ABUSE.MOM
THREAT REPORT

IP Threat Report
94.156.14.71

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-27 19:33:43
First seen: 2026-03-14 04:44:26
Last seen: 2026-03-14 04:44:26
70

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 70/100. Total malicious requests observed: 1.

FORM_SPAM
01

Geolocation & Classification

IP Address
94.156.14.71
Type
Residential
Country
🇧🇬 Bulgaria
City
Sofia
ISP
Datacamp Limited
Organization
Datacamp Limited
Autonomous System
AS212238 Datacamp Limited
Hit Count
1
02

Detection Signatures

SignatureDescriptionPointsSeverity
Form spam: no_js_checkSpam/malware keywords in request content+0
Σ = 0
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

2026-03-14 04:44:26
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
Form spam: no_js_check
2026-03-14 04:44:26
Last malicious request observed
Total score reached: 70/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

Datacamp Limited
AS212238 · 🇧🇬 Bulgaria
06

Recommendations

Actions taken & recommended

  • IP 94.156.14.71 is blocked at application level (HTTP 403)
  • Consider blocking at firewall level (iptables/CSF) to reduce server load
  • Other malicious IPs detected in the same /24 subnet — consider blocking 94.156.14.0/24
  • Report abuse to the network provider via their abuse contact
  • Ensure sensitive files (.env, .git, backups) are not accessible from the web

📧 Content Abuse Prevention

IP 94.156.14.71 is flooding forms with spam. Implement time-based tokens and block IPs submitting more than 5 forms per hour.

07

Neighbors in 94.156.14.0/24

Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.

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

94.156.14.71 has been assigned a threat score of 70/100 (High). At this threat level, the IP is considered high risk. Firewall rules should be updated to deny traffic from this source.

📊 Threat Analysis

94.156.14.71 is registered in Sofia, Bulgaria, operating on the network of Datacamp Limited. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 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. Bulgaria currently accounts for 57 blocked IPs in our database, making it a notable source of malicious traffic. The score of 70/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.

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 Bulgaria

85.11.167.90 (293)104.28.246.54 (280)85.11.167.15 (240)146.70.245.66 (230)85.11.167.80 (225)View all →

🏢 Same network: AS212238

94.156.14.35 (118)94.156.14.80 (70)94.156.14.44 (70)94.156.14.105 (70)94.156.14.83 (70)View all →
12

Security Intelligence

💡 SQL Injection Campaigns

SQL injection remains one of the most common web attack vectors. Attackers inject malicious SQL code through input fields to extract database contents, modify data, or gain administrative access. Automated scanners test for SQLi vulnerabilities at massive scale.

💡 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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