ABUSE.MOM
THREAT REPORT

IP Threat Report
83.219.96.25

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-30 08:07:02
First seen: 2026-02-19 06:00:07
Last seen: 2026-02-19 06:00:07
255

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 255/100. Total malicious requests observed: 1.

UA_CHANGEDDANGER_PATHBURST
01

Geolocation & Classification

IP Address
83.219.96.25
Type
Residential
Country
🇯🇲 JM
City
Kingston
ISP
GSL Networks Pty LTD
Organization
GSL Networks Pty LTD
Autonomous System
AS137409 GSL Networks Pty LTD
Hit Count
1
02

Detection Signatures

SignatureDescriptionPointsSeverity
UA changed for same IPMultiple User-Agents — bot rotation technique+25
Danger strong hits: 362High-risk paths: shells, RCE vectors, exploits+100
Danger medium hits: 959Medium-risk: admin panels, config files+60
Burst: 10 req / 2sAbnormally fast request rate — automated scanning+35
Burst: 36 req / 10sAbnormally fast request rate — automated scanning+35
Σ = 255
03

Observed Activity

Reconstructed HTTP requests from server access logs. Target domains redacted for security.

[redacted]
GET
/
200
[redacted]
GET
/page
200
Requests shown: 2 · HTTP 404: 0 · Dangerous patterns: 0

* Typical request patterns for detected signatures. Actual target domains are redacted.

04

Timeline

2026-02-19 06:00:07
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
UA changed for same IP (+25), Danger strong hits: 362 (+100), Danger medium hits: 959 (+60)
2026-02-19 06:00:07
Last malicious request observed
Total score reached: 255/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

GSL Networks Pty LTD
AS137409 · 🇯🇲 JM
06

Recommendations

Actions taken & recommended

  • IP 83.219.96.25 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

🤖 User-Agent Anomaly Defense

IP 83.219.96.25 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.

🌊 Flood / DDoS Mitigation

Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 83.219.96.25.

09

Blacklist Status (DNSBL)

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

✓ Clean
Spamhaus ZEN

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

10

Threat Analysis

83.219.96.25 has been assigned a threat score of 255/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.

The following attack categories were identified:

User-Agent AnomalyRequest Flooding

📊 Threat Analysis

IP address 83.219.96.25 has been traced to Kingston, JM, operating on the network of GSL Networks Pty LTD. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 1 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~1 requests per day. This residential IP is likely a compromised consumer device. Home routers and IoT equipment with default credentials are prime targets for botnet operators. The dual attack vectors of User-Agent Anomaly combined with Request Flooding indicate a coordinated assault rather than opportunistic scanning. At 255/100, this is an extremely high-risk address. All traffic should be considered hostile.

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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12

Security Intelligence

💡 User-Agent Analysis Techniques

Analyzing User-Agent strings reveals automated tools masquerading as legitimate browsers. Inconsistencies between claimed browser capabilities and actual behavior, impossible version combinations, and known scanner signatures help identify malicious clients.

💡 Autonomous System Analysis

Analyzing attack patterns at the AS (Autonomous System) level reveals which networks harbor the most malicious activity. Some ASes have abuse rates orders of magnitude higher than average, indicating lax enforcement of acceptable use policies.

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