
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| 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.
IP 154.54.100.177 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
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.
154.54.100.177 has been assigned a threat score of 75/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
The following attack categories were identified:
IP address 154.54.100.177 has been traced to Beltsville, United States, operating on the network of Cogent Communications. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Over a period of 9 days, this IP generated 2,414 malicious requests, averaging approximately 268.2 requests per day. Operating from a residential network, this IP may represent a compromised home gateway or IoT device that has been drafted into a larger attack infrastructure. Detected suspicious User-Agent anomalies including empty, forged, or rapidly rotating UA strings — characteristic of automated scanning tools. With 101 flagged addresses, United States represents a significant presence in our threat database. At 75/100, this IP warrants immediate defensive action.
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.
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.
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.