
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: python | Known bot/crawler User-Agent detected | +40 | |
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Address UA spoofing from 136.226.239.78: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
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.
136.226.239.78 has been assigned a threat score of 65/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
The following attack categories were identified:
Our monitoring infrastructure has identified 136.226.239.78, geolocated to Tokyo, Japan, operating on the network of ZSCALER, INC., as a source of suspicious network activity. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 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. The IP exhibits User-Agent manipulation, switching between different browser identities or sending empty headers. Our records show 104 malicious IPs originating from Japan, positioning it as a significant contributor to global threat activity. At 65/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
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.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
False positives erode trust in security systems and waste analyst resources. Effective management requires feedback loops, allowlisting mechanisms, contextual analysis, and regular tuning of detection rules based on operational experience.