Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
226 detections
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This rule detects unauthorized or suspicious automated scraping behavior by identifying periodic, non-interactive tasks that perform high-volume outbound network requests to multiple external hosts. It specifically filters for processes launched via scheduling mechanisms (Task Scheduler, cron) that exhibit indicators of anti-bot or proxy-rotation bypass techniques (such as usage of headless browser tools or proxy-related keywords in command lines). The rule is tuned to ignore legitimate system maintenance, update, and monitoring processes.
This rule detects unauthorized or suspicious automated scraping behavior by identifying periodic, non-interactive tasks that perform high-volume outbound network requests to multiple external hosts. It specifically filters for processes launched via scheduling mechanisms (Task Scheduler, cron) that exhibit indicators of anti-bot or proxy-rotation bypass techniques (such as usage of headless browser tools or proxy-related keywords in command lines). The rule is tuned to ignore legitimate system maintenance, update, and monitoring processes.
This rule detects potential bulk data exfiltration by monitoring for high volumes of file access events (FileAccessed, FileRead, FileModified) originating from the 'doc_helper.aspx' file-management web shell. It summarizes activity by device and user account, flagging instances where over 100 unique files are touched within a short timeframe, which is indicative of automated collection and exfiltration activities.
This rule detects potential bulk data exfiltration by monitoring for high volumes of file access events (FileAccessed, FileRead, FileModified) originating from the 'doc_helper.aspx' file-management web shell. It summarizes activity by device and user account, flagging instances where over 100 unique files are touched within a short timeframe, which is indicative of automated collection and exfiltration activities.
Detects a sequence of events starting with a device code OAuth sign-in, followed within two hours by the creation or modification of inbox rules and high-frequency enumeration of email items (contacts, folders, messages, threads, people) via Microsoft Graph API. This behavior is indicative of post-compromise activity where an adversary uses OAuth device code grants to maintain access and subsequently automates inbox analysis or data harvesting.
This rule detects potential bulk data exfiltration by monitoring for high volumes of file access events (FileAccessed, FileRead, FileModified) originating from the 'doc_helper.aspx' file-management web shell. It summarizes activity by device and user account, flagging instances where over 100 unique files are touched within a short timeframe, which is indicative of automated collection and exfiltration activities.
This rule detects potential bulk data exfiltration by monitoring for high volumes of file access events (FileAccessed, FileRead, FileModified) originating from the 'doc_helper.aspx' file-management web shell. It summarizes activity by device and user account, flagging instances where over 100 unique files are touched within a short timeframe, which is indicative of automated collection and exfiltration activities.
MacSync Stealer (formerly known as Mac.c) is a macOS information stealer offered as a Malware-as-a-Service (MaaS). On May 5, 2026, researchers identified a new C2 domain (jacksonvillemma[.]com) delivering the malware's Stage 2 loader. Notably, this new infrastructure was active just days after the operator's previous C2 domain was publicly exposed, highlighting the threat actor's agility in rotating their delivery architecture.
Detects bulk SharePoint/M365 file access or download either from a known UNC6671 exfiltration IP, or via a scripting-library user agent that is new for the user and exceeds a volume threshold within a short session window.
Detects potential data exfiltration attempts using PowerShell by identifying Invoke-WebRequest usage with specific flags (Put, ContentType, InFile) or encoding patterns (EncodedCommand) while targeting sensitive file extensions, directories, or suspicious network destinations.
This hunting query detects potential data exfiltration by identifying users whose outbound data volume to cloud storage and messaging platforms: (OneDrive, Dropbox, Google Drive, SharePoint, Telegram) spikes dramatically compared to their own historical behavior.
It uses a per-user adaptive baseline approach rather than static thresholds, making it more resilient to normal variance in user upload habits while still flagging statistically abnormal spikes.
MITRE ATT&CK Mapping
Tactic: Exfiltration (TA0010)
Technique: T1567 – Exfiltration Over Web Service
Sub-technique T1567.002 – Exfiltration to Cloud Storage
Data Source
DeviceNetworkEvents (Microsoft Defender for Endpoint / Microsoft 365 Defender advanced hunting schema)
Logic Breakdown:
Baseline window (31 days, excluding the most recent day):
The query calculates each user's average daily upload volume (BytesSent) to a defined set of cloud storage/messaging destinations over the trailing 31 days, ending 1 day ago. This establishes a personal historical norm per user.
Global peer baseline fallback:
For users with little or no prior upload history (average below minBaselineFloor = 1 MB), the query substitutes a global average across all users (globalPeerBaseline) — or the 1 MB floor, whichever is higher — to avoid division-by-near-zero producing artificially inflated ratios for low-activity users.
Today's upload volume:
Sums BytesSent to the same destination patterns over the last 24 hours per user.
Ratio comparison:
Joins today's upload volume against each user's effective baseline and computes UploadRatio = TodayUpload / EffectiveBaseline.
