Detections

Explore public detection logic contributed by the community across SIEM and rule languages.

1,598 detections

ET PHISHING Microsoft ClickFix Landing Domain
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Daniel Jeremiah@daniyyell
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Detections.ai Community
9 months ago
1021
ET PHISHING Microsoft ClickFix Landing Domain
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Daniel Jeremiah@daniyyell
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Detections.ai Community
9 months ago
0017
This rule detects potential malware execution and persistence by correlating suspicious script and HTA activity with known malicious infrastructure. It flags mshta, wscript, and cscript executing remote HTA or script content, related network connections and downloads, temporary file artefacts, connections to listed suspicious domains and IPs, attempts to add Windows Defender exclusions, and fake browser update style process launches that retrieve executables over HTTP.
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Daniel Jeremiah@daniyyell
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Detections.ai Community
9 months ago
150229
This query hunts for inbound emails that contain URLs embedded in QR codes and originate from senders that are not commonly observed in the environment, a common technique used in QR-based phishing campaigns.
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Andre Zeemering@Azeemering
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Detections.ai Community
9 months ago
141231
This query hunts for network activity and other events related to specific domains (freefoodaid.com, wellnessmedcare.org) potentially associated with CVE-2026-21509. It searches DNS queries, network connections, generic web proxy/firewall logs, and URL click events to identify interactions with these domains.
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J W@JW404
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Detections.ai Community
9 months ago
190123
This query is better used in a workbook for automation.
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Igor Oliveira@Pr0fiLeRR
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Detections.ai Community
9 months ago
5052
This KQL check for email links that were clicked/ email attachment that were opened before recall
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Udi B@Udi
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Detections.ai Community
9 months ago
352257
The rule detects the execution of a suspicious executable named 'sohuva_update_*.exe' from user-writable or download paths (Temp, Downloads, AppData, or ProgramData\Microsoft\MF). This execution is considered suspicious if its parent process is typically a web browser or explorer.exe, or if the parent process itself originates from one of these suspicious paths. The rule explicitly excludes legitimate executions of 'sohuva_update_*.exe' from standard 'Program Files' directories associated with Sohu installations.
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Daniel Jeremiah@daniyyell
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Detections.ai Community
9 months ago
0014
The goal of this detection is to alert on activity associated with the ClickFix social engineering technique, where a user is coerced into manually executing attacker-controlled commands via the Windows Run dialog after interacting with a malicious website reached through a phishing email. More details can be found in the reference URL linked below
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McDavid A@d3t3ct1v3
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Detections.ai Community
9 months ago
552351
This rule detects a multi-stage attack chain starting with a high-confidence phishing email, followed by a risky sign-in from the recipient, and culminating in sensitive actions within Azure, specifically targeting IAM role assignments, storage account key access, or AKS cluster modifications. The detection correlates these events within a defined time window to identify potential compromise of cloud resources originating from a successful phishing attack.
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Mehulkumar Savajani@Msavajani
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Detections.ai Community
9 months ago
8082
This query will generate an alert for any emails that Defender AIR identifies as malicious but has taken no AIR actions.
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Kevin Nguyen@kevin
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Detections.ai Community
9 months ago
9163
This rule detects network connections to a predefined list of malicious domains, many of which are hosted on Vercel and appear to be associated with phishing campaigns. The domains often mimic legitimate services or financial documents, suggesting an attempt to deliver malware or steal credentials through phishing. The rule specifically looks for these domains in the 'RemoteUrl' field of 'DeviceNetworkEvents'.
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Kaung Khant Ko@kaungkhantko
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Detections.ai Community
9 months ago
191108
There are simpler versions of this when investigating manually, this is not for manual use on your day to day activities, this is for automation.
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Igor Oliveira@Pr0fiLeRR
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Detections.ai Community
9 months ago
40122
This query detects suspicious sign-in activity from a user who has recently clicked on a phishing link. It correlates email click events with sign-in logs, identifying sign-ins from new or infrequent IP addresses after a user has interacted with a suspicious email. The rule uses configurable parameters for sender email addresses, sender domains, and subject keywords to identify phishing emails.
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Benjamin Zulliger@benscha
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Detections.ai Community
10 months ago
687436
This rule detects suspicious process creation patterns indicative of the MuddyWater (also known as RustyWater) APT group. Specifically, it looks for Microsoft Office applications (Word, Excel, PowerPoint) launching scripting or Living Off The Land Binaries (LOLBins) such as cmd.exe, powershell.exe, mshta.exe, rundll32.exe, wscript.exe, or cscript.exe. The detection is further narrowed down by checking if these processes are created in suspicious writable paths like ProgramData, Users\Public, AppData\Roaming, or AppData\Local\Temp. Additionally, the rule includes optional enrichment for known MuddyWater/RustyWater payload names (reddit.exe, art.exe, cloud.exe, nginx.exe), persistence via the Run registry key, and the dropping of a specific staging artifact named 'CertificationKit.ini'.
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Awais Munir@iawais
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Detections.ai Community
10 months ago
795522
TIFCE (Threat Intelligence Feed Content Evaluation) is a simple way to measure if a TI feed is actually useful based on four things — uniqueness, real matches, confirmed maliciousness, and activity.
🚨 TIFCE allows using one detection per IOC type (URLs, domains, file hashes) instead of dozens of rules per feed, keeping detections clean, centralized, and easy to maintain.
If you rely on threat intelligence for detection, it can be a good solution to how to evaluate current or new TI feeds.
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Sergio Albea@Sergio_Albea
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01 | šŸ‡ØšŸ‡­ Swiss Cyber Hunters
10 months ago
667496
TIFCE (Threat Intelligence Feed Content Evaluation) is a simple way to measure if a TI feed is actually useful based on four things — uniqueness, real matches, confirmed maliciousness, and activity.
🚨 TIFCE allows using one detection per IOC type (URLs, domains, file hashes) instead of dozens of rules per feed, keeping detections clean, centralized, and easy to maintain.
If you rely on threat intelligence for detection, it can be a good solution to how to evaluate current or new TI feeds.
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Sergio Albea@Sergio_Albea
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01 | šŸ‡ØšŸ‡­ Swiss Cyber Hunters
10 months ago
442363
This rule detects inbound emails classified as phishing or malware by the email security system. It summarizes the total number of such messages, provides sample subjects, and counts how many were blocked, delivered to mailboxes, junked, or had other actions. The rule specifically focuses on emails that were delivered to a mailbox, indicating a potential bypass of initial security measures or a need for further investigation.
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Information hacker@Informationhacker
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Detections.ai Community
10 months ago
170113
This query detects when a user downloads a potentially malicious or unauthorized file type (e.g., executable, script, archive) from the internet using a web browser. It leverages endpoint file creation events from data mapped to the Elastic Common Schema (ECS).
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Kenji Choy@kchoy
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Detections.ai Community
10 months ago
80116
This rule detects suspicious activity related to Microsoft 365 Device Code OAuth consent flows. It specifically looks for device code authentications that are single-factor or lack MFA, and then groups these by IP address and client application. The rule identifies suspicious patterns such as a high number of logins, unique users, or unique applications from a single IP address, or rapid login attempts within a short timeframe, which could indicate enumeration or phishing attempts.
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Lacey Cochrane@NullVectorX
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XQL Threat Forge
10 months ago
253291
Detects phishing emails containing QR codes rendered using HTML tables. This technique is used to evade image-based scanning and OCR security controls by representing the QR code as a grid of small, colored table cells (<td>) instead of a traditional image file.
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Tim Peck@timpeck
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Detections.ai Community
10 months ago
93116
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