Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
40,056 detections
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Detects processes using Python shutil.copyfile to harvest sensitive browser data files from Chromium-based profiles.
Detects suspicious Kerberos ticket requests via command line using System.IdentityModel.Tokens.KerberosRequestorSecurityToken class.
Threat actors may use command line interfaces to request Kerberos tickets for service accounts in order to
perform offline password cracking attacks commonly known as Kerberoasting or other Kerberos ticket abuse
techniques like silver ticket attacks.
Threat actors may use command line interfaces to request Kerberos tickets for service accounts in order to
perform offline password cracking attacks commonly known as Kerberoasting or other Kerberos ticket abuse
techniques like silver ticket attacks.
This rule detects potential COM hijacking of the CLSID InprocServer32 registry keys or unauthorized loading/creation of the 'EhStoreShell.dll' file. These behaviors are common indicators of persistence mechanisms or DLL side-loading where attackers redirect legitimate system calls to malicious code.
This rule detects potential COM hijacking of the CLSID InprocServer32 registry keys or unauthorized loading/creation of the 'EhStoreShell.dll' file. These behaviors are common indicators of persistence mechanisms or DLL side-loading where attackers redirect legitimate system calls to malicious code.
This rule monitors for process creation or file interaction events associated with a specific SHA256 file hash identified as malicious. It uses Microsoft Defender for Endpoint (MDE) logs to correlate both process execution and file activity related to this indicator of compromise.
This rule detects attempts to disable, terminate, or misconfigure security software, including antivirus, EDR, and endpoint protection solutions, by monitoring the execution of system administration binaries (such as taskkill, net, sc, wmic, powershell) with command-line arguments indicating service or process stopping, deletion, or configuration changes related to security-specific product names.
This rule detects potential attempts to tamper with Microsoft Defender security settings by creating or managing scheduled tasks that modify Defender exclusion paths. It correlates process execution (specifically schtasks.exe or PowerShell commands used for setting exclusions) with Windows Registry events that modify the Microsoft Defender exclusion path registry keys, flagging suspicious activity within a 15-minute window.
This rule detects network connections and HTTP requests to known malicious URLs, including file downloads associated with C2 infrastructure and malware distribution campaigns. It monitors both direct device network events and HTTP requests logged through security product events to identify potential secondary-stage payload delivery.
Detects anomalous clipboard activity where content is repeatedly replaced by cryptocurrency wallet address patterns (Bitcoin or Ethereum) within a short timeframe. This behavior is indicative of clipboard hijacking (clipjacking), a technique used by malware like EggJagger to substitute legitimate payment addresses with attacker-controlled addresses.
Detects anomalous clipboard activity where content is repeatedly replaced by cryptocurrency wallet address patterns (Bitcoin or Ethereum) within a short timeframe. This behavior is indicative of clipboard hijacking (clipjacking), a technique used by malware like EggJagger to substitute legitimate payment addresses with attacker-controlled addresses.
This rule detects potential Server-Side Request Forgery (SSRF) activity where web application or runtime processes attempt to access the Cloud Instance Metadata Service (IMDS) or container task metadata endpoints. By monitoring network connections and application logs, the rule filters out known legitimate metadata clients and identifies suspicious processes frequently associated with web-based vulnerabilities that are repeatedly querying sensitive metadata paths.
This rule detects potential Server-Side Request Forgery (SSRF) activity where web application or runtime processes attempt to access the Cloud Instance Metadata Service (IMDS) or container task metadata endpoints. By monitoring network connections and application logs, the rule filters out known legitimate metadata clients and identifies suspicious processes frequently associated with web-based vulnerabilities that are repeatedly querying sensitive metadata paths.
Detects automated reconnaissance and enumeration of sensitive web application paths (admin, config, environment files, etc.) from a single source IP. The rule identifies high-frequency request patterns that target specific non-public surface areas while excluding known search engine crawlers and monitoring bots.
Detects anomalous activity patterns consistent with automated firmware reverse engineering pipelines, specifically correlating the high-frequency execution of firmware analysis tools (e.g., binwalk, Ghidra) with the creation of vulnerability research knowledge base artifacts. This behavioral heuristic aims to identify potential AI-driven or automated zero-day discovery workflows by tracking tool usage density and output characteristics.
Detects artifacts of the winAgent v2.0 Windows implant line, including mod_* modules, WUEngine persistence binaries, and COM/CLSID hijack strings used by AI-assisted implant development
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 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 suspicious command executions (e.g., whoami, downloadstring, iex, registry modifications) initiated by processes associated with the CrowdStrike Falcon agent. It also correlates these executions with the loading of unsigned or unverifiable DLLs by the same Falcon processes within a 5-minute window, which may indicate attempts to tamper with or masquerade as the security agent.
Detects the creation, modification, or renaming of PHP files within the 'wp-content/uploads' directory of a WordPress installation. This pattern is commonly indicative of an attacker uploading a web shell to maintain persistence or execute arbitrary code on a compromised web server.
Detects the execution of PowerShell with suspicious command-line arguments (e.g., encoded commands, hidden windows) initiated by mshta.exe, excluding instances where a .ps1 script file is involved. This pattern often indicates attempts to bypass execution policy or run obfuscated payloads in memory, which is a common behavior of malicious HTA files.
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