Security Scan Report: dexfi-next-js.vercel.app

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Submitted: Oct 16, 2025, 5:40:07 PMCompleted: Oct 16, 2025, 5:41:14 PMpubliccompleted
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Summary

This website contacted 2 IPs in 1 country across 1 domain to perform 30 HTTP transactions. The main domain is dexfi-next-js.vercel.app.

Submitted URL: https://dexfi-next-js.vercel.app/

AI Security Verdict

High Risk

Confidence: 92%

8
Risk Score

Impersonates PancakeSwap on an unranked Vercel domain; likely phishing.

Risk Factors
Brand impersonation on untrusted domain
Unranked domain claiming major brand
New/unknown domain age
Potential wallet‑connection phishing lure
Domain age information unavailable

Details

Page Title

BunnySwap

Scan Type

public

Language

🇺🇸

English

(50% confidence)

Category

cryptocurrency

(30%)

Domain Information

Within the application-focused generic top-level domain (.app), 'dexfi-next-js.vercel.app' is registered and includes subdomain 'dexfi-next-js'. The core label 'vercel' covers 6 characters holding 2 vowels versus four consonants. Tokenizing the label suggests 2 words: ver, cel. Median word length is 3 characters. The linguistic tilt is Portuguese for 'ver'. You will also see it in Portuguese (Brazil) and Galician contexts.

Screenshot

Security scan screenshot of https://dexfi-next-js.vercel.app/

Page Load Overview

27.13s
Total Load Time
30
HTTP Requests
1
Domains
16 KB
Total Size

Language Analysis

Primary Language

🇺🇸English
Code: en
Confidence:50%
Script:Latin
Direction:ltr

Detection Details

Language Code:en
Detection Confidence:50%
Script Type:Latin
Text Length:998 chars
Detector Agreement:100%

Website Classification

Primary Category

cryptocurrency30% confidence
Type: dynamic
Method: ml+structural

All Detected Categories

cryptocurrency
30%
cryptocurrency blockchain
30%

Detected Features

No structural features detected

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
15216.198.79.195United States
AS16509AMAZON-02
1564.29.17.195United States
AS16509AMAZON-02
302--

Detected Technologies3

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T1D3031930D74061BF54238B99FA21B7D8501FB41AEB779799B258CA60B38FCB78D121E1

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

768:IVeXDYjHYjQYjDYj3Yj0YjmYjOTYsls4qAuOrPYzvKLf9ZkslFl:GTL5uOUzcT3zl

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:39142:GgMkDQEEhgBmlkEcTA4VVIISICyWC0QoKpJ6AOSRBNLHAAO8siKAEAAETYQApBCQBQOXcG+ChDQECDIGCEwI3CQDOBgMKiAA

These hashes enable detection of similar websites and malware variants by comparing content similarity even when exact matches aren't found.

Image Hashes

Perceptual Hashes

Average Hash:fef3d3939391ffff
Perceptual Hash:edf4934b34924d64
Difference Hash:e85636362632db27
Wavelet Hash:7ef282918190f1f7
Color Hash:#2d8646

Other Hashes

Scan History

Scan history not available

Unable to load historical scan data