Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
20,499 detections
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IDS Classtypes
1
This rule detects the execution of known lateral movement tools, including Impacket suite components and NetExec (formerly CrackMapExec), by monitoring process command lines. It specifically flags tools such as psexec.py, wmiexec.py, and secretsdump.py, which are frequently used for remote execution and credential dumping. The rule also joins these process events with network events targeting SMB port 445 to correlate tool usage with potential lateral movement activity.
Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
This rule detects the execution of known lateral movement tools, including Impacket suite components and NetExec (formerly CrackMapExec), by monitoring process command lines. It specifically flags tools such as psexec.py, wmiexec.py, and secretsdump.py, which are frequently used for remote execution and credential dumping. The rule also joins these process events with network events targeting SMB port 445 to correlate tool usage with potential lateral movement activity.
Detects file, process, and network activity associated with the TraderTraitor/KelpDAO malware campaign based on known hashes, filenames, paths, and C2 infrastructure.
Detects file, process, and network activity associated with the TraderTraitor/KelpDAO malware campaign based on known hashes, filenames, paths, and C2 infrastructure.
Detects a suspicious sequence where Microsoft Outlook launches Microsoft Edge to open a URL, and that browser process subsequently makes network connections to infrastructure associated with the ClickFix phishing campaign (e.g., rsvpopenh.one or specific malicious IP).
Detects suspicious behavior where an identical executable (same path and SHA256) is launched twice within a short window (60 minutes) following a system reboot, utilizing two distinct persistence methods: one triggered via the Task Scheduler (under svchost.exe -k netsvcs) and one via a user logon autostart mechanism (spawned by explorer.exe).
This rule detects a multi-stage attack chain involving the execution of a script (likely config.cmd) via curl, followed by the modification of the Active Setup StubPath registry key for persistence, and subsequent execution of a Python script (config.py) from a non-standard location in AppData\Local\Microsoft\.
Detects the use of reg.exe to query the MachineGuid value from the Windows Registry (HKLM\SOFTWARE\Microsoft\Cryptography). This behavior is often associated with adversary reconnaissance, specifically host fingerprinting, to uniquely identify compromised machines.
Detects the use of tasklist.exe combined with process filtering (findstr) to identify common debugger names such as 'dbg.exe', 'x64dbg.exe', 'windbg.exe', or 'ollydbg.exe'. This technique is a known evasion tactic employed by the Kimsuky threat group (and others) to check for the presence of analysis tools before continuing execution of malicious batch scripts.
Detects the execution of mshta.exe with command-line arguments that reference VLC-themed filenames or presentations. This behavior is indicative of a malicious HTA file being used as a lure to drop the Lightlife RAT loader, leveraging the mshta.exe utility to bypass standard application execution controls.
Detects the execution of PowerShell with a combination of flags commonly used to hide malicious activity: -NoProfile, -WindowStyle Hidden, and -EncodedCommand. This combination is frequently used by adversaries to execute obfuscated code silently, bypassing user visibility and script analysis.
Detects the execution of the Lightlife RAT via a PowerShell script named 'lightlife.ps1'. The rule monitors for processes running from the LOCALAPPDATA directory, the use of PowerShell with execution policy bypass and hidden window style, or execution triggered via common scripting hosts like wscript.exe or mshta.exe combined with PowerShell download cmdlets.
Detects exploit attempts targeting a Python prompt-injection vulnerability (CVE-2026-26030). The rule identifies command lines attempting to traverse Python's object hierarchy (e.g., using __class__, __base__, __subclasses__) to import the os module and execute system commands. It also flags subsequent child processes (e.g., calc.exe, cmd.exe) spawned by Python interpreters or Semantic Kernel host processes.
Detects instances where browser automation tools (e.g., Selenium, Puppeteer, Playwright) or headless browser instances initiate command-line interpreters or scripting hosts. This behavior is indicative of potential automated exploitation, such as browser-based attacks where a malicious script attempts to execute commands directly on the host operating system.
This rule detects scenarios where a browser or agent process downloads a file and subsequently initiates a local process that references the downloaded file within a very short timeframe (2 minutes). This pattern is indicative of automated or agentic behavior (such as AI-assisted malware execution) where a download is followed by immediate, non-human-mediated execution of the downloaded content.
This rule detects potential AI agent prompt injection by monitoring email subjects or attachment names for keywords often associated with system instruction overrides. It correlates these potentially malicious emails with subsequent execution of common administrative or script-interpreting binaries (e.g., PowerShell, cmd.exe) on the recipient's device within a one-hour window, suggesting a potential successful hijack of an automated process or AI agent.
Detects instances where a Semantic Kernel agent host process (or related .NET processes) spawns suspicious child processes such as command shells, network tools, or system enumeration utilities. This activity is indicative of potential command injection or remote code execution via AI agent frameworks, where an LLM-driven agent might be coerced into executing arbitrary commands on the underlying host.
Detects instances where an AI agent (e.g., Semantic Kernel) launches command-line tools commonly used to perform bulk data destruction or system wiping, such as file deletion commands, volume shadow copy removal, or disk formatting. This rule flags suspicious orchestration by AI agents that may be acting outside of expected parameters.
Detects attempts to bypass input sanitization in AI agent frameworks, specifically Semantic Kernel, by using Python bracket or subscript notation to access sensitive attributes or functions (e.g., __class__, __subclasses__, __import__, eval, exec). This technique is characteristic of filter injection attacks aiming to achieve remote code execution in AI agent environments.
Detects instances where a web browser process (e.g., Chrome, Edge, Firefox) spawns a command-line utility or scripting engine (e.g., PowerShell, WScript, Cmd) to execute a file located within common download or temporary directories. This behavior is a common indicator of a user executing a malicious file downloaded from the internet.
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