Alert conditions (both must be true):
UploadRatio > 5 — today's upload is more than 5x the user's normal daily average.
TodayUpload > minUploadFloor (100 MB) — an absolute floor ensuring small, low-value increases (e.g., 1 KB → 6 KB) don't trigger noise alerts even if the ratio is high.
Output: Results sorted by UploadRatio descending, surfacing the most anomalous users firs
It uses a per-user adaptive baseline approach rather than static thresholds, making it more resilient to normal variance in user upload habits while still flagging statistically abnormal spikes.
MITRE ATT&CK Mapping
Tactic: Exfiltration (TA0010)
Technique: T1567 – Exfiltration Over Web Service
Sub-technique T1567.002 – Exfiltration to Cloud Storage
Data Source
DeviceNetworkEvents (Microsoft Defender for Endpoint / Microsoft 365 Defender advanced hunting schema)
Logic Breakdown:
Baseline window (31 days, excluding the most recent day):
The query calculates each user's average daily upload volume (BytesSent) to a defined set of cloud storage/messaging destinations over the trailing 31 days, ending 1 day ago. This establishes a personal historical norm per user.
Global peer baseline fallback:
For users with little or no prior upload history (average below minBaselineFloor = 1 MB), the query substitutes a global average across all users (globalPeerBaseline) — or the 1 MB floor, whichever is higher — to avoid division-by-near-zero producing artificially inflated ratios for low-activity users.
Today's upload volume:
Sums BytesSent to the same destination patterns over the last 24 hours per user.
Ratio comparison:
Joins today's upload volume against each user's effective baseline and computes UploadRatio = TodayUpload / EffectiveBaseline.
Alert conditions (both must be true):
UploadRatio > 5 — today's upload is more than 5x the user's normal daily average.
TodayUpload > minUploadFloor (100 MB) — an absolute floor ensuring small, low-value increases (e.g., 1 KB → 6 KB) don't trigger noise alerts even if the ratio is high.
Output: Results sorted by UploadRatio descending, surfacing the most anomalous users firs
This rule detects anomalous, high-volume file access activity within SharePoint by monitoring the 'FileAccessed' operation. It calculates a 14-day rolling baseline for each user and triggers an alert when the hourly 'FileAccessed' count significantly exceeds this baseline, specifically when no corresponding 'FileDownloaded' events are present. This behavior aims to detect stream-based data exfiltration techniques that bypass standard download-based DLP controls.
Detects anomalous spikes in per-user M365 (SharePoint and Exchange) activity volume by comparing current 15-minute intervals against a 30-day rolling baseline (mean + 3 standard deviations). This behavior may indicate mass data staging or exfiltration activities common in precursor phases of extortion or data theft.
This rule detects potential data exfiltration via email by identifying mass near-identical background-image URLs in the body of an email. The rule looks for a high volume (8+) of distinct URLs that contain short parameters (indicative of encoded or smuggled data) within a single email. It further refines the detection by checking for the presence of specific CSS-like syntax often used in CSS-injection keylogging techniques to force the browser or email client to send background-image requests.
This rule detects potential data exfiltration via email by identifying mass near-identical background-image URLs in the body of an email. The rule looks for a high volume (8+) of distinct URLs that contain short parameters (indicative of encoded or smuggled data) within a single email. It further refines the detection by checking for the presence of specific CSS-like syntax often used in CSS-injection keylogging techniques to force the browser or email client to send background-image requests.
UNC6671 - Outbound connection to known automated SaaS data exfiltration IP, excluding maintained allowlist of legitimate backup/sync/CDN and sanctioned bulk-transfer destinations
Detects anomalous, high-volume access, download, or synchronization activity in SharePoint or OneDrive within a one-hour window. The rule focuses on non-interactive user agents typically used by scripting libraries (e.g., Python requests, PowerShell, Go-http-client) and flags activity that exceeds an individual user's 21-day rolling baseline, suggesting potential automated data exfiltration or mass collection.
Detects command-line execution associated with WhatsApp API contact synchronization, specifically looking for 'WPP.contact.getAllContacts' or 'WPP.contact.list' commands. This pattern may indicate automated or unauthorized scraping of contact lists via a messaging interface.
Detects a sequence of file write operations followed by file read operations on specific files named 'AGENTS.md', 'MEMORY.md', 'IDENTITY.md', 'USER.md', or 'SOUL.md' with a significant time gap between the operations. This pattern is indicative of data staging, where an adversary writes sensitive information to a file and subsequently reads it for exfiltration.
Detects access to sensitive browser internal pages (profile-internals) via command line or the generation of error PDF files, which may indicate browser-based reconnaissance, credential harvesting, or data exfiltration attempts.
Detects unusually large outbound network data transfers (over 1TB) from a single process account within an hour. This could indicate data exfiltration, unauthorized bulk data synchronization, or other malicious activity where large volumes of data are being moved out of the environment.
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