Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 GitHub events indicating data or asset exposure, such as creating public repositories with specific naming conventions, changing private repositories to public, or creating new gists, which could indicate exfiltration of sensitive assets or information.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
This rule detects anomalous spikes in network connections from a device to major LLM provider API endpoints (OpenAI, Azure OpenAI, Anthropic) by establishing a baseline for each device and identifying deviations exceeding a statistical threshold. Such behavior may indicate unauthorized data exfiltration or mass data processing using LLM services.
Detects a burst of file access or download operations from SharePoint or OneDrive by a single user account within a short timeframe. This behavior is indicative of bulk data collection or exfiltration, often associated with post-credential-theft activity where an attacker stage and extract organizational data.
This detection identifies execution of the file synchronization utility "rclone".
It leverages Cisco Network Visibility Module logs, specifically flow data in order to capture process executions
initiating network connections.
While rclone is a legitimate command-line tool for syncing data to cloud storage providers, it has been widely abused by threat actors for data exfiltration.
This analytic inspects process name and arguments for rclone and flags usage of suspicious flags.
If matched, this could indicate malicious usage for stealthy data exfiltration or cloud abuse.
It leverages Cisco Network Visibility Module logs, specifically flow data in order to capture process executions
initiating network connections.
While rclone is a legitimate command-line tool for syncing data to cloud storage providers, it has been widely abused by threat actors for data exfiltration.
This analytic inspects process name and arguments for rclone and flags usage of suspicious flags.
If matched, this could indicate malicious usage for stealthy data exfiltration or cloud abuse.
Detects anomalous, high-volume data retrieval from Salesforce via OAuth-authenticated Connected Apps using SOQL or Bulk API. This behavior is indicative of a compromised integration token being used by an adversary to exfiltrate sensitive CRM data, such as User, Account, Case, or Opportunity records, by bypassing standard user interface workflows.
Detects instances where a browser extension is installed, followed within a two-hour window by the browser process making network connections to generative AI service domains. This behavior is indicative of potential malicious browser extension activity, such as scraping or exfiltrating session/browsing data to third-party AI backend services.
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.
This rule detects typical behavior associated with infostealers targeting browser data, specifically the creation of compressed archives (.zip, .rar) in temporary directories (Temp or AppData) and the subsequent exfiltration of data via known webhook services (e.g., Discord, Telegram, Pastebin) or direct IP connections. It filters out common signed backup and synchronization software to minimize noise.
Detects the use of cloud synchronization utilities such as rclone (including renamed binaries) or Mega command-line tools to interact with cloud storage services. The rule correlates this execution with preceding mass file enumeration or access activity on the same host, which is a common pattern in ransomware double-extortion scenarios.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects usage of the Rclone utility (or renamed binaries) to transfer data to various cloud storage backends (S3, Mega, pCloud, SFTP, WebDAV). The detection correlates this activity with the execution of common archival tools (7-Zip, WinRAR, TAR) within one hour prior to the data transfer, which is a common behavior of adversaries preparing data for exfiltration.
Detects outbound network traffic associated with the Azazel operation, including connections to identified C2 infrastructure, open directory/upload listeners, domain-specific staging servers, and exfiltration attempts to MEGA cloud storage endpoints.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects potential insider threats or departing employees by correlating abnormal bulk data downloads from cloud repositories (SharePoint/OneDrive/Box/etc.) with subsequent sensitive data access and exfiltration signals. It uses baseline behavioral profiling, requires a threshold of sensitive file interactions, and mandates high-confidence exfiltration indicators (removable media usage, suspicious external domains, or multiple combined destination events) to reduce noise. Archive/staging activity is used as an optional confidence booster.
Detects high-risk GitHub events indicating data or asset exposure, such as creating public repositories with specific naming conventions, changing private repositories to public, or creating new gists, which could indicate exfiltration of sensitive assets or information.
Detects the creation or exposure of public repositories, releases, or gists under a personal GitHub account that utilize naming conventions associated with the 'PixelLeak' workaround. This technique allows adversaries to make previously private screenshots, recordings, or internal assets publicly accessible by hosting them in public personal repositories or releases.
Detects anomalous, high-volume file download or export activity originating from a single identity across multiple downstream cloud applications or tenants within a one-hour window. This behavior is indicative of a compromised shared service provider, MSP, or clearinghouse account being leveraged to exfiltrate data from multiple client environments simultaneously. The detection excludes pre-approved service accounts and relies on a dynamic baseline comparison rather than a static threshold.
Detects the BotHelper RAT performing live screen surveillance by identifying repeated high-frequency JPEG frame uploads to the '/api/v1/screen_live.php' endpoint, initiated by the 'msedge_proxy.exe' process.
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