ABUSE.MOM
THREAT REPORT

IP Threat Report
34.135.10.224

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-21 17:34:56
First seen: 2026-04-27 20:00:06
Last seen: 2026-04-27 20:00:07
180

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 180/100. Total malicious requests observed: 31.

DANGER_PATHRATIO_404BURST
01

Geolocation & Classification

IP Address
34.135.10.224
Type
Hosting
Country
🇺🇸 United States
City
Council Bluffs
ISP
Google LLC
Organization
Google Cloud (us-central1)
Autonomous System
AS396982 Google LLC
Hit Count
31
02

Detection Signatures

SignatureDescriptionPointsSeverity
Danger strong hits: 1High-risk paths: shells, RCE vectors, exploits+25
Danger medium hits: 4Medium-risk: admin panels, config files+40
404 ratio 40-60%Majority of requests returned 404 — enumeration+15
Danger medium hits: 9Medium-risk: admin panels, config files+60
404 ratio >= 60%Majority of requests returned 404 — enumeration+25
Burst: 8 req / 2sAbnormally fast request rate — automated scanning+35
Burst: 11 req / 10sAbnormally fast request rate — automated scanning+35
Danger medium hits: 6Medium-risk: admin panels, config files+60
Burst: 5 req / 2sAbnormally fast request rate — automated scanning+35
Danger medium hits: 5Medium-risk: admin panels, config files+50
Burst: 6 req / 2sAbnormally fast request rate — automated scanning+35
Danger medium hits: 12Medium-risk: admin panels, config files+60
Burst: 11 req / 2sAbnormally fast request rate — automated scanning+35
Burst: 14 req / 10sAbnormally fast request rate — automated scanning+35
Danger medium hits: 7Medium-risk: admin panels, config files+60
Danger medium hits: 13Medium-risk: admin panels, config files+60
Burst: 10 req / 2sAbnormally fast request rate — automated scanning+35
Burst: 15 req / 10sAbnormally fast request rate — automated scanning+35
Σ = 735
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

2026-04-27 20:00:06
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: 4 (+40), 404 ratio 40-60% (+15)
2026-04-27 20:00:07
Last malicious request observed
Total score reached: 180/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

Google LLC
AS396982 · 🇺🇸 United States
06

Recommendations

Actions taken & recommended

  • IP 34.135.10.224 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 34.135.10.224 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.

🌊 Flood / DDoS Mitigation

Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 34.135.10.224.

08

Open Ports & Services

Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.

OPEN PORTS (1)
PortServiceRiskDescription
7777UnknownLowService on port 7777
Hostnames: 224.10.135.34.bc.googleusercontent.com
PTR: 224.10.135.34.bc.googleusercontent.com

Data source: Shodan InternetDB. Scanned independently of abuse.mom.

09

Blacklist Status (DNSBL)

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

✓ Clean
ix.dnsbl.manitu.net
✓ Clean
dnsbl.sorbs.net
✓ Clean
zen.spamhaus.org
✓ Clean
bl.spamcop.net
✓ Clean
dnsbl-1.uceprotect.net
✓ Clean
b.barracudacentral.org
✓ Clean
truncate.gbudb.net
✓ Clean
psbl.surriel.com

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

10

Threat Analysis

34.135.10.224 has been assigned a threat score of 180/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.

The following attack categories were identified:

Path EnumerationRequest Flooding

📊 Threat Analysis

IP address 34.135.10.224 has been traced to Council Bluffs, United States, operating on the network of Google LLC. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Over a period of 1 days, this IP generated 31 malicious requests, averaging approximately 31 requests per day. Classified as a hosting IP, this address likely runs on a rented server or cloud instance. Attackers prefer datacenter IPs for their high bandwidth and disposable nature. The dual attack vectors of Path Enumeration combined with Request Flooding indicate a coordinated assault rather than opportunistic scanning. United States currently accounts for 142 blocked IPs in our database, making it a significant source of malicious traffic. At 180/100, this is an extremely high-risk address. All traffic should be considered hostile.

This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.

11

Related Threats

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🏢 Same network: AS396982

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12

Security Intelligence

💡 Credential Stuffing at Scale

Credential stuffing uses stolen username-password pairs from data breaches to attempt logins across many websites. Since users frequently reuse passwords, these automated attacks achieve success rates of 0.1-2%, which translates to thousands of compromised accounts from millions of attempts.

💡 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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