Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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.
Detects anomalous AWS Bedrock activity indicative of LLMjacking by correlating multiple phases of an attack chain (Model Enablement, Reconnaissance, Probing, and Execution) performed by a single identity within a short timeframe. This rule identifies attackers leveraging stolen IAM credentials to discover available AI models, probe for vulnerabilities, and execute unauthorized inference requests at scale.
The following analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment.
Detects unauthorized attempts to disable, suspend, or downgrade critical native cloud security and monitoring services (e.g., AWS GuardDuty, AWS Security Hub, Azure Defender for Cloud, and GCP Security Command Center). Attackers often perform these actions to evade detection of subsequent malicious activity within the cloud environment.
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
Detects anomalous authentication patterns where a single source IP targets a high number of unique user accounts with failed authentication attempts over a rolling window. By filtering out SSO/Federated methods and specific service account patterns, this rule isolates low-and-slow brute-force or password-spraying activities designed to evade standard account lockout mechanisms.
Detects anomalous AWS activity combining bulk secret retrieval from AWS Secrets Manager using the BatchGetSecretValue API with subsequent authentication failures or validation errors in AWS STS. This behavior is indicative of automated credential harvesting pipelines or agentic frameworks rapidly enumerating and testing stolen credentials.
Detects anomalous authentication patterns associated with AI agent or service account credentials. The rule monitors for logins originating from multiple disparate network locations, ASNs, or countries within a 15-minute window, and detects unexpected MFA challenges directed at accounts that typically function as automated, headless identities.
Detects malicious attempts to delete or modify the access control settings of centralized cloud audit log storage, such as S3 buckets, Azure Log Analytics workspaces, or GCP log buckets. These actions aim to impair visibility into an adversary's activities, a common step in anti-forensics and data destruction workflows.
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
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 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 attempts by potentially malicious processes, such as web-browsing tools, scripts, or agents, to access the cloud instance metadata service (169.254.169.254) or associated metadata paths. This behavior is indicative of Server-Side Request Forgery (SSRF) exploitation aimed at stealing cloud instance credentials.
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.
This rule detects anomalous activity involving cloud-based shell services (AWS CloudShell, Azure Cloud Shell, GCP Cloud Shell). It triggers on two main conditions: (1) First-time usage of these shell environments by a principal within the detection window, and (2) The execution of commands associated with data staging, ingress tool transfer, or exfiltration (e.g., curl, wget, storage copy commands).
Detects modifications to security groups, network security groups (NSG), or firewall rules across AWS, Azure, and GCP that expose sensitive management or database ports (22, 3389, 3306, 5432, 6379, 9200, 27017) to the entire internet (0.0.0.0/0). Such configurations significantly increase the attack surface and potential for unauthorized access, brute-force attacks, or data exposure.
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 anomalous high-volume download or 'GetObject' operations from a single identity across multiple cloud storage objects and buckets within a short time window, which is indicative of bulk data exfiltration.
Detects high-risk AWS API calls indicative of privilege escalation within IAM (e.g., policy attachments, credential creation) or activities aimed at tampering with or disabling CloudTrail logging, which is often a precursor to or indication of unauthorized activity to evade detection.
Detects the creation of a new application secret or credential in a cloud environment followed by an immediate authentication attempt using that credential from an IP address that has not been observed performing successful authentication within the specified lookback window. This behavior is indicative of potential unauthorized persistence or the use of compromised credentials by an adversary.
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