IP Threat Report
51.77.74.125
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst: 5 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 | |
| Danger strong hits: 3 | High-risk paths: shells, RCE vectors, exploits | +75 | |
| UA changed | Multiple User-Agents — bot rotation technique | +25 | |
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 |
Observed Activity
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Timeline
Network Provider
Recommendations
Actions taken & recommended
- IP 51.77.74.125 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
🔎 Path Enumeration Protection
Block scanning from 51.77.74.125: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
🌊 Traffic Flood Defense
IP 51.77.74.125 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
🤖 Bot Detection
Address UA spoofing from 51.77.74.125: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
Blacklist Status (DNSBL)
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
Threat Analysis
51.77.74.125 has been assigned a threat score of 120/100 (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:
📊 Threat Analysis
Threat intelligence analysis has linked 51.77.74.125 to malicious activity originating from Roubaix, France, operating on the network of OVH SAS. The address has been under observation since its initial detection. The address has been active for 10 days in our monitoring system, producing 308 flagged requests at a rate of ~30.8/day. Operating from datacenter infrastructure, this IP is typical of addresses used in organized attack operations. Cloud and VPS providers are commonly exploited as launching platforms for automated scanning. The diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. France currently accounts for 125 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 120/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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.
Related Threats
Security Intelligence
💡 DDoS Mitigation Approaches
Distributed denial of service attacks overwhelm infrastructure with traffic volume. Effective mitigation combines always-on traffic scrubbing, anycast network distribution, rate limiting, and the ability to quickly scale absorption capacity during attacks.
💡 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.