting**: Implement externally to avoid DNS query floods
- **Timeout protection**: 3-second SSL connection timeout, 3-second DNS query timeout, 25-second per-target deadline

#### Performance

- Parallel DNS queries for speed
- Batch processing for large domain lists (10 concurrent)
- Caching at application level (not in Actor)

#### Future Enhancements

1. **WHOIS Integration**: For domain registration date analysis
2. **GeoIP Database**: MaxMind for accurate IP location
3. **SSL Certificate History**: Certificate Transparency logs
4. **Reputation Scoring**: Integration with Phishing/Malware databases
5. **Machine Learning**: Pattern recognition for new phishing techniques

### API Signature

#### Request format

```json
{
  "targets": ["example.com"],
  "mode": "full|phishing_detect|domain_trust|brand_monitor",
  "lookalikeLimit": 20
}
```

The MCP format (`toolName` + nested `input`) is still accepted.

#### Error row

A target that fails analysis still returns a row, so you always know what happened to each entry:

```json
{
  "tool": "full",
  "domain": "example.com",
  "verdict": "ERROR",
  "error": "Error description",
  "timestamp": "2026-09-10T07:48:11.204Z"
}
```

### Testing

```bash
npm install

## Local run with a fixed input
mkdir -p storage/key_value_stores/default
echo '{"targets":["paypa1-secure.com"],"mode":"full"}' > storage/key_value_stores/default/INPUT.json
CRAWLEE_STORAGE_DIR=./storage node src/main.js
cat storage/datasets/default/*.json

## On the platform
apify call phish-radar --input input.json
```

### Maintenance

- Update `known_brands.js` quarterly with emerging platforms
- Monitor `suspicious_tlds.js` for new high-abuse extensions
- Review phishing signal weights annually based on threat patterns

### Support

For brand additions or TLD updates, modify:

- Brand list: `src/data/known_brands.js`
- TLD list: `src/data/suspicious_tlds.js`

***

**Version**: 1.0
**Node.js**: 18+
**License**: Private (ntriq)

***

### 🔗 Related Actors by ntriqpro

Build your data pipeline with the ntriqpro Actor suite:

- [**maigret-actor**](https://apify.com/ntriqpro/maigret-actor) — Maigret OSINT — Username search
- [**website-tech-detector**](https://apify.com/ntriqpro/website-tech-detector) — Website Tech Stack Scanner
- [**eu-sanctions-monitor**](https://apify.com/ntriqpro/eu-sanctions-monitor) — EU Sanctions Screening — KYC/AML

### ⭐ Love it? Leave a Review

Your rating helps professionals discover this actor. [Rate it here](https://apify.com/ntriqpro/phish-radar/reviews).

### Disclaimer

**RISK ASSESSMENT DISCLAIMER**: This tool provides data-driven risk analysis based on publicly available and curated sources. Assessments are indicative only and **NOT** professional risk management advice. You must engage qualified professionals (risk managers, insurance brokers, compliance officers) to validate findings before making investment or operational decisions. Risk scores do not constitute investment advice or due diligence completion. We assume no liability for business decisions based on this tool's output.

# Actor input Schema

## `targets` (type: `array`):

Domains or URLs to analyze. One analysis is charged per target (max 50 per run). A 12-target default is prefilled so a fresh run returns a useful multi-row report instead of a single sample.

## `mode` (type: `string`):

full = phishing verdict + trust score in one row (recommended). brand_monitor = find live lookalike domains for your brand, one row per lookalike.

## `lookalikeLimit` (type: `integer`):

Max lookalike domains returned per brand in brand_monitor mode.

## `toolName` (type: `string`):

Deprecated. Kept so existing MCP callers keep working; use Analysis mode instead.

## `input` (type: `object`):

Deprecated. Nested payload for existing MCP callers, e.g. {"domain": "example.com"}.

## Actor input object example

```json
{
  "targets": [
    "paypa1-secure.com",
    "stripe.com",
    "github.com",
    "google.com",
    "paypal.com",
    "amazon.com",
    "5tripe.com",
    "netflix.com",
    "facebook.com",
    "microsoft.com",
    "apple.com",
    "binance.com"
  ],
  "mode": "full",
  "lookalikeLimit": 20
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "targets": [
        "paypa1-secure.com",
        "stripe.com",
        "github.com",
        "google.com",
        "paypal.com",
        "amazon.com",
        "5tripe.com",
        "netflix.com",
        "facebook.com",
        "microsoft.com",
        "apple.com",
        "binance.com"
    ],
    "mode": "full",
    "lookalikeLimit": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("ntriqpro/phish-radar").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "targets": [
        "paypa1-secure.com",
        "stripe.com",
        "github.com",
        "google.com",
        "paypal.com",
        "amazon.com",
        "5tripe.com",
        "netflix.com",
        "facebook.com",
        "microsoft.com",
        "apple.com",
        "binance.com",
    ],
    "mode": "full",
    "lookalikeLimit": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("ntriqpro/phish-radar").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "targets": [
    "paypa1-secure.com",
    "stripe.com",
    "github.com",
    "google.com",
    "paypal.com",
    "amazon.com",
    "5tripe.com",
    "netflix.com",
    "facebook.com",
    "microsoft.com",
    "apple.com",
    "binance.com"
  ],
  "mode": "full",
  "lookalikeLimit": 20
}' |
apify call ntriqpro/phish-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,ntriqpro/phish-radar"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/k5VmDMSypydtAhvBC/builds/bUh4hHdru8nziL5r5/openapi.json