Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
18,128 detections
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Detects instances where a legitimate Remote Monitoring and Management (RMM) agent (MSP360 or Faronics) is used to execute PowerShell commands that silently install ScreenConnect (ConnectWise Control) MSI packages within a 10-minute window. This behavior is indicative of an adversary abusing administrative tools for lateral movement or persistence.
Detects instances where a legitimate Remote Monitoring and Management (RMM) agent (MSP360 or Faronics) is used to execute PowerShell commands that silently install ScreenConnect (ConnectWise Control) MSI packages within a 10-minute window. This behavior is indicative of an adversary abusing administrative tools for lateral movement or persistence.
This rule detects the execution of potentially malicious binaries masquerading as legitimate Windows utilities (e.g., Windows Update, Defender, or Phone Link) from within a ScreenConnect temporary directory. This behavior is indicative of a phishing attack where an adversary uses remote access tools to stage and execute malicious payloads on a victim's system.
Detects instances where a digitally signed process loads a DLL file from a user-writeable directory (such as Temp, AppData, Downloads, or Users\Public). This behavior is characteristic of DLL search order hijacking or side-loading, where an attacker attempts to load malicious code into a trusted, signed application.
Detects remote thread creation (CreateRemoteThread or QueueUserAPC) into common Windows system processes (rundll32.exe or svchost.exe) originating from suspicious source processes. This activity is indicative of process injection techniques commonly utilized by Cobalt Strike beacons during post-exploitation, lateral movement, or ransomware deployment phases.
Detects the loading of unsigned DLLs from non-system directories where the DLL filename matches common Windows system libraries. This behavior is indicative of DLL side-loading, where adversaries use a trusted, digitally-signed application to load a malicious DLL in an attempt to bypass security controls and maintain persistence.
Detects potential help-desk impersonation or social engineering activity involving account credential or MFA method resets, followed shortly by a sign-in from an unknown device or geography. The rule excludes activity tied to established ticketing systems or standard automated password expiration processes.
Detects potential help-desk impersonation or social engineering activity involving account credential or MFA method resets, followed shortly by a sign-in from an unknown device or geography. The rule excludes activity tied to established ticketing systems or standard automated password expiration processes.
Detects instances of mshta.exe initiating network connections to cloud storage providers (CloudFront, Cloudflare R2/pages) to retrieve multiple remote files (specifically .js or .txt). This behavior is indicative of an exploit stage where mshta.exe is used as a LOLBAS to fetch orchestrator scripts and gadget payloads for memory-based deserialization attacks.
Detects mshta.exe, wscript.exe, or cscript.exe initiating network connections to common CDN infrastructure (Cloudflare R2, CloudFront, pages.dev) to retrieve scripts (js, txt, log). This behavior is indicative of a staging phase in a malicious orchestration, often associated with subsequent deserialization attacks and DLL sideloading.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects evidence of the Antino backdoor performing sleep-masking shellcode evasion. The rule monitors for 'slc.dll' being sideloaded by 'GatherOsState.exe' and the subsequent invocation of memory management APIs (VirtualAlloc, NtAllocateVirtualMemory) paired with memory protection changes (VirtualProtect) or thread context modification, which are characteristic behaviors used by Antino to mask malicious shellcode in memory between execution intervals.
Detects cloud service principals identified as AI agents or assistants performing sensitive API operations such as IAM policy modifications or storage configuration changes. This pattern may indicate that an AI service account has been compromised or misused to perform reconnaissance, privilege escalation, or persistence, exceeding its intended scope of operation.
Detects activity from known Storm-3168 (JADEPUFFER) campaign IP addresses across Azure control-plane, sign-in, and App Service logs. The rule monitors for malicious indicators in Azure Activity logs, Azure Sign-in logs, and Service Principal sign-ins, as well as specific sensitive URI paths (e.g., shells, admin login paths) within App Service HTTP logs.
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 a precursor pattern identified as JADEPUFFER, where a failed 'ListKeys' operation (indicating an attempt to discover or access keys for a non-existent storage account) is immediately followed by a burst of destructive delete operations on critical cloud resources (e.g., storage, databases, key vaults) by the same actor.
This rule detects high-frequency HTTP GET requests containing abnormally long Base64-encoded strings within the URI. This pattern is often indicative of automated payload assembly or exfiltration where data is fragmented across multiple requests, or the use of URL-shortener services to facilitate C2 communication.
This rule detects potential data exfiltration by monitoring corporate user activities involving consumer generative AI services (e.g., ChatGPT, Gemini, Claude). It identifies suspicious patterns such as large volume submissions, repeated chunked prompts, and the presence of sensitive content (e.g., PII markers, classification labels) or obfuscated payloads (base64/rot13) that suggest an attempt to bypass DLP or detection controls.
Detects network requests to known domains (acrobat-reader-installer.com, 03webzoominvite.us) masquerading as Microsoft Store resources to deliver malicious MSI installers. This activity is associated with RMM-based RAT deployment chains.
Detects the presence of zero-width, non-printing, or bidirectional-override Unicode characters in command lines, commonly used for obfuscation or prompt injection.
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