Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
734 detections
Filters
Last updated
All Time
Detection languages
195
188
140
128
53
Contributors
229
147
111
94
34
Categories
613
214
127
93
73
Platforms
713
77
57
26
26
Products / Services
10,650
9,755
6,688
4,418
3,745
734
MITRE Techniques
294
276
143
124
106
CVEs
3
1
1
1
IDS Classtypes
5
5
4
2
IDS Protocols
16
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 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 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.
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.
Detects unauthorized sharing, copying, or exporting of EBS, disk, or database snapshots to external cloud accounts, subscriptions, or projects that are not present in the defined trusted allow-list for AWS, Azure, or GCP environments.
Detects attempts by a principal to modify, stop, or delete AWS CloudTrail logging configurations. Such activity is a common indicator of defense evasion during the early stages of a cloud-based attack to prevent audit trail capture.
Detects high-volume or multi-type destructive operations against cloud backup, snapshot, and recovery infrastructure, including the disabling of immutability policies or soft-delete protections. This activity is consistent with ransomware actors attempting to eliminate recovery options before encrypting data.
Detects administrative IAM operations such as user, role, or access key creation performed by an identity containing 'agent' in its ARN or username. This behavior is intended to flag unauthorized privilege escalation or persistence mechanisms initiated by automated service identities that should not be modifying IAM configurations.
Detects anomalous AWS Bedrock model invocation patterns where a single identity accesses models across three or more distinct AWS regions within a short timeframe. This behavior is indicative of quota evasion, where attackers bypass per-region rate limits to maximize throughput when leveraging compromised credentials for unauthorized LLM use.
This rule detects administrative changes to cloud storage configurations (AWS S3, Azure Blob, and GCP Storage) that result in public or anonymous read/write access to buckets or containers, which could lead to unauthorized data exposure.
Detects unauthorized or suspicious modifications to audit and logging configurations across AWS, Azure, and GCP. The rule identifies actions such as stopping, deleting, or updating logging trails, diagnostic settings, or logging sinks, which may indicate an adversary attempting to impair visibility into their activities (Anti-Forensics).
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.
Page 2 of 35