Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
53 detections
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Detects suspicious command execution activities using Azure RunCommand, Custom Script Extension, or Azure Connected Machine Agent combined with potentially malicious command arguments or tools such as PowerShell encoded commands, 'IEX', download strings, certutil, whoami, base64, curl, or wget.
Detects the initialization of an AWS CloudShell session followed by file transfer operations (PutFile or GetFile). This behavior may indicate an attacker using CloudShell to stage, exfiltrate, or move tools within the cloud environment, potentially bypassing endpoint detection by operating within the cloud service infrastructure.
Detects mass device management actions (e.g., remote wipe, retire, or delete) performed via the Microsoft Intune console by a user who has simultaneously exhibited risky sign-in behavior, potentially indicating a compromised administrative account.
Detects the initialization of an AWS CloudShell environment or session. Attackers often abuse CloudShell as a managed, agentless environment within the provider's infrastructure to stage tools, perform reconnaissance using the AWS CLI, and exfiltrate data while bypassing standard endpoint-based detection.
Detects potential abuse of Azure Cloud Shell backing storage (acsCloudShell) to stage or persist malicious scripts following an Azure AD session compromise. Adversaries may use this storage to blend in with legitimate engineering operations while maintaining persistent access to their tooling.
Detects potential abuse of Cloud Shell to persist access in cloud environments. For GCP, it monitors the injection of SSH keys into instance or project metadata from a Cloud Shell session. For Azure, it detects a pattern involving Cloud Shell storage provisioning followed by a subsequent VM run-command or SSH public key update, indicating potential unauthorized management of virtual machines.
Detects suspicious modifications to Google Cloud Platform compute instance metadata, specifically the enabling of serial ports or the addition of startup scripts, which can be leveraged to execute arbitrary commands on a virtual machine. This rule also monitors for the initiation of GCP Cloud Shell environments, which may be used as a platform for further administrative activity.
Detects suspicious modifications to Google Cloud Platform compute instance metadata, specifically the enabling of serial ports or the addition of startup scripts, which can be leveraged to execute arbitrary commands on a virtual machine. This rule also monitors for the initiation of GCP Cloud Shell environments, which may be used as a platform for further administrative activity.
This rule detects the use of browser-based cloud shells (AWS CloudShell, Azure Cloud Shell) as an entry point for post-exploitation activities, specifically tracking when these sessions are followed by object storage operations (S3 PutObject/GetObject or equivalent Azure Blob operations) within the same session context. This pattern is indicative of attackers using ephemeral cloud shells to stage stolen data or download offensive tooling, thereby bypassing host-based EDR monitoring.
This rule detects the use of browser-based cloud shells (AWS CloudShell, Azure Cloud Shell) as an entry point for post-exploitation activities, specifically tracking when these sessions are followed by object storage operations (S3 PutObject/GetObject or equivalent Azure Blob operations) within the same session context. This pattern is indicative of attackers using ephemeral cloud shells to stage stolen data or download offensive tooling, thereby bypassing host-based EDR monitoring.
Detects the creation of an AWS CloudShell environment followed by a high volume of file transfer activity (PutFile, GetFile, or PutObject) by the same user within a short time window. This pattern is indicative of potential initial access or abuse of CloudShell for tool staging or unauthorized data exfiltration.
Detects potential abuse of Cloud Shell to persist access in cloud environments. For GCP, it monitors the injection of SSH keys into instance or project metadata from a Cloud Shell session. For Azure, it detects a pattern involving Cloud Shell storage provisioning followed by a subsequent VM run-command or SSH public key update, indicating potential unauthorized management of virtual machines.
Detects potential abuse of Azure Cloud Shell backing storage (acsCloudShell) to stage or persist malicious scripts following an Azure AD session compromise. Adversaries may use this storage to blend in with legitimate engineering operations while maintaining persistent access to their tooling.
Detects the initialization of an AWS CloudShell session followed by file transfer operations (PutFile or GetFile). This behavior may indicate an attacker using CloudShell to stage, exfiltrate, or move tools within the cloud environment, potentially bypassing endpoint detection by operating within the cloud service infrastructure.
Detects anomalous AI agent behavior characterized by a significant spike in resource or tool invocations within a 15-minute window, potentially indicating indirect prompt injection or unauthorized agent hijacking where an agent is coerced into excessive or unauthorized actions.
This rule detects the presence of unauthorized WebView or HTML overlay windows rendered on top of active banking applications on Android devices. This behavior is indicative of credential-theft malware, such as banking trojans, which use overlays to capture user input, including banking credentials, while masking their activity over legitimate finance-related applications.
This rule detects potentially unauthorized or suspicious administrative actions in an Azure environment following a login to specific AI-related applications (AI Gateway, LLM Proxy, or Model Router). It triggers when a user authenticates without applying Conditional Access policies or without Multi-Factor Authentication (MFA), followed immediately (within 30 minutes) by sensitive Azure administrative operations such as role assignments, Key Vault access, or VM write operations.
This rule detects potentially unauthorized or suspicious administrative actions in an Azure environment following a login to specific AI-related applications (AI Gateway, LLM Proxy, or Model Router). It triggers when a user authenticates without applying Conditional Access policies or without Multi-Factor Authentication (MFA), followed immediately (within 30 minutes) by sensitive Azure administrative operations such as role assignments, Key Vault access, or VM write operations.
Detects execution of Azure Automation runbook jobs by managed identities that have recently been assigned high-privilege roles (Owner, Contributor, or User Access Administrator). This behavior is indicative of potential privilege escalation or abuse of existing high-privilege identities to execute unauthorized automation tasks.
Detects execution of Azure Automation runbook jobs by managed identities that have recently been assigned high-privilege roles (Owner, Contributor, or User Access Administrator). This behavior is indicative of potential privilege escalation or abuse of existing high-privilege identities to execute unauthorized automation tasks.
Detects installation or update of a VM extension whose settings invoke scripts, remote support tooling, or command execution outside standard configuration management. This could indicate an adversary establishing persistence or remote access.
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