IP Threat Report
173.239.240.74
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 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| 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 for same IP | Multiple User-Agents — bot rotation technique | +25 | |
| UA suspicious | Behavioral anomaly detected by automated analysis | +15 | |
| UA suspicious (short/empty) | Behavioral anomaly detected by automated analysis | +15 |
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 173.239.240.74 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 173.239.240.0/24
- 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 173.239.240.74: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
🤖 Bot Detection
Address UA spoofing from 173.239.240.74: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
Neighbors in 173.239.240.0/24
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
173.239.240.74 has been assigned a threat score of 100/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
The following attack categories were identified:
📊 Threat Analysis
Threat intelligence analysis has linked 173.239.240.74 to malicious activity originating from Edison, United States, operating on the network of LogicWeb Inc.. The address has been under observation since its initial detection. Our sensors captured 103 malicious requests from this address across a 85-day span, reflecting a sustained attack cadence of ~1.2 requests per day. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. Two attack patterns were identified (Path Enumeration and User-Agent Anomaly), suggesting a semi-automated campaign that targets multiple vulnerabilities. With 235 flagged addresses, United States represents a significant presence in our threat database. At 100/100, this is an extremely high-risk address. All traffic should be considered hostile.
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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💡 API Abuse and Enumeration
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.
💡 Automated Incident Response
Automated response systems can block threats in milliseconds, far faster than human analysts. However, automation requires careful safeguards — rate limits on blocking actions, automatic expiration, and human review queues prevent automated systems from causing self-inflicted outages.