Arnold Chan
@slazTrusted contributorCompletionist
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7,544 detections
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Detects a multi-stage attack chain originating with a healthcare-themed phishing email (e.g., billing or patient coordination lures) that leads to a link click, followed by a subsequent suspicious user sign-in from a non-managed/non-compliant device for the same recipient. This correlates email threat data, URL click telemetry, and sign-in logs to identify potential credential harvesting.
Detects potential lateral movement or compromised account use where a single user identity authenticates to multiple distinct cloud or administrative resources within a short time window (15 minutes). The rule uses a threshold of six or more unique applications or resources to identify anomalous patterns typical of reconnaissance or mass resource access often associated with stolen credentials. Administrative, service, and automation-related accounts are excluded to minimize noise.
This rule detects various malicious indicators of compromise (IOCs) across multiple telemetry sources, including DNS queries, web network traffic, file executions, process creation, and email activity. It monitors for interactions with known malicious domains, URLs, file hashes, and specific sender email addresses to identify potential malware distribution or C2 activity.
This rule detects various malicious indicators of compromise (IOCs) across multiple telemetry sources, including DNS queries, web network traffic, file executions, process creation, and email activity. It monitors for interactions with known malicious domains, URLs, file hashes, and specific sender email addresses to identify potential malware distribution or C2 activity.
This rule detects various malicious indicators of compromise (IOCs) across multiple telemetry sources, including DNS queries, web network traffic, file executions, process creation, and email activity. It monitors for interactions with known malicious domains, URLs, file hashes, and specific sender email addresses to identify potential malware distribution or C2 activity.
This rule detects various malicious indicators of compromise (IOCs) across multiple telemetry sources, including DNS queries, web network traffic, file executions, process creation, and email activity. It monitors for interactions with known malicious domains, URLs, file hashes, and specific sender email addresses to identify potential malware distribution or C2 activity.
IOC hunt across DNS, network, and file-hash telemetry for the Ledger Google Ads phishing campaign. Vercel redirect domains are matched by exact hostname (DNS query name / parsed URL host) rather than substring, eliminating false positives from unrelated strings that merely contain a look-alike domain fragment. GCS bucket and Google Sites path IOCs remain substring-matched since the bucket IDs and page slugs are already highly specific.
Detects a multi-stage phishing attack targeting cryptocurrency users, specifically Ledger wallet holders. The detection identifies an initial request to a Vercel-hosted domain for a BIP-39 wordlist (a common recovery phrase standard) followed by a POST request to a similar Vercel-hosted endpoint, suggesting the exfiltration of a secret recovery phrase.
Detects activity from known Storm-3168 (JADEPUFFER) campaign IP addresses across Azure control-plane, sign-in, and App Service logs. The rule monitors for malicious indicators in Azure Activity logs, Azure Sign-in logs, and Service Principal sign-ins, as well as specific sensitive URI paths (e.g., shells, admin login paths) within App Service HTTP logs.
Detects activity from known Storm-3168 (JADEPUFFER) campaign IP addresses across Azure control-plane, sign-in, and App Service logs. The rule monitors for malicious indicators in Azure Activity logs, Azure Sign-in logs, and Service Principal sign-ins, as well as specific sensitive URI paths (e.g., shells, admin login paths) within App Service HTTP logs.
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