
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Add 158.62.210.155 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
158.62.210.155 has been assigned a threat score of 105/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
IP address 158.62.210.155 has been traced to Los Angeles, United States, operating on the network of Blazing SEO, LLC. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 78 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~78 requests per day. The address operates as a VPN/proxy exit node. Attackers route traffic through anonymizing services to obscure their real location and evade IP-based security controls. Our records show 107 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. With a threat score of 105/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
This IP is associated with a VPN or proxy service. Attackers frequently route their traffic through anonymizing services to obscure their true location. This makes attribution more challenging but the malicious behavior patterns remain detectable.
Cache poisoning manipulates web cache behavior to serve malicious content to other users. By identifying unkeyed inputs that influence cached responses, attackers can inject JavaScript, redirect users, or cause denial of service at scale through the cache infrastructure.
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.