ABUSE.MOM
THREAT REPORT

IP Threat Report
89.108.171.58

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-27 06:55:08
First seen: 2026-03-03 08:00:06
Last seen: 2026-03-28 04:00:07
103

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 103/100. Total malicious requests observed: 2.

DANGER_PATHMETHOD
01

Geolocation & Classification

IP Address
89.108.171.58
Type
Residential
Country
🇱🇧 LB
City
Jdaidet el Matn
ISP
Sodetel
Organization
Unknown
Autonomous System
AS31126 SODETEL S.A.L.
Hit Count
2
02

Detection Signatures

SignatureDescriptionPointsSeverity
Danger strong hits: 3High-risk paths: shells, RCE vectors, exploits+75
Danger medium hits: 2Medium-risk: admin panels, config files+20
POST requests presentBehavioral anomaly detected by automated analysis+8
Σ = 103
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

2026-03-03 08:00:06
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
Danger strong hits: 3 (+75), Danger medium hits: 2 (+20), POST requests present (+8)
2026-03-28 04:00:07
Last malicious request observed
Total score reached: 103/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

Sodetel
AS31126 · 🇱🇧 LB
06

Recommendations

Actions taken & recommended

  • IP 89.108.171.58 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

⚙️ Defensive Recommendations

Block 89.108.171.58 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.

09

Blacklist Status (DNSBL)

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

⛔ LISTED
Spamhaus ZEN

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

10

Threat Analysis

89.108.171.58 has been assigned a threat score of 103/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.

📊 Threat Analysis

The address 89.108.171.58 originates from Jdaidet el Matn, LB, operating on the network of Sodetel. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 24 days, this IP generated 2 malicious requests, averaging approximately 0.1 requests per day. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. LB currently accounts for 30 blocked IPs in our database, making it a notable source of malicious traffic. A score of 103/100 places this address in the top tier of severity. Block and investigate any historical connections.

This IP is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.

11

Related Threats

🇱🇧 Top threats from LB

213.204.92.222 (103)213.204.87.92 (103)77.235.149.20 (103)94.187.17.18 (103)185.187.94.117 (103)View all →

🏢 Same network: AS31126

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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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