Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects when a user flagged as 'high risk' by Azure AD Identity Protection lists storage account keys. This could indicate a compromised account is being used to access sensitive data or escalate privileges.
This rule detects successful sign-ins that used single-factor authentication and were flagged by Azure AD Identity Protection for having unfamiliar properties, such as a new location, device, or ASN. Single-factor authentication is inherently weaker, and when combined with anomalous sign-in properties, it can be an indicator of a compromised account.
Microsoft Entra ID Protection can provide a broad range of risk detections that can be used to identify suspicious activity in your organization.
This KQL detection rule identifies and prioritizes high-fidelity user risk events in Azure AD. It focuses on high-fidelity risk event types, filtering out known, noisy, or low-fidelity events to ensure alerts are accurate, actionable, and reliable.
This KQL detection rule identifies and prioritizes high-fidelity user risk events in Azure AD. It focuses on high-fidelity risk event types, filtering out known, noisy, or low-fidelity events to ensure alerts are accurate, actionable, and reliable.
This query identifies user sign-ins leveraging legacy authentication protocols in Azure Active Directory (AAD). Legacy authentication methods, such as IMAP, POP3, Exchange ActiveSync, SMTP, and older Outlook protocols, do not support modern security features like multi-factor authentication (MFA). Their use increases the risk of account compromise, brute force attacks, and credential theft.
Monitoring for legacy authentication activity is a critical security control to detect potential unauthorized access or accounts bypassing modern security protections.
Monitoring for legacy authentication activity is a critical security control to detect potential unauthorized access or accounts bypassing modern security protections.
This rule detects a highly suspicious sequence of events: a user clicking on a URL (potentially a phishing link) followed by a risky sign-in attempt to Azure AD from an IP address outside the organization's defined range. The rule specifically looks for sign-ins with a high-risk level (>= 50) that occur after a URL click event, suggesting a potential compromise initiated by a phishing attack.
https://www.proofpoint.com/us/blog/threat-insight/beyond-credentials-weaponizing-oauth-applications-persistent-cloud-access
Detects when Microsoft Cloud App Security (MCAS) reports a risky sign-in attempt due to an impossible travel scenario. This indicates a potential compromise of user credentials where an attacker logs in from a geographically distant location in a short timeframe, making physical travel impossible.
This rule detects successful sign-in attempts to Azure AD from devices that are not registered in Active Directory (non-compliant devices) and where Multi-Factor Authentication (MFA) was not required. The sign-in is also flagged as 'atRisk' by Azure AD. This scenario can indicate an adversary attempting to gain initial access or maintain persistence using compromised credentials from an unregistered device, bypassing stronger authentication controls.
This rule detects suspicious sign-in activities that occur shortly after a network connection to an IP address associated with 'Lab539 Clickfix'. It correlates risky sign-in events from Azure Active Directory (AAD) with network connection logs from devices. Specifically, it looks for AAD sign-ins with a risk level of 30 or higher, from non-guest, non-compliant, and non-managed devices, where the IP address has not been seen for that user in the last 30 days. This is then joined with network connection events to IP addresses found in the 'Lab539 Clickfix' external data, ensuring the sign-in occurs after the network connection within a short timeframe. The 'Lab539 Clickfix' reference suggests a connection to a known threat or campaign, possibly related to phishing or credential compromise.
This rule detects anomalous user activity patterns, specifically focusing on suspicious changes to directories or other behaviors that Microsoft Entra ID Protection identifies as 'anomalousUserActivity'. This could indicate compromised accounts or insider threats.
The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
This rule identifies an event where the number of accounts assigned the Global Administrator role in Azure Active Directory (now Microsoft Entra ID) exceeds a predefined threshold. This is detected by monitoring the 'tooManyGlobalAdminsAssignedToTenantAlertIncident' risk event type from Azure PIM (Privileged Identity Management). A high number of Global Administrators can increase the attack surface and risk of privilege abuse or compromise.
This analytic detects suspicious data transfer activities involving the use of the `split` syscall with the `-b` option, which specifies the size of each output file. This technique can be used by attackers to evade detection by breaking large files into smaller parts, bypassing size-based security controls, and facilitating covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall with a specified block size, this analytic helps identify potential data exfiltration attempts.
This analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame (10 seconds). A detection is triggered if 15 or more unique readme paths are observed. This activity is a strong indicator of an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
KQL Query from file: Palo Alto Scanning Surges Detection
This rule detects potential device code phishing attempts by correlating URL click events with sign-in logs using the 'deviceCode' authentication protocol. It specifically looks for successful or failed sign-ins (result types 0, 50074, 50076, 500121) that occur after a user clicks on a URL.
Detects potential Multi-Factor Authentication (MFA) fatigue attacks by identifying a high number of MFA denial events for a single user within a short timeframe. This rule specifically looks for sign-in logs where the user explicitly declined the authentication prompt, indicating a possible attempt by an attacker to bombard the user with MFA requests until they approve one.
This rule detects suspicious sign-in attempts to Microsoft services (login.microsoftonline.com, graph.microsoft.com, outlook.office365.com) that occur within 30 minutes of a user clicking on a URL associated with these services. The sign-in is considered suspicious if it originates from a user agent commonly used by automated tools (e.g., axios, curl, python-requests, httpclient, Go-http-client) and is flagged with a 'atRisk' or 'high' risk level by Azure AD. This combination suggests a potential credential compromise following a phishing attempt, where an attacker might be using automated tools to leverage stolen credentials.
This rule detects instances where a user clicks on a URL identified as potentially malicious (phishing-related) and subsequently has a risky sign-in event in Azure AD. The rule correlates URL click events from email with Azure AD sign-in logs, specifically looking for sign-ins marked as risky or originating from suspicious user agents (e.g., specific axios versions). The phishing URLs are identified by keywords such as 'azureapplicationregistration.pages.dev', 'chrnobinson.com', 'workers.dev', '.pages.dev', '.web.core.windows.net', '/oauth2/v2.0/authorize', 'client_id=', and 'scope=openid'.
Indicates that there are anomalous patterns of behavior like suspicious changes to the directory.
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