The hypothesis is that the detected malicious URLs are used by adversaries to download malware into the network, leveraging compromised or phishing-based delivery methods. SOC teams should proactively hunt for these URLs in Azure Sentinel to identify and mitigate potential malware infections before they spread laterally within the environment.
Threat: malware_download Total URLs: 3 Active URLs: 1
| URL | Status | Threat | Date Added |
|---|---|---|---|
hxxp://222.141.43.192:52642/bin.sh | offline | malware_download | 2026-05-05 |
hxxp://119.109.211.168:60683/i | online | malware_download | 2026-05-05 |
hxxp://110.36.71.51:46164/i | offline | malware_download | 2026-05-05 |
// Hunt for DNS resolution of URLhaus malicious domains
// Threat: malware_download
let malicious_domains = dynamic(["119.109.211.168"]);
DnsEvents
| where Name has_any (malicious_domains)
| project TimeGenerated, Computer, Name, IPAddresses
| order by TimeGenerated desc
// Hunt for web traffic to URLhaus malicious domains
let malicious_domains = dynamic(["119.109.211.168"]);
CommonSecurityLog
| where RequestURL has_any (malicious_domains) or DestinationHostName has_any (malicious_domains)
| project TimeGenerated, SourceIP, RequestURL, DestinationHostName, DeviceAction
| order by TimeGenerated desc
| Sentinel Table | Notes |
|---|---|
CommonSecurityLog | Ensure this data connector is enabled |
DnsEvents | Ensure this data connector is enabled |
Scenario: A system administrator is downloading a known malicious URL as part of a security test or penetration testing exercise.
Filter/Exclusion: Exclude URLs that match known penetration testing domains (e.g., vulnweb.com, exploit-db.com) or use a custom list of approved test URLs.
Scenario: A scheduled job is configured to fetch updates from a third-party repository, which includes a URL flagged by URLhaus as malicious.
Filter/Exclusion: Exclude URLs that match known update or patch server domains (e.g., updates.examplecorp.com, repo.examplecorp.com) or use a whitelisted list of trusted update URLs.
Scenario: A user is accessing a legitimate phishing training page that mimics a malicious URL, which is falsely flagged by URLhaus.
Filter/Exclusion: Exclude URLs that match known phishing training domains (e.g., phishingtraining.example.com, trainingsite.org) or use a custom list of approved training URLs.
Scenario: A legitimate software update is being distributed via a URL that is incorrectly categorized as malicious by URLhaus.
Filter/Exclusion: Exclude URLs that match known software update domains (e.g., software.update.example.com, patch.example.com) or use a whitelisted list of trusted software update URLs.
Scenario: A system is configured to use a legitimate tool like wget or curl to download files from a URL that is mistakenly flagged as malicious.
Filter/Exclusion: Exclude URLs that match known command-line tool usage patterns (e.g., wget https://trusted-source.com/file) or use a rule based on the presence of legitimate command-line tools in the request context.