Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
361 detections
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Detects the use of open-source secret-scanning tools (e.g., TruffleHog) by identifying their specific User-Agent strings in AWS CloudTrail logs during GetCallerIdentity calls, followed by suspicious enumeration activity (ListBuckets, ListSecrets, ListAccessKeys) from the same source IP. This behavior is indicative of threat actors validating stolen credentials for unauthorized access to cloud resources.
Detects a service principal or user account performing a burst of 'ListKeys' operations across multiple Azure Storage accounts. This behavior is indicative of an attacker attempting to dump access keys for multiple storage accounts after initial compromise or privilege escalation to gain further access to sensitive cloud data.
Detects anomalous, unauthorized access to machine learning model artifacts (e.g., .onnx, .pt, .h5) within object storage or model registries. The rule identifies suspicious patterns including high-volume file reads by non-ML team identities, lack of multi-factor authentication, or access from geolocations outside approved operating regions, indicative of potential model exfiltration or intellectual property theft.
Detects usage of AWS CloudShell followed by S3 data-plane activity (GetObject/PutObject) from users without a documented baseline or exhibiting geographic anomalies. This pattern is indicative of potential misuse of a pre-authenticated execution context to perform unauthorized data staging or access, often evading host-based EDR monitoring.
Detects anomalous, high-volume file download or sync activity by a single user within a 10-minute window in M365 (SharePoint/OneDrive). This behavior is indicative of bulk data exfiltration, potentially occurring after a session hijacking or account takeover, where the attacker leverages legitimate user credentials to access and export sensitive data.
Detects an anomalous volume of file downloads from cloud storage platforms (SharePoint, OneDrive, Google Drive, Box) occurring within two hours of a high or medium risk sign-in event, including impossible travel or token replay scenarios. This pattern is indicative of potential data exfiltration by an actor using compromised credentials.
Detects a service principal or user account performing a burst of 'ListKeys' operations across multiple Azure Storage accounts. This behavior is indicative of an attacker attempting to dump access keys for multiple storage accounts after initial compromise or privilege escalation to gain further access to sensitive cloud data.
Detects a service principal or user account performing a burst of 'ListKeys' operations across multiple Azure Storage accounts. This behavior is indicative of an attacker attempting to dump access keys for multiple storage accounts after initial compromise or privilege escalation to gain further access to sensitive cloud data.
Detects anomalous AWS IAM and Secrets Manager API activity indicative of credential abuse following suspected exposure. The rule identifies suspicious patterns such as unauthorized enumeration of access keys, secret retrieval, and creation of new keys by non-service principals, reflecting activity associated with ShinyHunters-style operations where compromised credentials are abused.
Detects the ingestion of documents into production RAG (Retrieval-Augmented Generation) indexes from untrusted or external-facing sources, which may indicate an attempt to perform RAG poisoning attacks.
Detects anomalous access to SharePoint or OneDrive by identities identified as bots, agents, or service principals. The rule specifically monitors for multiple file accesses where the file names suggest the presence of secrets, passwords, or API keys, which may indicate a compromised or malicious AI agent attempting to harvest credentials.
Detects anomalous, high-volume file access or preview activity in OneDrive or SharePoint from non-browser user agents. This pattern is indicative of potential data exfiltration where adversaries use scripted clients or APIs to mass-read or preview files, bypassing traditional download-based alerts.
Detects anomalous, high-volume file access or preview activity in OneDrive or SharePoint from non-browser user agents. This pattern is indicative of potential data exfiltration where adversaries use scripted clients or APIs to mass-read or preview files, bypassing traditional download-based alerts.
Detects automated mailbox monitoring (mailbox access cadence) followed by searches for sensitive finance-related keywords (e.g., invoice, payment, wire, ACH). This pattern is characteristic of a 'Box Monitor' or 'Inbox Monitor' actor, which maintains persistence via API polling to exfiltrate financial information from a compromised account.
This rule monitors for high-frequency download or access events of files associated with AI development, such as model weights (.pt, .ckpt, .safetensors, .h5, .onnx, .pkl, .pb), checkpoints, and research datasets from cloud storage. It triggers when an account accesses or downloads 20 or more unique AI-related files within a 60-minute window, which is indicative of unauthorized data exfiltration of intellectual property.
This rule detects the unauthorized download of complete ML model checkpoint or weight files from a model registry or object storage. It monitors for common model file extensions in download events that are not associated with a legitimate deployment justification or a pre-approved change management window, helping to identify potential intellectual property theft or model exfiltration attempts.
Detects a single identity listing access keys for a high volume (20+) of Azure Storage Accounts within a 15-minute window. This behavior is indicative of credential harvesting, reconnaissance, or automated exfiltration attempts targeting storage resources.
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.
This rule detects potentially compromised accounts by correlating suspicious authentication activity (such as risky sign-ins, impossible travel, or malicious OAuth/Mail-rule configurations) with a subsequent spike in SharePoint or OneDrive file access, downloads, or external link sharing within a one-hour window. This behavior is indicative of an adversary performing cloud-based data exfiltration after gaining unauthorized access to an account.
Detects high-volume file download activity in Office 365/SharePoint/OneDrive audit logs. The rule correlates download spikes with users who have recently triggered security alerts related to session hijacking, token theft, or risky sign-ins, which are indicators of a potential cloud account takeover scenario.
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