Security Scan Report: accept-ios-4f332.web.app

Redirected to:
https://www.accept.net/
Site favicon
Submitted: Completed: publiccompleted
This website contacted 20 IPs in 2 countries across 15 domains to perform 48 HTTP transactions. The main domain is web.app and was registered 7 years ago.

Submitted URL: https://accept-ios-4f332.web.app/

Effective URL:
https://www.accept.net/
Redirected

AI Security Verdict

Moderate Risk

Confidence: 55%

5
Risk Score

Entry *.web.app host carries one unverified single-source phishing report, but it redirects to the vendor's own accept.net, which serves a form-free product page with no credential, payment, hunting rule or IDS signals.

Risk Factors (3)
Single-source, unverified phishing report on the entry Firebase hosting subdomain, with no corroborating feed
The scanned URL spends its first hop on an anonymously publishable *.web.app tenant whose creation date and reputation cannot be established
Ranking and age data describe the web.app platform apex, not the domain that actually served the content
Safety Factors (5)
Final URL www.accept.net matches the brand the page presents (Accept), so no cross-brand impersonation
No password, payment or card fields anywhere on the page; the only form is an internal keyword search
No Indicators of Compromise on the served content beyond the unverified single-source report on the redirector host
No hunting rule malware patterns, no network IDS alerts, no credential exfiltration in JavaScript
Business legitimacy indicators present, including a physical address and complete pricing/support documentation
Domain age information unavailable

Details

Page Title

Accept | Stripe Card Payments | Point of Sale

Scan Type

public

Domain Name Analysis

The domain 'accept-ios-4f332.web.app' uses the application-focused generic top-level domain (.app), featuring subdomain 'accept-ios-4f332'. The core label 'web' covers 3 characters containing one vowel alongside two consonants. Splitting it apart reveals one word: web. Median word length is 3 characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://accept-ios-4f332.web.app/

Page Load Overview

4.40s
Total Load Time
1.7 MB
Total Size

Language Analysis

Primary Language

🇺🇸English
Code: en
Confidence:60%
Script:Latin
Direction:ltr

Detection Details

Text Length:1,919 chars
Detector Agreement:100%

Website Classification

Primary Category

finance banking54% confidence
Type: dynamic
Method: ml+structural

All Detected Categories

finance banking
54%
technology software
40%
corporate business
39%
e-commerce shopping
35%
social media network
34%

Detected Features

Search
OG: website

Domain & IP Information

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10199.36.158.100Fastly · CDNUnited States
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AS13335Cloudflare, Inc.
275.2.70.75Aws · CLOUDUnited States
AS16509Amazon.com, Inc.
2216.239.36.54Google · CDNUnited States
AS15169Google LLC
2172.217.112.4Google · CDNUnited States
AS15169Google LLC
2192.178.183.97Google · CDNUnited States
AS15169Google LLC
2216.239.34.36Google · CDNUnited States
AS15169Google LLC
2104.18.0.22Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
2172.217.115.4Google · CDNUnited States
AS15169Google LLC
4820--

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T1D5928537D844003343478AF4B2A17F8DA3D3C29DCE939CC566F48A8D5BD9CC889B9569

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

384:e9zrTWkDhlx64V8hTL51I5tXIF8tUMsKKc15hsPSN6/1KtziXR0ZkCHcex7tGtte:e9zrTWkDhlxXVaTLHIvYFkUMsKKc15hX

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:20933:flEgQSjyCghWMAZ1AwiEwiUGChVAIARQAMsELzUKSAlZ4gBIURpuBEaQIppJS8I6gVPCEBADOKyDYLRUxDQxjfEM+I5gaIdK

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:463818013919212d
Perceptual Hash:8eb533cc32339ccc
Difference Hash:9461b15921a14969
Wavelet Hash:573d19053d193d2d
Color Hash:#91931f

Scan History

Scan history not available

Unable to load historical scan data