Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects attempts to load critical system DLLs (ntdll.dll, kernel32.dll, user32.dll, advapi32.dll) from non-standard or suspicious file paths. This behavior can indicate an attempt to inject malicious code or evade detection by using modified or malicious versions of legitimate system modules.
Detects various post-exploitation activities associated with the Amadey loader, including the execution of its persistence mechanism (nudwee.exe), dropping and executing secondary payloads in temporary directories, using PowerShell download cradles, creating scheduled tasks for persistence, and silent MSI installations. This rule aims to identify the delivery of secondary payloads like StealC and Lumma Stealer.
Detects attempts to load critical system DLLs (ntdll.dll, kernel32.dll, user32.dll, advapi32.dll) from non-standard or suspicious file paths. This behavior can indicate an attempt to inject malicious code or evade detection by using modified or malicious versions of legitimate system modules.
This rule detects the loading of Aspose.Email DLLs from suspicious user-writable or temporary directories. This behavior is indicative of an attacker potentially using a legitimate library for malicious purposes, possibly to evade detection or to collect information, rather than an approved mail application loading it from its intended location.
Detects attempts to load critical system DLLs (ntdll.dll, kernel32.dll, user32.dll, advapi32.dll) from non-standard or suspicious file paths. This behavior can indicate an attempt to inject malicious code or evade detection by using modified or malicious versions of legitimate system modules.
Detects attempts to load critical system DLLs (ntdll.dll, kernel32.dll, user32.dll, advapi32.dll) from non-standard or suspicious file paths. This behavior can indicate an attempt to inject malicious code or evade detection by using modified or malicious versions of legitimate system modules.
KQL Query from file: Potential Notepad++ CVE-2025-49144 Exploitation
KQL Query
KQL Query
KQL Query
Baseline Comparison.
Author: miflower.
The purpose of this query is to perform a comparison between "known good" machines and suspected bad machines.
The original concept for this query was born due to reapplying the same 'whitelist' filters over and over.
It brings deltas between a baseline and another machine quickly to the analyst's view.
This query supports multiple suspected bad machines and multiple "known good" machines.
It also supports providing a timeframe for how far back in time to build a baseline as well as how far back in time to evaluate the suspected bad machines.
Each of the links provided by DeviceId/DeviceName will go to the most recent entry for whatever entity is listed.
Average results for the pre-defined settings below with a single good host and a single bad host on a 'huge' tenant (300k+ machines):.
Compute Time: ~10-20 seconds.
Result Set Size: ~500 rows.
The workflow is as follows:.
1. Establish Variables that are editable on a per-query basis.
2. Define functions for reuse.
3. Calculate DeviceIds for all machines in scope.
4. Derive deltas using the aforementioned functions.
5. Union together all results into a single view.
The following datasets are returned:.
1. Alerts on the suspected bad machines (ignores known good machines, because...they're alerts, additional data has the triggered file).
2. Connected Networks (from DeviceNetworkInfo table, additional data has full Connected Network details).
3. File Creations (disabled by default due to volume, enable at your own risk, additional data has initiating processes).
4. Image Loads (disabled by default due to volume, enable at your own risk, additional data has initiating processes).
5. Logon (derived from DeviceLogonEvents for the unique users logged on, additional data has logon types).
6. Network communication (grouped by 2nd level-domain, ie 'microsoft.com' in 'www.microsoft.com' and 'web.microsoft.com', additional data has the full list of URLs).
7. Process creation (additional da
Author: miflower.
The purpose of this query is to perform a comparison between "known good" machines and suspected bad machines.
The original concept for this query was born due to reapplying the same 'whitelist' filters over and over.
It brings deltas between a baseline and another machine quickly to the analyst's view.
This query supports multiple suspected bad machines and multiple "known good" machines.
It also supports providing a timeframe for how far back in time to build a baseline as well as how far back in time to evaluate the suspected bad machines.
Each of the links provided by DeviceId/DeviceName will go to the most recent entry for whatever entity is listed.
Average results for the pre-defined settings below with a single good host and a single bad host on a 'huge' tenant (300k+ machines):.
Compute Time: ~10-20 seconds.
Result Set Size: ~500 rows.
The workflow is as follows:.
1. Establish Variables that are editable on a per-query basis.
2. Define functions for reuse.
3. Calculate DeviceIds for all machines in scope.
4. Derive deltas using the aforementioned functions.
5. Union together all results into a single view.
The following datasets are returned:.
