Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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KQL Query from file: glibc critical vulnerability (CVSS 9.8)
KQL Query from file: Detecting Windows Downdate Abuse
KQL Query
KQL Query from file: CVE-2024-43452 PoC Detection
KQL Query
KQL Query from file: Hunting Malicious Chrome Extension
KQL Query from file: Detect CVE-2024-43572 Abuse
KQL Query from file: Golden Chickens TerraLogger Detection
This rule detects the creation of hidden files or directories on Linux systems. It specifically looks for common commands like 'mkdir', 'nano', 'touch', 'vi', or 'vim' being executed with arguments that start with a '.' (dot), which is the standard convention for hidden files and directories in Unix-like operating systems. This behavior is often used by adversaries for defense evasion.
This query lists files copied to USB external drives with USB drive information based on FileCreated events associated with most recent USBDriveMount events befor file creations. But be aware that Advanced Hunting is not monitoring all the file types.
'This hunting query looks in Azure Web Application Firewall data to find possible SpringShell Exploitation Attempt (CVE-2022-22965).
The Spring Framework is one of the most widely used lightweight open-source framework for Java. To exploit the vulnerability attackers can
send a specially crafted query to a web server running the Spring Core framework to change the target of logging facility and create a new
malicious JSP file in a location accessible by http requests. Attackers then make requests to the malicious backdoor to run system commands.
Reference: https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/'
The Spring Framework is one of the most widely used lightweight open-source framework for Java. To exploit the vulnerability attackers can
send a specially crafted query to a web server running the Spring Core framework to change the target of logging facility and create a new
malicious JSP file in a location accessible by http requests. Attackers then make requests to the malicious backdoor to run system commands.
Reference: https://www.microsoft.com/security/blog/2022/04/04/springshell-rce-vulnerability-guidance-for-protecting-against-and-detecting-cve-2022-22965/'
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
This query was originally published in the threat analytics report, CVE-2018-15982 exploit attacks.
CVE-2018-15982 is an exploit of Adobe Flash Player, that allows for remote execution of arbitrary code. It has since been patched.
Actors have been observed using this vulnerability in targeted attacks. Exploits for CVE-2018-15982 have also been included in several exploit kits.
In some initial attacks exploiting CVE-2018-15982, attackers sent targets spear-phishing emails. The emails would include an attached RAR archive, which contained a lure document, as well as a second archive disguised as a .jpg file. Opening the document would automatically run an embedded Flash ActiveX control. This, in turn, would call a script containing the exploit. The exploit's ability to run arbitrary code would be employed to unpack and run a payload from the second archive. The payload is a backdoor used both to achieve persistance and for command and control.
The following query detects possible instances of a payload being extracted by the exploit.
References:
https://nvd.nist.gov/vuln/detail/CVE-2018-15982
https://helpx.adobe.com/security/products/flash-player/apsb18-42.html
CVE-2018-15982 is an exploit of Adobe Flash Player, that allows for remote execution of arbitrary code. It has since been patched.
Actors have been observed using this vulnerability in targeted attacks. Exploits for CVE-2018-15982 have also been included in several exploit kits.
In some initial attacks exploiting CVE-2018-15982, attackers sent targets spear-phishing emails. The emails would include an attached RAR archive, which contained a lure document, as well as a second archive disguised as a .jpg file. Opening the document would automatically run an embedded Flash ActiveX control. This, in turn, would call a script containing the exploit. The exploit's ability to run arbitrary code would be employed to unpack and run a payload from the second archive. The payload is a backdoor used both to achieve persistance and for command and control.
The following query detects possible instances of a payload being extracted by the exploit.
References:
https://nvd.nist.gov/vuln/detail/CVE-2018-15982
https://helpx.adobe.com/security/products/flash-player/apsb18-42.html
Check all created files.
That does not have extension ps1, bat or cmd to avoid IT Pro scripts.
That are not copied to C:\ to detect all file share, external drive, data partition that are not allowed, etc.
This could help to detect malicious insider/user that has unencrypted data partition and that are using it to exfiltrate data even while removable devices & cloud storage is blocked.
