
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Danger medium hits: 10 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 12 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 14 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 16 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 18 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| Danger medium hits: 20 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 22 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 24 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 26 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 28 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 30 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 32 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 34 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 36 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 | |
| Danger medium hits: 6 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 8 | Medium-risk: admin panels, config files | +60 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 | |
| UA changed | Multiple User-Agents — bot rotation technique | +25 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block scanning from 104.223.33.99: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
IP 104.223.33.99 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
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.
104.223.33.99 has been assigned a threat score of 130/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
The following attack categories were identified:
Threat intelligence analysis has linked 104.223.33.99 to malicious activity originating from Los Angeles, United States, operating on the network of HostPapa. The address has been under observation since its initial detection. The address has been active for 12 days in our monitoring system, producing 864 flagged requests at a rate of ~72/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 User-Agent Anomaly indicate a coordinated assault rather than opportunistic scanning. Our records show 145 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. At 130/100, 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.
Modern attacks increasingly target APIs rather than traditional web interfaces. Attackers enumerate endpoints, test for broken authentication, and exploit excessive data exposure. API attacks are harder to detect as they mimic legitimate programmatic access patterns.
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.