1. Alerts on the suspected bad machines (ignores known good machines, because...they're alerts, additional data has the triggered file).
2. Connected Networks (from DeviceNetworkInfo table, additional data has full Connected Network details).
3. File Creations (disabled by default due to volume, enable at your own risk, additional data has initiating processes).
4. Image Loads (disabled by default due to volume, enable at your own risk, additional data has initiating processes).
5. Logon (derived from DeviceLogonEvents for the unique users logged on, additional data has logon types).
6. Network communication (grouped by 2nd level-domain, ie 'microsoft.com' in 'www.microsoft.com' and 'web.microsoft.com', additional data has the full list of URLs).
7. Process creation (additional da
These queries was originally published in the threat analytics report, Attacks on gov't, think tanks, NGOs.
As described further in Analysis of cyberattack on U.S. think tanks, non-profits, public sector by unidentified attackers, there was a very large spear-phishing campaign launched in November 2019.
The attackers would gain access to a target by having the user click on a link to a compromised website and download a .zip archive.
Once established on a target's device, the attackers used a malicious DLL named cyzfc.dat to execute additional payloads. They would call a function in the malicious DLL via the legitimate Windows process, rundll32.exe, to connect directly to their command-and-control (C2) servers.
The following queries detect activity associated with the malicious DLL, cyzfc.dat., used in this campaign.
Reference - https://docs.microsoft.com/windows-server/administration/windows-commands/rundll32
As described further in Analysis of cyberattack on U.S. think tanks, non-profits, public sector by unidentified attackers, there was a very large spear-phishing campaign launched in November 2019.
The attackers would gain access to a target by having the user click on a link to a compromised website and download a .zip archive.
Once established on a target's device, the attackers used a malicious DLL named cyzfc.dat to execute additional payloads. They would call a function in the malicious DLL via the legitimate Windows process, rundll32.exe, to connect directly to their command-and-control (C2) servers.
The following queries detect activity associated with the malicious DLL, cyzfc.dat., used in this campaign.
Reference - https://docs.microsoft.com/windows-server/administration/windows-commands/rundll32
Look for the creation of suspicious DLL files spawned in the \spool\ folder along with DLLs that were recently loaded afterwards from \Old.
This query was originally published in the threat analytics report, Solorigate supply chain attack. Please note that these attacks are currently known as the Nobelium campaign.
Microsoft detects the 2020 SolarWinds supply chain attack implant and its other components as part of a campaign by the Nobelium activity group. Nobelium is the threat actor behind the attack against SolarWinds, which was previously referred to as Solorigate.
Nobelium silently added malicious code to legitimate software updates for Orion, which is IT monitoring software provided by SolarWinds. In this way, malicious dynamic link libraries (DLLs) were distributed to SolarWinds customers.
The following query retrieves an inventory of SolarWinds Orion software use in your organization, organized by product name and ordered by how many devices the software is installed on.
More Nobelium-related queries can be found listed under the See also section of this document.
References:
https://msrc-blog.microsoft.com/2020/12/13/customer-guidance-on-recent-nation-state-cyber-attacks/
https://www.microsoft.com/security/blog/2020/12/18/analyzing-solorigate-the-compromised-dll-file-that-started-a-sophisticated-cyberattack-and-how-microsoft-defender-helps-protect/
Microsoft detects the 2020 SolarWinds supply chain attack implant and its other components as part of a campaign by the Nobelium activity group. Nobelium is the threat actor behind the attack against SolarWinds, which was previously referred to as Solorigate.
Nobelium silently added malicious code to legitimate software updates for Orion, which is IT monitoring software provided by SolarWinds. In this way, malicious dynamic link libraries (DLLs) were distributed to SolarWinds customers.
The following query retrieves an inventory of SolarWinds Orion software use in your organization, organized by product name and ordered by how many devices the software is installed on.
More Nobelium-related queries can be found listed under the See also section of this document.
References:
https://msrc-blog.microsoft.com/2020/12/13/customer-guidance-on-recent-nation-state-cyber-attacks/
https://www.microsoft.com/security/blog/2020/12/18/analyzing-solorigate-the-compromised-dll-file-that-started-a-sophisticated-cyberattack-and-how-microsoft-defender-helps-protect/
Use this query to locate suspicious load image events by rundll32.exe or regsvr32.exe, a behavior associated with IcedId, which can lead to ransomware.