ABUSE.MOM
THREAT REPORT

IP Threat Report
177.65.250.95

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-27 19:26:38
First seen: 2026-03-09 00:00:07
Last seen: 2026-04-04 23:00:05
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
177.65.250.95
Type
Residential
Country
🇧🇷 Brazil
City
Itu
ISP
Claro NXT Telecomunicacoes Ltda
Organization
Claro NXT Telecomunicacoes Ltda
Autonomous System
AS28573 Claro NXT Telecomunicacoes Ltda
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

2026-03-09 00:00:07
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)
2026-04-04 23:00:05
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

Claro NXT Telecomunicacoes Ltda
AS28573 · 🇧🇷 Brazil
06

Recommendations

Actions taken & recommended

  • IP 177.65.250.95 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 177.65.250.95 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

177.65.250.95 has been assigned a threat score of 103/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.

📊 Threat Analysis

The address 177.65.250.95 originates from Itu, Brazil, operating on the network of Claro NXT Telecomunicacoes Ltda. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 26 days, this IP generated 2 malicious requests, averaging approximately 0.1 requests per day. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. Our records show 201 malicious IPs originating from Brazil, positioning it as a significant contributor to global threat activity. With a threat score of 103/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.

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: AS28573

186.221.196.113 (143)189.62.47.54 (140)189.63.146.109 (103)201.81.52.82 (103)179.158.171.112 (103)View all →
12

Security Intelligence

💡 Vulnerability Scanning Explained

Vulnerability scanning is the automated process of probing web applications for known weaknesses. Attackers use tools like Nuclei, Nikto, and ZAP to test thousands of hosts per hour, looking for exposed configuration files, outdated software, and default credentials.

💡 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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