That does not have extension ps1, bat or cmd to avoid IT Pro scripts.
That are not copied to C:\ to detect all file share, external drive, data partition that are not allowed, etc.
This could help to detect malicious insider/user that has unencrypted data partition and that are using it to exfiltrate data even while removable devices & cloud storage is blocked.
The query for malicious file creations via TOCTOU Vulnerability in Leading Endpoint Detection and Response (EDR) and Antivirus (AV) Solutions.
- Microsoft Defender (CVE-2022-37971)
- Defender for Endpoint (CVE-2022-37971)
- SentinelOne EDR
- TrendMicro Apex One (CVE-2022-45797)
- Avast Antivirus (CVE-2022-4173)
- AVG Antivirus (CVE-2022-4173)
- Microsoft Defender (CVE-2022-37971)
- Defender for Endpoint (CVE-2022-37971)
- SentinelOne EDR
- TrendMicro Apex One (CVE-2022-45797)
- Avast Antivirus (CVE-2022-4173)
- AVG Antivirus (CVE-2022-4173)
Microsoft has observed Bazacall using Cobalt Strike in order to move laterally to other machines on the network.
The following analytic detects potentially suspicious file copy operations targeting the System32 or SysWow64 directories as source, often indicative of malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on activity initiated by command-line tools like cmd.exe or PowerShell. This behavior is significant as it may indicate an attempt to evade defenses by copying an existing binary from the system directory and renaming it. If confirmed malicious, this activity could allow an attacker to execute code undetected and potentially leading to system compromise or further lateral movement within the network.
'Finds instances where a file uploaded to blob or file storage and it is seen on an endpoint by Microsoft Defender XDR.'
'Extracts MDATP Alert for a web shell being placed on the server and then enriches this event with information from W3CIISLog to idnetigy the Attacker that placed the shell'
Expanding on DeviceEvents output with Attack Surface Reduction (ASR) rule descriptions.
The ActionType values of the ASR events already explain what rule was matched and if it was audited or blocked.
However, it could still be useful to have a more human-friendly description in the results.
Also, this query is a good example for how you could define your own lookup tables and join with them.
The events in the DeviceEvents table contain a GUID for the various ASR rules rather than a full description of the rule.
This query will create a table which has the description for each ASR rule as per https://docs.microsoft.com/windows/security/threat-protection/windows-defender-exploit-guard/enable-attack-surface-reduction.
This table is then joined to the output of a query against the DeviceEvents table and shows a summary count of the events by the newly defined description.
This query shows the ability to use joins and custom dimension tables.
See https://docs.loganalytics.io/docs/Language-Reference/Tabular-operators/join-operator for more information on the join syntax.
For more questions on this query, feel free to ping @FlyingBlueMonki on twitter or mattegen@microsoft.com via email.
First lets start by creating a table of the rule descriptions to rule guids.
The ActionType values of the ASR events already explain what rule was matched and if it was audited or blocked.
However, it could still be useful to have a more human-friendly description in the results.
Also, this query is a good example for how you could define your own lookup tables and join with them.
The events in the DeviceEvents table contain a GUID for the various ASR rules rather than a full description of the rule.
This query will create a table which has the description for each ASR rule as per https://docs.microsoft.com/windows/security/threat-protection/windows-defender-exploit-guard/enable-attack-surface-reduction.
This table is then joined to the output of a query against the DeviceEvents table and shows a summary count of the events by the newly defined description.
This query shows the ability to use joins and custom dimension tables.
See https://docs.loganalytics.io/docs/Language-Reference/Tabular-operators/join-operator for more information on the join syntax.
For more questions on this query, feel free to ping @FlyingBlueMonki on twitter or mattegen@microsoft.com via email.
First lets start by creating a table of the rule descriptions to rule guids.
'This hunting query will identify where a file is uploaded to Azure File or Blob storage
and is then accessed once before being deleted. This activity may be indicative of
exfiltration activity.'
and is then accessed once before being deleted. This activity may be indicative of
exfiltration activity.'
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