IP Threat Report
23.94.155.42
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA suspicious (short/empty) | Behavioral anomaly detected by automated analysis | +15 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| UA bot: Go-http-client | Known bot/crawler User-Agent detected | +40 | |
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| Foreign referer seen | Referer from unrelated external domain | +10 |
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 23.94.155.42 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Other malicious IPs detected in the same /24 subnet — consider blocking 23.94.155.0/24
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🤖 User-Agent Anomaly Defense
IP 23.94.155.42 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
🔎 Directory Scan Defense
IP 23.94.155.42 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
Neighbors in 23.94.155.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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
23.94.155.42 has been assigned a threat score of 135/100 (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
IP address 23.94.155.42 has been traced to Miami, United States, operating on the network of Maxihost LTDA. Our threat detection systems have flagged this address based on observed malicious behavior patterns. During its 3-day observation window, we recorded 4 hostile requests from this IP — roughly 1.3 per day on average. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. The dual attack vectors of User-Agent Anomaly combined with Path Enumeration indicate a coordinated assault rather than opportunistic scanning. Our records show 224 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. With a threat score of 135/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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.
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💡 TLS Fingerprinting (JA3/JA4)
TLS fingerprinting creates unique identifiers based on how clients negotiate encrypted connections. The JA3 and JA4 methods generate hashes from TLS ClientHello parameters, enabling identification of specific tools and malware regardless of IP address changes.
💡 Rate Limiting and Throttling Strategies
Effective rate limiting must balance protection against abuse with allowing legitimate traffic bursts. Sliding window algorithms, token buckets, and adaptive thresholds based on client reputation provide layered defense against flooding attacks.