ABUSE.MOM
THREAT REPORT

IP Threat Report
176.29.78.188

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-27 08:00:08
First seen: 2026-03-26 08:00:07
Last seen: 2026-03-26 08:00:07
60

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 60/100. Total malicious requests observed: 1.

DANGER_PATHRATIO_404REFERER
01

Geolocation & Classification

IP Address
176.29.78.188
Type
Mobile
Country
🇯🇴 JO
City
Amman
ISP
ZAIN
Organization
Unknown
Autonomous System
AS48832 Jordanian mobile phone services Ltd
Hit Count
1
02

Detection Signatures

SignatureDescriptionPointsSeverity
Danger strong hits: 1High-risk paths: shells, RCE vectors, exploits+25
404 ratio >= 60%Majority of requests returned 404 — enumeration+25
Foreign referer seenReferer 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

2026-03-26 08:00:07
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
Danger strong hits: 1 (+25), 404 ratio >= 60% (+25), Foreign referer seen (+10)
2026-03-26 08:00:07
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

ZAIN
AS48832 · 🇯🇴 JO
06

Recommendations

Actions taken & recommended

  • IP 176.29.78.188 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 176.29.78.188 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.

⛔ LISTED
zen.spamhaus.org
✓ Clean
dnsbl.sorbs.net
✓ Clean
ix.dnsbl.manitu.net
✓ Clean
bl.spamcop.net
✓ Clean
b.barracudacentral.org
✓ Clean
truncate.gbudb.net
✓ Clean
psbl.surriel.com
✓ Clean
dnsbl-1.uceprotect.net

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

10

Threat Analysis

176.29.78.188 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

Threat intelligence analysis has linked 176.29.78.188 to malicious activity originating from Amman, JO, operating on the network of ZAIN. The address has been under observation since its initial detection. During its 1-day observation window, we recorded 1 hostile requests from this IP — roughly 1 per day on average. 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 78 malicious IPs originating from JO, positioning it as a notable 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 JO

91.186.252.91 (208)176.29.225.112 (198)91.186.255.110 (168)109.107.243.93 (128)176.28.170.254 (128)View all →

🏢 Same network: AS48832

176.29.225.112 (198)176.28.170.254 (128)176.29.249.183 (103)176.28.155.119 (83)176.28.168.249 (83)View all →
12

Security Intelligence

💡 XML External Entity (XXE) Attacks

XXE vulnerabilities in XML parsers allow attackers to read local files, perform SSRF, and execute denial of service attacks. Many legacy applications and APIs remain vulnerable to XXE due to insecure default XML parser configurations.

💡 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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