Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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The following analytic detects a suspicious registry modification aimed at disabling security audit logs by adding a specific registry entry. It leverages data from the Endpoint.Registry data model, focusing on changes to the "Control\\MiniNt" registry path. This activity is significant because it can prevent Windows from logging any events to the Security Log, effectively blinding security monitoring efforts. If confirmed malicious, this technique could allow an attacker to operate undetected, making it difficult to trace their actions and compromising the integrity of security audits.
This analytic identifies potential privilege escalation activities within an organization's Active Directory (AD) environment by correlating multiple analytics from the "Active Directory Privilege Escalation" analytic story within a specified time frame. This is significant for a SOC as it helps identify coordinated attempts to gain elevated privileges, which could indicate a serious security threat. If confirmed malicious, this activity could allow attackers to gain unauthorized access to sensitive systems and data, leading to potential data breaches and further compromise of the network.
The following analytic detects instances where Azure AD has blocked a user's attempt to grant consent to a risky or potentially malicious application. This detection leverages Azure AD audit logs, focusing on user consent actions and system-driven blocks. Monitoring these blocked consent attempts is crucial as it highlights potential threats early on, indicating that a user might be targeted or that malicious applications are attempting to infiltrate the organization. If confirmed malicious, this activity suggests that Azure's security measures successfully prevented a harmful application from accessing organizational data, warranting immediate investigation to understand the context and take preventive measures.
The following analytic detects cloud provisioning activities originating from previously unseen IP addresses. It leverages cloud infrastructure logs to identify events where resources are created or started, and cross-references these with a baseline of known IP addresses. This activity is significant as it may indicate unauthorized access or potential misuse of cloud resources. If confirmed malicious, an attacker could gain unauthorized control over cloud resources, leading to data breaches, service disruptions, or increased operational costs.
This analytic detects the execution of the 'shred' command on a Linux machine, which is used to overwrite files to make them unrecoverable. It leverages data from Linux Auditd, focusing on process names and command-line arguments. This activity is significant because the 'shred' command can be used in destructive attacks, such as those seen in the Industroyer2 malware targeting energy facilities. If confirmed malicious, this activity could lead to the permanent destruction of critical files, severely impacting system integrity and data availability.
The analytic identifies a single source IP failing to authenticate with multiple valid users, potentially indicating a Password Spraying attack against an Azure Active Directory tenant. It uses Azure SignInLogs data and calculates the standard deviation for source IPs, applying the 3-sigma rule to detect unusual numbers of failed authentication attempts. This activity is significant as it may signal an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information.
Secrets added to applications have legitimate purposes, but can also be a method of persistence.
Entra ID application was restored. This implies a soft delete was performed at some point but less than 30 days ago.
This rule detects when an Azure AD user's credentials have been identified as leaked. This detection is based on the 'leakedCredentials' risk event type reported by Azure AD Identity Protection, indicating that the user's valid credentials (e.g., username and password) have been found on the dark web or other publicly available sources.
Detects suspicious inbox manipulation rules that are set on a user's mailbox to delete or move messages or folders. This often indicates an attacker is trying to hide their activities or exfiltrate data by automatically moving emails.
Detects when Microsoft Cloud App Security reports activities performed by users whose accounts have been terminated in Azure AD, but are still active in other platforms like AWS or Salesforce. This is particularly relevant for accounts used to manage resources that might not be terminated when a user leaves the company.
This rule detects attempts to access a Primary Refresh Token (PRT) resource within Azure. A PRT is a key artifact used for single sign-on (SSO) in Azure AD, allowing users to maintain their authenticated state across various applications and services. Unauthorized access to a PRT can enable an adversary to move laterally within an organization's Azure environment, perform credential theft, and bypass multi-factor authentication (MFA) to gain access to resources as the legitimate user. This detection specifically looks for the 'attemptedPrtAccess' risk event type reported by Azure's risk detection service.
This rule detects successful password spray activity. A password spray attack involves an adversary attempting a single or small list of commonly used passwords against many different accounts to acquire valid credentials, often to avoid account lockouts. The detection specifically triggers when Azure AD Identity Protection identifies a 'passwordSpray' risk event.
Detects when an end user's consent to an application is blocked by Azure AD due to risk-based consent policies. This indicates that Azure AD has identified a risky application or a risky consent request, preventing the user from granting permissions to it. This can be a proactive measure against consent phishing or malicious applications attempting to gain access to user data.
This rule identifies sign-in attempts from geographically distant locations within a short timeframe, which is indicative of an "impossible travel" scenario. It also considers if one of these locations is atypical for the user based on their past behavior. This pattern often suggests that an attacker has compromised user credentials and is attempting to access the account from a new, unusual location, potentially using a VPN or proxy service to mask their true origin.
This rule identifies 'impossible travel' events where a user's activities originate from geographically distant locations within a time period that is too short for physical travel between those locations. This often indicates a compromised account being accessed from multiple locations simultaneously or in rapid succession, potentially using different IP addresses or devices.
This rule detects an anomaly in the SAML token issuer, indicating a potential compromise of the SAML token. The detection is triggered when the 'riskEventType' is 'tokenIssuerAnomaly', suggesting that the claims within the token are unusual or align with known attacker patterns. This could signify an adversary forging web credentials, specifically SAML tokens, to gain unauthorized access.
Detects sign-in attempts with properties (e.g., device, location, browser) that are unusual or unfamiliar compared to a user's past sign-in history. This rule leverages Azure AD Identity Protection's risk detection capabilities to identify anomalous sign-ins.
This rule detects anomalous token characteristics, such as an unusual token lifetime or a token being used from an unfamiliar location. This indicates potential compromise or misuse of identity tokens.
This rule detects sign-in attempts originating from IP addresses that have been identified as infected with malware. These malware-infected IP addresses are known to actively communicate with bot servers, indicating a high risk of compromise or malicious activity associated with the sign-in.
This rule identifies user sign-ins originating from anonymous IP addresses, which may indicate the use of VPNs, Tor, or other anonymization services. Such activity can be a sign of an adversary attempting to mask their true location or identity during an access attempt.
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