Sergio Albea
@Sergio_AlbeaTrusted contributorCompletionist
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This query is oriented to identify and classify browser extensions by group and risk severity based on a well-known list of browser extension.
One of the ongoing threats that I feel we face daily is the use of multiple plugins and add-ons across various sites from devices so I focus on identifying common platforms where users might connect and utilize different features. I started with WordPress plugins to gather information and monitor user activity, particularly to stay informed about any connections to specific WordPress sites that employ unknown plugins.
Additionally, if you have installed a WordPress site on any of your devices and it is integrated with Microsoft Defender for Endpoint, this query can be adapted to detect the plugins in use by the users connectives —both from within and externally. It can also help identify potential attacks and vulnerabilities, such as outdated plugin versions.
Additionally, if you have installed a WordPress site on any of your devices and it is integrated with Microsoft Defender for Endpoint, this query can be adapted to detect the plugins in use by the users connectives —both from within and externally. It can also help identify potential attacks and vulnerabilities, such as outdated plugin versions.
Monitoring the execution of auditpol.exe can be crutial to detect first-stage of a real attack because they will be shown as previous steps to obfuscate the next execution such a ransomware.
Monitoring the execution of auditpol.exe can be crutial to detect first-stage of a real attack because they will be shown as previous steps to obfuscate the next execution such a ransomware.
CyberSecurityNews has reported a raising case about a new wave of phishing emails masquerading as breach notifications from LastPass.
To summarise, these emails claim that the recipient’s account has been compromised and urge them to download a “security patch” to restore access.
In reality, the attached file is a sophisticated malware loader built to steal credentials and deploy additional malicious payloads onto the victim’s system.
Each phishing message includes a ZIP attachment named 'LastPass_Security_Update' that contains an executable disguised as an MSI installer. When executed, the fake MSI installs a PowerShell script into the user’s AppData folder and schedules it to run via a scheduled task. The script then contacts a remote command-and-control server to download a second-stage payload capable of keylogging, taking screenshots, and moving laterally within corporate networks.
This following KQL Query is based on the IOA where the emails arrive with a .zip file attached and it content is a msi file. Using IOA instead the IOC (LastPass_Security_Update .zip) is more feasible because the malicious actors could easily change the zip name and the detection would not detect the mentioned threat.
To summarise, these emails claim that the recipient’s account has been compromised and urge them to download a “security patch” to restore access.
In reality, the attached file is a sophisticated malware loader built to steal credentials and deploy additional malicious payloads onto the victim’s system.
Each phishing message includes a ZIP attachment named 'LastPass_Security_Update' that contains an executable disguised as an MSI installer. When executed, the fake MSI installs a PowerShell script into the user’s AppData folder and schedules it to run via a scheduled task. The script then contacts a remote command-and-control server to download a second-stage payload capable of keylogging, taking screenshots, and moving laterally within corporate networks.
This following KQL Query is based on the IOA where the emails arrive with a .zip file attached and it content is a msi file. Using IOA instead the IOC (LastPass_Security_Update .zip) is more feasible because the malicious actors could easily change the zip name and the detection would not detect the mentioned threat.
The following KQL query focuses on detecting cases where files are created, modified, or otherwise accessed via RDP from another computer. Its main objective is to verify whether, during an RDP session, the connection has mapped a local disk or other redirected device — allowing us to easily identify potential cases of data exfiltration through RDP file transfer
Researching about how to classify ISP based on different factors, I created a query based on ISP sign-in attempts.
This detection helps to identify if an ASN that typically handles an average of X connection requests per day over a consistent 7-day period suddenly triples—or more—the number of requests. (You can adjust this line to define the exact multiplication threshold). In addition, identify if during the last week, a new ASN has triggered more than 1K connection attempt during a day.
This KQL is focus on gather statistics related to hackers activities over our tenants, such as the number of emails sent tagged as threats. Below, I’ve provided KQL queries to help identify the risky ASNs mentioned at the beggining.
You can adjust the filtering criteria by modifying the line: “| where SuspiciousPercentage > 95 and Total_different_IPs > 10” based on your specific requirements.
You can adjust the filtering criteria by modifying the line: “| where SuspiciousPercentage > 95 and Total_different_IPs > 10” based on your specific requirements.
When two accounts from the same Entra tenant are signed into the same browser app, the device authentication (the PRT) belonging to the primary account is implicitly applied to the second account. That means the second account can inherit device-based Conditional Access, effectively bypassing intended device checks and weakening protection for privileged accounts.
Based on this behavior, I created a KQL query that detects any account assigned a privileged role signing in with private/incognito browser sessions on a device where potentially another account is already signed in — a pattern that helps to detect the mentioned PRT/device-trust bypass. Even lower-privileged accounts are concerning when they browse in private mode so I am not filtering by just admin ones. However, to target specific roles, add:
| 𝘸𝘩𝘦𝘳𝘦 𝘵𝘰𝘴𝘵𝘳𝘪𝘯𝘨(𝘈𝘴𝘴𝘪𝘨𝘯𝘦𝘥𝘙𝘰𝘭𝘦𝘴) 𝘤𝘰𝘯𝘵𝘢𝘪𝘯𝘴 "𝘢𝘥𝘮𝘪𝘯"
Based on this behavior, I created a KQL query that detects any account assigned a privileged role signing in with private/incognito browser sessions on a device where potentially another account is already signed in — a pattern that helps to detect the mentioned PRT/device-trust bypass. Even lower-privileged accounts are concerning when they browse in private mode so I am not filtering by just admin ones. However, to target specific roles, add:
| 𝘸𝘩𝘦𝘳𝘦 𝘵𝘰𝘴𝘵𝘳𝘪𝘯𝘨(𝘈𝘴𝘴𝘪𝘨𝘯𝘦𝘥𝘙𝘰𝘭𝘦𝘴) 𝘤𝘰𝘯𝘵𝘢𝘪𝘯𝘴 "𝘢𝘥𝘮𝘪𝘯"
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