Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Define a baseline threshold for failed sign-ins due to Conditional Access failures
Detects successful Google Workspace logins from an IP address or user agent not previously seen for the user within a 24-hour window.
This analytic detects the usage of the Azure CLI to interact with user accounts such as creating or deleting a user.
Adversaries create new users so that their malicious activity does not interrupt the normal functions of the compromised users and can remain undetected.
While legitimate administrative use is expected, anomalous execution patterns, unexpected users, or unusual parent processes should be treated as potential indicators of compromise and investigated promptly.
Adversaries create new users so that their malicious activity does not interrupt the normal functions of the compromised users and can remain undetected.
While legitimate administrative use is expected, anomalous execution patterns, unexpected users, or unusual parent processes should be treated as potential indicators of compromise and investigated promptly.
Detects the rare 'AssumeRoot' event in AWS STS followed by high-impact administrative or destructive API calls within a one-hour window. This behavior often indicates an adversary attempting to gain elevated, privileged access to perform unauthorized actions such as disabling logging, deleting infrastructure, or modifying IAM policies.
Detects high-risk operations performed by the Entra ID/AAD Connect synchronization service account (e.g., Sync_* or ADToAADSyncServiceAccount). This account, which often holds broad directory permissions, is a prime target for attackers to gain persistence or escalate privileges within the cloud directory. The rule monitors for unauthorized sign-ins, sensitive configuration changes (such as modifications to federation trust, authentication policies, or directory synchronization settings), and password resets targeting the sync account itself.
Detects anomalous account behavior where a single user account authenticates to three or more distinct applications within a 60-minute window while originating from at least two different IP addresses. This pattern is indicative of a successful account compromise followed by rapid reconnaissance or data exfiltration across multiple cloud services.
This rule identifies successful sign-in events using the Azure CLI application where multi-factor authentication (MFA) was not enforced. This is a potential indicator of a misconfiguration where sensitive cloud management interfaces are accessible via single-factor authentication, or a sign of an adversary attempting to leverage a less secure authentication pathway.
This rule detects potential privilege escalation in cloud environments (AWS, Azure, GCP) by identifying the attachment or update of overly permissive policies (e.g., wildcard permissions or administrative roles) followed by rapid execution of high-privilege actions (like creating new users, keys, or profiles) by the same actor within 15 minutes.
Detects chained AWS STS AssumeRole/AssumeRoleWithSAML/AssumeRoleWithWebIdentity events where a principal assumes a role in a different account and then quickly performs another role assumption from that new context. This pattern, often referred to as 'role chaining' or 'role laundering', is frequently used to obfuscate identity tracking and facilitate lateral movement across AWS account boundaries.
Okta flagged a password as present in a known breach corpus and still issued a session
for the same account within the same hour. Indicates the tenant's sign-on policy is not
enforcing the breached-credential signal and the account is likely under attacker control.
for the same account within the same hour. Indicates the tenant's sign-on policy is not
enforcing the breached-credential signal and the account is likely under attacker control.
The following analytic detects cloud provisioning activities originating from previously unseen countries. It leverages cloud infrastructure logs and compares the geographic location of the source IP address against a baseline of known locations. This activity is significant as it may indicate unauthorized access or potential compromise of cloud resources. If confirmed malicious, an attacker could gain control over cloud assets, leading to data breaches, service disruptions, or further infiltration into the network.
This rule identifies successful sign-in events using the Azure CLI application where multi-factor authentication (MFA) was not enforced. This is a potential indicator of a misconfiguration where sensitive cloud management interfaces are accessible via single-factor authentication, or a sign of an adversary attempting to leverage a less secure authentication pathway.
This rule detects potentially compromised accounts by correlating risky Entra ID sign-ins to Citrix NetScaler with subsequent VPN session establishment followed by rapid, high-volume outbound connection activity from the source IP address. This pattern is indicative of a threat actor using compromised credentials to gain access via VPN and immediately perform internal network scanning or data staging.
Detects instances where a long-dormant Entra ID (Azure AD) service principal is reactivated shortly after having a new credential added or a new app role assigned. This activity is a potential persistence technique where an attacker repurposes a legitimate, high-privileged, but unused service principal to blend in as legacy automation.
Detects potential session cookie replay attacks by identifying sign-in events using SSO or session tokens from different IP addresses, geographic locations, or devices compared to an established user baseline, without a corresponding fresh MFA challenge in the same timeframe. This behavior is indicative of unauthorized access to an already-authenticated session.
Detects successful authentication events to Snowflake cloud data warehouse platforms where no secondary authentication factor (MFA) was registered or triggered. This pattern is indicative of potential credential stuffing or reuse attacks, particularly those involving stolen credentials from infostealer malware, where MFA was not configured or successfully bypassed.
The following analytic identifies the creation of cloud compute instances by users who have not previously created them. It leverages data from the Change data model, focusing on 'create' actions by users, and cross-references with a baseline of known user activities. This activity is significant as it may indicate unauthorized access or misuse of cloud resources by new or compromised accounts. If confirmed malicious, attackers could deploy unauthorized compute instances, leading to potential data exfiltration, increased costs, or further exploitation within the cloud environment.
This rule correlates phishing emails containing URLs with successful, MFA-satisfied sign-ins from an ASN or network location that has not been associated with the user account within the previous 30 days. This pattern is highly indicative of an Adversary-in-the-Middle (AiTM) session hijacking attempt, where a user is phished and their session cookie is intercepted by an attacker.
Detects repeated occurrences of 'ValidationException' errors in AWS Bedrock 'InvokeModel' and 'Converse' API calls. This activity is indicative of an attacker attempting to probe or validate the usability of stolen AWS credentials by sending deliberately malformed requests, aiming to confirm successful authentication without triggering common access-related alerts.
Detects potential help-desk impersonation or social engineering activity involving account credential or MFA method resets, followed shortly by a sign-in from an unknown device or geography. The rule excludes activity tied to established ticketing systems or standard automated password expiration processes.
This rule detects potentially compromised accounts by correlating risky Entra ID sign-ins to Citrix NetScaler with subsequent VPN session establishment followed by rapid, high-volume outbound connection activity from the source IP address. This pattern is indicative of a threat actor using compromised credentials to gain access via VPN and immediately perform internal network scanning or data staging.
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