ABUSE.MOM
THREAT REPORT

IP Threat Report
105.172.114.11

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-07-29 18:49:34
First seen: 2026-06-28 15:09:25
Last seen: 2026-07-05 04:01:32
60

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 60/100. Total malicious requests observed: 630.

DANGER_PATHRATIO_404REFERER
01

Geolocation & Classification

IP Address
105.172.114.11
Type
Mobile
Country
🇦🇴 AO
City
Luanda
ISP
UNITEL SA
Organization
UnitelNetworkPool3
Autonomous System
AS37119 UNITEL SA
Hit Count
630
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

2026-06-28 15:09:25
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)
2026-07-05 04:01:32
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

UNITEL SA
AS37119 · 🇦🇴 AO
06

Recommendations

Actions taken & recommended

  • IP 105.172.114.11 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

🔎 Path Enumeration Protection

Block scanning from 105.172.114.11: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.

09

Blacklist Status (DNSBL)

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

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

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

10

Threat Analysis

105.172.114.11 has been assigned a threat score of 60/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.

The following attack categories were identified:

Path Enumeration

📊 Threat Analysis

Network traffic from 105.172.114.11, located in Luanda, AO, operating on the network of UNITEL SA, has been classified as malicious by our automated threat scoring engine. The address has been active for 6 days in our monitoring system, producing 630 flagged requests at a rate of ~105/day. This is a mobile network IP. While mobile addresses are typically shared via CGNAT, persistent malicious activity from this specific address suggests automated abuse. The IP exhibits directory enumeration behavior, systematically requesting non-existent paths to discover hidden files and misconfigured resources. Our records show 25 malicious IPs originating from AO, positioning it as a notable contributor to global threat activity. At 60/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.

11

Related Threats

🇦🇴 Top threats from AO

105.174.17.50 (125)105.168.175.155 (103)105.174.59.178 (103)196.249.229.22 (103)102.220.192.174 (103)View all →

🏢 Same network: AS37119

105.174.17.50 (125)105.168.44.104 (60)View all →
12

Security Intelligence

💡 Directory Traversal Attacks

Path traversal attacks attempt to access files outside the intended directory by manipulating file path references. Attackers use sequences like ../ to reach sensitive system files such as /etc/passwd or application configuration files.

💡 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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