ABUSE.MOM
THREAT REPORT

IP Threat Report
42.75.154.73

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-10-26 15:35:13
First seen: 2026-10-19 07:23:53
Last seen: 2026-10-25 04:03:59
60

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 60/100. Total malicious requests observed: 119.

DANGER_PATHRATIO_404REFERER
01

Geolocation & Classification

IP Address
42.75.154.73
Type
Mobile
Country
🇹🇼 Taiwan
City
Kaohsiung
ISP
EMOME
Organization
Chunghwa Telecom Co. Ltd.
Autonomous System
AS17421 Mobile Business Group
Hit Count
119
02

Detection Signatures

SignatureDescriptionPointsSeverity
404 ratio >= 60%Majority of requests returned 404 — enumeration+25
Danger strong hits: 1High-risk paths: shells, RCE vectors, exploits+25
Foreign refererReferer from unrelated external domain+10
Σ = 60
03

Observed Activity

Reconstructed HTTP requests from server access logs. Target domains redacted for security.

[redacted]
GET
/
200
Requests shown: 1 · HTTP 404: 0 · Dangerous patterns: 0

* Typical request patterns for detected signatures. Actual target domains are redacted.

04

Timeline

2026-10-19 07:23:53
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
404 ratio >= 60% (+25), Danger strong hits: 1 (+25), Foreign referer (+10)
2026-10-25 04:03:59
Last malicious request observed
Total score reached: 60/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

EMOME
AS17421 · 🇹🇼 Taiwan
06

Recommendations

Actions taken & recommended

  • IP 42.75.154.73 is blocked at application level (HTTP 403)
  • Consider blocking at firewall level (iptables/CSF) to reduce server load
  • Report abuse to the network provider via their abuse contact
  • Ensure sensitive files (.env, .git, backups) are not accessible from the web

🔎 Directory Scan Defense

IP 42.75.154.73 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.

09

Blacklist Status (DNSBL)

This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.

✓ Clean
spam.dnsbl.sorbs.net
✓ Clean
b.barracudacentral.org
✓ Clean
dnsbl.dronebl.org
✓ Clean
bl.blocklist.de
✓ Clean
zen.spamhaus.org
✓ Clean
cbl.abuseat.org
✓ Clean
psbl.surriel.com

Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.

10

Threat Analysis

42.75.154.73 has been assigned a threat score of 60/100 (High). At this threat level, the IP is considered high risk. Firewall rules should be updated to deny traffic from this source.

The following attack categories were identified:

Path Enumeration

📊 Threat Analysis

The address 42.75.154.73 originates from Kaohsiung, Taiwan, operating on the network of EMOME. It was identified through automated analysis of incoming network traffic across monitored endpoints. During its 5-day observation window, we recorded 119 hostile requests from this IP — roughly 23.8 per day on average. The address belongs to a mobile carrier network. The sustained pattern of malicious requests indicates either a compromised device or deliberate abuse. The IP exhibits directory enumeration behavior, systematically requesting non-existent paths to discover hidden files and misconfigured resources. Our records show 405 malicious IPs originating from Taiwan, positioning it as a significant contributor to global threat activity. The score of 60/100 warrants active monitoring and rate-limiting. Full blocking is advisable for sensitive systems.

11

Related Threats

🇹🇼 Top threats from Taiwan

34.81.125.215 (298)35.201.157.167 (298)104.199.142.243 (298)35.229.133.140 (298)35.194.199.14 (298)View all →

🏢 Same network: AS17421

View all →
12

Security Intelligence

💡 Prototype Pollution Attacks

Prototype pollution manipulates JavaScript object prototypes to inject properties that affect all objects in an application. This can lead to denial of service, property injection, and in some cases remote code execution in Node.js applications.

💡 Machine Learning in Threat Detection

Machine learning models analyze vast amounts of network traffic to identify attack patterns invisible to rule-based systems. Supervised models classify known attack types while unsupervised models detect anomalies that may indicate novel threats.

🔍 Check Any IP Address

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