Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
51 detections
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Detects a suspicious sequence of activities in GCP Compute Engine where a principal creates a new VM instance, attaches a disk, and creates a disk snapshot or new disk within a short window, potentially indicating data staging or exfiltration preparation.
A successful EC2 StopInstances request was followed within one hour by a successful ModifyInstanceAttribute request targeting userData on the same instance, by the same IAM principal.
This may indicate an attempt to change instance startup behavior or establish persistence.
These CloudTrail events do not confirm that the modified user-data script executed.
This may indicate an attempt to change instance startup behavior or establish persistence.
These CloudTrail events do not confirm that the modified user-data script executed.
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.
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.
The following analytic detects the creation of a cloud compute instance in a region that has not been previously used within the last hour. It leverages cloud infrastructure logs and compares the regions of newly created instances against a lookup file of historically used regions. This activity is significant because the creation of instances in new regions can indicate unauthorized or suspicious activity, such as an attacker attempting to evade detection or establish a foothold in a less monitored area. If confirmed malicious, this could lead to unauthorized resource usage, data exfiltration, or further compromise of the cloud environment.
Detects the creation of GCP compute instances that are attached to service accounts with broad 'cloud-platform' scope permissions. This activity is monitored to ensure that only authorized automation service accounts (e.g., IaC pipelines) are performing these actions, as creating instances with highly privileged service accounts can be a technique used to escalate privileges or establish persistence.
Detects the creation of high-cost, resource-intensive compute instances (such as GPU-enabled or large-scale VMs) within cloud regions that have not been previously utilized by the account, subscription, or project. This behavior is often indicative of unauthorized resource hijacking, such as crypto-mining or large-scale data processing by threat actors.
The following analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
The following analytic detects the creation of EC2 instances with previously unseen instance types.
It leverages Splunk's tstats command to analyze data from the Change data model, identifying instance types that have not been previously recorded.
This activity is significant for a SOC because it may indicate unauthorized or suspicious activity, such as an attacker attempting to create instances for malicious purposes.
If confirmed malicious, this could lead to unauthorized access, data exfiltration, system compromise, or service disruption. Immediate investigation is required to determine the legitimacy of the instance creation.
It leverages Splunk's tstats command to analyze data from the Change data model, identifying instance types that have not been previously recorded.
This activity is significant for a SOC because it may indicate unauthorized or suspicious activity, such as an attacker attempting to create instances for malicious purposes.
If confirmed malicious, this could lead to unauthorized access, data exfiltration, system compromise, or service disruption. Immediate investigation is required to determine the legitimacy of the instance creation.
The following analytic detects cloud provisioning activities originating from previously unseen cities. 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 misuse of cloud resources from an unexpected location. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
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.
The following analytic detects cloud provisioning activities originating from previously unseen regions. It leverages cloud infrastructure logs to identify events where resources are started or created, and cross-references these with a baseline of known regions. This activity is significant as it may indicate unauthorized access or misuse of cloud resources from unfamiliar locations. If confirmed malicious, this could lead to unauthorized resource creation, potential data exfiltration, or further compromise of cloud infrastructure.
Detects the provisioning of high-performance compute instances (GPU or high-CPU) in AWS regions that fall outside of an established organizational baseline. This behavior is often associated with unauthorized resource hijacking (cryptomining) by adversaries leveraging compromised IAM principals to deploy infrastructure in regions with lower visibility or monitoring coverage.
Detects the deployment of virtual machines with public IP addresses (in Azure) or compute instances with external IP addresses (in GCP) by a single principal within a 15-minute window. This behavior is indicative of a newly-privileged identity or an compromised account establishing externally-accessible infrastructure for further malicious activity.
Detects the mass creation of high-performance compute instances (GPU or high-memory variants) in an AWS environment. By filtering out legitimate service-linked roles associated with auto-scaling, the rule isolates potentially unauthorized manual provisioning, often indicative of cryptojacking or resource hijacking.
Detects the use of iam:PassRole to attach a potentially highly privileged IAM role (e.g., Administrator, PowerUser, FullAccess) to newly created or modified compute or serverless resources (EC2 instances, Lambda functions, Launch Templates, or Auto Scaling Groups). This behavior is indicative of an attacker attempting to escalate privileges by granting a managed resource excessive permissions that the attacker can subsequently abuse.
Detects anomalous, large-scale provisioning of GPU-intensive cloud instances (such as p3, p4, g4dn, g5, and trn1 families) in AWS. This activity is indicative of cloud cryptojacking campaigns where adversaries hijack resources to rapidly spin up compute clusters for cryptomining, potentially incurring significant financial damage within a short period.
This rule detects the creation of compute resources (EC2 instances, launch templates, or spot instances) within AWS regions that are designated as dormant or rarely used. This behavior is a common indicator of regional 'region-hopping,' used by attackers for cryptojacking or establishing C2 infrastructure to evade detection, particularly in regions where monitoring and cost-based alerts are less mature.
Detects the launch of compute-optimized or GPU-accelerated EC2 instances via the RunInstances API. This behavior is a common indicator of unauthorized resource hijacking, such as cryptomining, where attackers leverage compromised credentials to deploy resource-intensive workloads in regions that have not been historically used by the organization.
This rule detects the launch of an AWS EC2 instance that is configured with an IAM instance profile containing administrative keywords (e.g., AdministratorAccess, FullAccess) or a wildcard (*). Attackers can abuse this configuration after gaining initial access to the console or API to attach an over-privileged profile to a new instance, then pivot to the instance metadata service (IMDS) to extract these administrative credentials for persistent, broadly-scoped access.
Detects rapid, large-scale deployment of high-vCPU/GPU compute instances, potentially indicating resource hijacking for cryptomining. The rule correlates high-compute instance launches with preceding AWS service quota increases, identifying potential malicious activity within an AWS environment.
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