Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects high-volume outbound network traffic (exceeding 50MB) directed towards known consumer-grade cloud storage and anonymous file-sharing services. This activity can indicate data exfiltration, as these services are frequently abused to bypass security controls while masking data movement within legitimate-appearing traffic.
This rule detects potential data exfiltration by correlating access to files labeled as sensitive (confidential, restricted, etc.) with a subsequent upload to a personal cloud storage provider or transmission via email to a personal domain within a two-hour window.
Detects the execution of the 'gitshot' binary, which is known to automatically create public repositories and release tags to host screenshots or screen recordings. This behavior can be abused by insiders or attackers to exfiltrate sensitive data outside of corporate control.
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.
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 a suspicious sequence of events where a remote access tool (RAT) is installed on a device, followed by significantly elevated and anomalous database query activity performed by the same account over an extended period. The rule utilizes a baseline window to identify deviations from normal query volumes while excluding known service accounts and administrative ETL/BI tasks.
Detects a suspicious sequence of events where a remote access tool (RAT) is installed on a device, followed by significantly elevated and anomalous database query activity performed by the same account over an extended period. The rule utilizes a baseline window to identify deviations from normal query volumes while excluding known service accounts and administrative ETL/BI tasks.
Detects a suspicious sequence of events where a remote access tool (RAT) is installed on a device, followed by significantly elevated and anomalous database query activity performed by the same account over an extended period. The rule utilizes a baseline window to identify deviations from normal query volumes while excluding known service accounts and administrative ETL/BI tasks.
Detects the installation of known AI-powered coding assistant extensions (e.g., Copilot, Tabnine, Cursor) on a device, followed by significant source-code repository cloning or archival activity by Git within two hours. This pattern is potentially indicative of intellectual property theft or sensitive source code exfiltration facilitated by a newly introduced AI tool.
This rule detects a single device making a high volume of connections to known machine learning model and dataset hosting repositories (e.g., Hugging Face, Kaggle, TensorFlow) within a short timeframe. Such activity may indicate bulk exfiltration of sensitive internal research data, proprietary models, or large datasets.
Detects a multi-stage exfiltration sequence consisting of: (1) bulk file archival/compression using common utilities, (2) suspicious OAuth grant or service principal authorization, and (3) abnormal network egress volume to common ports. This pattern suggests an adversary is preparing data, establishing or leveraging an identity/application for cloud access, and subsequently exfiltrating data.
This rule detects network traffic associated with Lumma Stealer information-stealing malware, specifically identifying HTTP POST requests that utilize the 'TeslaBrowser/5.5' user agent. This pattern is indicative of the malware exfiltrating stolen data to its Command and Control (C2) server.
This rule correlates multiple telemetry sources to detect a sophisticated cloud identity compromise chain. It identifies suspicious MFA-satisfied logins (potentially via Adversary-in-the-Middle) followed by the granting of high-risk OAuth application consents, the creation of persistent mailbox rules for hiding or forwarding communications, and finally mass data access or exfiltration from the user's account.
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 DNS tunneling attempts by identifying DNS queries with abnormally long subdomains (greater than 50 characters) that exhibit a high request rate (20 or more queries within 60 seconds). This behavior is characteristic of C2 communication tools such as iodine or dnscat2, which encode data within DNS query labels to bypass traditional network security controls.
Detects a credential-theft pattern associated with Psychedelic Stealer where a process accesses the 'Login Data' file of common Chromium-based browsers, followed shortly by a network connection to a known stealer exfiltration API endpoint.
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.
This rule detects outgoing HTTP POST requests to the '/api/credentials' endpoint, which is characteristic of the AMOS (Atomic macOS Stealer) malware family communicating with its command-and-control server to exfiltrate stolen sensitive information.
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).
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