Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects instances where Microsoft Copilot accesses external resources, specifically identifying events where 'XPIADetected' is true. This indicates Copilot interacting with resources outside its immediate environment, which could be a security concern if the accessed resources are sensitive or untrusted.
This rule detects suspicious enumeration of Azure Blob, File, Table, Queue, or DFS storage accounts by internal users. It identifies devices making network connections to public Azure storage URLs using potentially suspicious processes (e.g., PowerShell, Python, AzCopy, curl, wget, WinSCP, FileZilla, Storage Explorer). The rule then analyzes the distinct storage accounts accessed and uses a Jaccard index to identify unusual naming conventions among the accessed storage accounts, potentially indicating an insider attempting to discover or exfiltrate data from unfamiliar storage resources.
Detects file access or download activity from SharePoint or OneDrive where the user agent is PowerShell. This TTP has been associated with UNC6671 (related to ShinyHunters) for data exfiltration from SaaS applications.
This rule detects successful 'GetObject' operations on AWS S3 buckets originating from external IP addresses (not within common private IP ranges). This could indicate an attempt to exfiltrate data from S3 buckets by an unauthorized entity or compromised credential.
This rule detects Amazon S3 PutObject events where server-side encryption is performed using SSE-C (Server-Side Encryption with Customer-Provided Keys) or where the request uses Signature Version 2 (SigV2). SSE-C relies on the customer to manage encryption keys, which can introduce risks if not handled securely. SigV2 is an older, less secure authentication protocol for AWS requests and its use is generally discouraged in favor of SigV4.
This rule detects when a high number of downloads have been performed by a user from SharePoint. This could indicate an insider threat collecting data before sending it outside of the company.
Detects access or search activity in SharePoint or OneDrive involving keywords related to gift cards, IT workflows, and remote access. This is a reconnaissance technique used by the Jingle Thief group (also tracked as Atlas Lion and STORM-0539) to find sensitive internal documentation.
The analytic detects the use of the curl command contacting suspicious remote domains, such as s3.amazonaws.com, which is indicative of Command and Control (C2) activity or downloading further implants. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs and command-line arguments. This activity is significant as it may indicate the presence of MacOS adware or other malicious software attempting to establish persistence or exfiltrate data. If confirmed malicious, this could allow attackers to maintain control over the compromised system and deploy additional payloads.
Detects the use of known malicious or suspicious User-Agent strings associated with the UNC6395 data theft campaign targeting Salesforce instances.
Detects suspicious activities in Salesforce environments consistent with the TTPs of the ShinyHunters threat group (also tracked as UNC6040/UNC6240). The attack involves social engineering (vishing) to trick users into authorizing malicious OAuth applications that impersonate legitimate tools like 'Salesforce Data Loader'. This rule looks for key indicators such as the installation of unreviewed packages, creation of impersonating connected apps, elevation of permissions to 'full' API access, and large data exports.
Detects a single S3 object download event where the volume of data transferred exceeds a defined threshold. This can be an indicator of bulk data exfiltration. Attackers may download large archives or databases from compromised S3 buckets.
KQL Query from file: Azure Cloud Security Monitoring
KQL Query
KQL Query from file: Azure Storage Blob - Misconfiguration Check
KQL Query from file: Azure Cloud Security Monitoring
The following analytic identifies anomalous GetObject API activity in AWS, indicating potential data exfiltration attempts. It leverages AWS CloudTrail logs and uses the `anomalydetection` command to detect unusual patterns in the frequency of GetObject API calls by analyzing fields such as "count," "user_type," and "user_arn" within a 10-minute window. This activity is significant as it may indicate unauthorized data access or exfiltration from S3 buckets. If confirmed malicious, attackers could exfiltrate sensitive data, leading to data breaches and compliance violations.
This query looks for users sharing access to files with external users.
This applies to SharePoint and OneDrive users.
Audit event and Cloud application identifier references.
Reference - https://learn.microsoft.com/microsoft-365/compliance/audit-log-sharing?view=o365-worldwide
Reference - https://learn.microsoft.com/azure/sentinel/entities-reference#cloud-application-identifiers
This applies to SharePoint and OneDrive users.
Audit event and Cloud application identifier references.
Reference - https://learn.microsoft.com/microsoft-365/compliance/audit-log-sharing?view=o365-worldwide
Reference - https://learn.microsoft.com/azure/sentinel/entities-reference#cloud-application-identifiers
"This Kusto (KQL) hunting query detects blob-enumeration or file-spraying behaviour in Azure Storage by:
- Aggregating requests into time-bound sessions with row_window_session().
- Defining a "user" as the combination of AccountName | UserAgentHeader, which is tolerant of rapid IP rotation.
- Raising an alert when, within any single session:
- The actor touches at least 10 distinct objects, and
- At least 50 % of those requests return an HTTP 4xx/5xx status, and
- Each object is accessed exactly once ("touch-once" pattern typical of enumeration).
All tuning knobs (look-back window, session gap, thresholds, authentication type) are exposed at the top of the query.
Be sure to validate the query against both benign and malicious samples in your own environment before relying on it for production detection.
Reference: https://techcommunity.microsoft.com/blog/microsoftdefendercloudblog/protect-your-storage-resources-against-blob-hunting/3735238"
- Aggregating requests into time-bound sessions with row_window_session().
- Defining a "user" as the combination of AccountName | UserAgentHeader, which is tolerant of rapid IP rotation.
- Raising an alert when, within any single session:
- The actor touches at least 10 distinct objects, and
- At least 50 % of those requests return an HTTP 4xx/5xx status, and
- Each object is accessed exactly once ("touch-once" pattern typical of enumeration).
All tuning knobs (look-back window, session gap, thresholds, authentication type) are exposed at the top of the query.
Be sure to validate the query against both benign and malicious samples in your own environment before relying on it for production detection.
Reference: https://techcommunity.microsoft.com/blog/microsoftdefendercloudblog/protect-your-storage-resources-against-blob-hunting/3735238"
This query helps hunting for BEC - File sharing tactics - Dropbox
Check all created files.
That does not have extension ps1, bat or cmd to avoid IT Pro scripts.
That are not copied to C:\ to detect all file share, external drive, data partition that are not allowed, etc.
This could help to detect malicious insider/user that has unencrypted data partition and that are using it to exfiltrate data even while removable devices & cloud storage is blocked.
That does not have extension ps1, bat or cmd to avoid IT Pro scripts.
That are not copied to C:\ to detect all file share, external drive, data partition that are not allowed, etc.
This could help to detect malicious insider/user that has unencrypted data partition and that are using it to exfiltrate data even while removable devices & cloud storage is blocked.
'This query combines different Storage alerts with CommonSecurityLogs and AuditLogs helping analysts investigate any possible storage related attacks faster
thus reducing Mean Time To Respond'
thus reducing Mean Time To Respond'
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