Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects Node.js processes making outbound network connections to the AWS Instance Metadata Service (IMDS) IP address 169.254.169.254, which may indicate unauthorized data collection or discovery via server-side request forgery (SSRF).
Detects attempts to tamper with, disable, or delete audit logging configurations in AWS CloudTrail and Azure Monitor. Monitoring these activities is critical to ensure that audit logs remain intact for security investigations.
Detects high-volume authentication failures originating from a single source IP targeting multiple unique user accounts, indicative of password spraying or credential stuffing attacks against cloud identity providers.
Detects an identity that performs a 'ListSecrets' operation followed by a high volume of 'GetSecretValue' or 'BatchGetSecretValue' requests within a short timeframe (15 minutes). This behavioral pattern is indicative of automated bulk exfiltration of secrets from AWS Secrets Manager, a common activity following an initial cloud environment compromise.
Detects high-risk IAM policy modifications, specifically the attachment or creation of policies that grant wildcard permissions or full administrator access. The rule optionally correlates these permission changes with the recent creation of access keys or service account credentials for the same principal, indicating potential preparation for persistence or unauthorized access escalation.
Detects high-risk AWS IAM privilege escalation activities, such as attaching AdministratorAccess policies, creating new access keys, roles, or users, occurring shortly after a suspicious sign-in event (e.g., impossible travel or new device login with MFA) for the same principal.
Detects bulk read operations on AWS Secrets Manager or similar cloud secret stores where the accessed resources contain keywords related to cryptocurrency custody, private keys, or wallet material. This pattern is indicative of unauthorized exfiltration attempts by threat actors such as Slim Spider targeting digital asset infrastructure.
Detects the use of common command-line tools (curl, wget, python, etc.) to interact with known cloud provider metadata service IP addresses or URL paths, often performed by shell processes. This behavior is frequently associated with initial reconnaissance or attempts to steal identity tokens from cloud-based virtual machines.
This rule detects a suspicious sequence of AWS CloudTrail events where a single identity performs a burst of discovery and enumeration activities (reconnaissance) followed shortly by requests to access sensitive secrets, credentials, or sensitive files (e.g., .env files). This pattern is indicative of an attacker who has gained initial access and is pivoting to identify and exfiltrate credentials.
Detects anomalous bursts of cloud secret or parameter retrieval API calls followed shortly by administrative actions that expose data or credentials (such as bucket policy changes or new key creation). This behavior is consistent with an adversary staging compromised credentials to exfiltrate data or maintain persistence.
Detects when a namespace-scoped identity creates, updates, or patches AWS or Azure IAM-related custom resources in Kubernetes. This pattern monitors for a 'confused deputy' scenario where a user might attempt to leverage high-privilege cloud operators (like ACK or ASO v2) that operate with a shared high-privilege identity to modify cloud IAM configurations.
Detects when a namespace-scoped identity creates, updates, or patches AWS or Azure IAM-related custom resources in Kubernetes. This pattern monitors for a 'confused deputy' scenario where a user might attempt to leverage high-privilege cloud operators (like ACK or ASO v2) that operate with a shared high-privilege identity to modify cloud IAM configurations.
Detects when a namespace-scoped identity creates, updates, or patches AWS or Azure IAM-related custom resources in Kubernetes. This pattern monitors for a 'confused deputy' scenario where a user might attempt to leverage high-privilege cloud operators (like ACK or ASO v2) that operate with a shared high-privilege identity to modify cloud IAM configurations.
Detects multiple AWS API calls (Publish, GetCallerIdentity, ListTopics) originating from a specific IP address (23.234.84.102) associated with a Zapier SNS sender. This rule monitors for potentially unauthorized or anomalous API usage patterns by identifying repeated, successful, and non-error events from a known source that may indicate misuse or compromise of an integration account.
Detects high-volume outbound network scanning activity from a single compute resource, characterized by a significant number of distinct destination IP addresses and ports within a short timeframe. This behavior is indicative of automated service discovery and reconnaissance, often associated with adversary-orchestrated exploitation pipelines or botnet activity.
This analytic identifies instances where an internal host has attempted to communicate with 250 or more destination IP addresses using on of the NMAP top 20 ports. Horizontal port scans from internal hosts can indicate reconnaissance or scanning activities, potentially signaling malicious intent or misconfiguration. By monitoring network traffic logs, this detection helps detect and respond to such behavior promptly, enhancing network security and preventing potential threats.
Detects high-velocity retrieval of multiple secrets from AWS Secrets Manager using a single IAM role or session shortly after activity initiation. This behavior is indicative of automated credential harvesting, often occurring post-compromise of a host (e.g., an SSH bastion) that has been assigned a high-privilege IAM role.
Detects 'GetSecretValue' API calls to AWS Secrets Manager originating from programmatic agents like Boto3, Python requests, or the AWS CLI. This monitors for automated retrieval of stored secrets, which may indicate unauthorized credential access or exfiltration from an AWS environment.
Detects potential model weight poisoning or unauthorized integrity compromise by identifying training jobs launched by non-authorized users, model file modifications in production paths that bypass CI/CD approval gates, or integrity mismatches between deployed model artifacts and their corresponding approved training outputs.
Detects potential model weight poisoning or unauthorized integrity compromise by identifying training jobs launched by non-authorized users, model file modifications in production paths that bypass CI/CD approval gates, or integrity mismatches between deployed model artifacts and their corresponding approved training outputs.
Detects anomalous, high-velocity enumeration of IAM entities and cloud infrastructure resources by a single principal within a 5-minute window. This behavior is consistent with automated reconnaissance performed by agentic AI or scripted tools, often used to map cloud environments prior to exploitation.
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