ABUSE.MOM
THREAT REPORT

IP Threat Report
45.189.141.64

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-22 13:12:09
First seen: 2026-03-06 08:00:05
Last seen: 2026-03-06 08:00:05
68

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 68/100. Total malicious requests observed: 2.

DANGER_PATHRATIO_404METHOD
01

Geolocation & Classification

IP Address
45.189.141.64
Type
Residential
Country
🇧🇷 Brazil
City
Pedro Afonso
ISP
DOBLER NET TELECOMUNICAÇÕES LTDA
Organization
DOBLER NET TELECOMUNICAÇÕES LTDA
Autonomous System
AS269591 DOBLER NET TELECOMUNICAÇÕES LTDA
Hit Count
2
02

Detection Signatures

SignatureDescriptionPointsSeverity
Danger strong hits: 1High-risk paths: shells, RCE vectors, exploits+25
Danger medium hits: 1Medium-risk: admin panels, config files+10
404 ratio >= 60%Majority of requests returned 404 — enumeration+25
POST requests presentBehavioral anomaly detected by automated analysis+8
Σ = 68
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

2026-03-06 08:00:05
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
Danger strong hits: 1 (+25), Danger medium hits: 1 (+10), 404 ratio >= 60% (+25)
2026-03-06 08:00:05
Last malicious request observed
Total score reached: 68/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

DOBLER NET TELECOMUNICAÇÕES LTDA
AS269591 · 🇧🇷 Brazil
06

Recommendations

Actions taken & recommended

  • IP 45.189.141.64 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 45.189.141.64 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
Spamhaus ZEN

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

10

Threat Analysis

45.189.141.64 has been assigned a threat score of 68/100 (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 45.189.141.64 to malicious activity originating from Pedro Afonso, Brazil, operating on the network of DOBLER NET TELECOMUNICAÇÕES LTDA. The address has been under observation since its initial detection. During its 1-day observation window, we recorded 2 hostile requests from this IP — roughly 2 per day on average. Operating from a residential network, this IP may represent a compromised home gateway or IoT device that has been drafted into a larger attack infrastructure. Active path scanning has been detected — this IP probes for hundreds of common file and directory names. Brazil currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. At 68/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.

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

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🏢 Same network: AS269591

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12

Security Intelligence

💡 Remote Code Execution (RCE)

RCE vulnerabilities allow attackers to execute arbitrary code on target servers. These critical flaws often arise from deserialization bugs, template injection, or file upload vulnerabilities, and represent the highest severity class of web application weaknesses.

💡 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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