ABUSE.MOM
THREAT REPORT

IP Threat Report
14.139.157.2

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-27 19:46:32
First seen: 2026-03-12 11:00:07
Last seen: 2026-03-12 11:00:07
103

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 103/100. Total malicious requests observed: 1.

DANGER_PATHMETHOD
01

Geolocation & Classification

IP Address
14.139.157.2
Type
Residential
Country
🇮🇳 India
City
Bengaluru
ISP
National Knowledge Network
Organization
National Institute of Fasion Technology
Autonomous System
AS55824 NKN Core Network
Hit Count
1
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

2026-03-12 11:00:07
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)
2026-03-12 11:00:07
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

National Knowledge Network
AS55824 · 🇮🇳 India
06

Recommendations

Actions taken & recommended

  • IP 14.139.157.2 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

⚙️ General Security

Add 14.139.157.2 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.

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

14.139.157.2 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

14.139.157.2 is registered in Bengaluru, India, operating on the network of National Knowledge Network. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. The address has been active for 1 days in our monitoring system, producing 1 flagged requests at a rate of ~1/day. This residential IP is likely a compromised consumer device. Home routers and IoT equipment with default credentials are prime targets for botnet operators. Our records show 114 malicious IPs originating from India, 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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49.248.192.204 (313)62.72.43.237 (305)4.188.251.0 (280)103.78.247.150 (280)40.80.90.214 (280)View all →

🏢 Same network: AS55824

14.139.176.197 (235)14.139.42.195 (103)164.100.189.248 (103)157.15.158.245 (103)157.15.158.240 (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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