Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the execution of Windows Script Host (WScript.exe or CScript.exe) that creates a file or folder starting with 'Libs_' within the 'C:\Users\Public\' directory. This activity is often associated with attackers staging payloads, configuration files, or tools in publicly writable folders to evade detection.
Detects suspicious file activity where a script or process creates a staging folder within 'C:\Users\Public\' and subsequently writes potential decoy PDF files or compressed archive payloads to that same directory. This behavior is indicative of a dropper or secondary stage malware delivery mechanism utilizing world-writable directories for staging.
Detects instances where the Notepad++ executable is launched from a location within the C:\Users\Public\ directory, specifically when the parent process is a Windows scripting engine (wscript.exe or cscript.exe). This pattern is often indicative of an adversary executing a malicious payload staged in a common public directory using script-based wrappers to bypass execution policy or maintain stealth.
This rule detects instances where Windows script hosts (wscript.exe or cscript.exe) create directories with suspicious naming conventions (starting with 'Libs_' or containing 'Temp Documents') within the 'C:\Users\Public\' directory. This activity is often associated with the staging of malicious payloads or collected data prior to exfiltration or execution.
Detects instances where the Windows Script Host (wscript.exe) spawns a process to open a PDF file located within the Public user's 'Temp Documents' directory. This behavior is indicative of a potential malware staging or execution attempt where an adversary attempts to open a malicious document from a publicly accessible folder to bypass user suspicion or execute malicious content.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects rapid, machine-speed, sequential attack-path adaptation by suspected AI agent binaries. The rule monitors for a correlation of four stages occurring within a tight 20-minute window: 1) Failed service connections across three or more distinct protocols (SMB, SSH, HTTP, RDP, WinRM), 2) Credential access attempts (Logon events or execution of known credential-dumping tools), and 3) A lateral movement pivot to a new, previously un-targeted internal destination. The rule specifically keys on the InitiatingProcessId and InitiatingProcessCreationTime to ensure the sequence is attributed to a single process instance, distinguishing it from human manual penetration testing activity. Covers T1190, T1210, T1078
Detects rapid, machine-speed, sequential attack-path adaptation by suspected AI agent binaries. The rule monitors for a correlation of four stages occurring within a tight 20-minute window: 1) Failed service connections across three or more distinct protocols (SMB, SSH, HTTP, RDP, WinRM), 2) Credential access attempts (Logon events or execution of known credential-dumping tools), and 3) A lateral movement pivot to a new, previously un-targeted internal destination. The rule specifically keys on the InitiatingProcessId and InitiatingProcessCreationTime to ensure the sequence is attributed to a single process instance, distinguishing it from human manual penetration testing activity. Covers T1190, T1210, T1078
Detects rapid, machine-speed, sequential attack-path adaptation by suspected AI agent binaries. The rule monitors for a correlation of four stages occurring within a tight 20-minute window: 1) Failed service connections across three or more distinct protocols (SMB, SSH, HTTP, RDP, WinRM), 2) Credential access attempts (Logon events or execution of known credential-dumping tools), and 3) A lateral movement pivot to a new, previously un-targeted internal destination. The rule specifically keys on the InitiatingProcessId and InitiatingProcessCreationTime to ensure the sequence is attributed to a single process instance, distinguishing it from human manual penetration testing activity. Covers T1190, T1210, T1078
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
Detects rapid, multi-category system and network enumeration performed by AI-assisted development tools (e.g., Cursor, Claude, ChatGPT). The rule triggers when an AI agent process performs a comprehensive scan of the host environment, specifically covering machine/OS identity, network configuration, process listings, installed software, and access to sensitive credential files within a 10-minute window. It requires at least 5 distinct discovery categories and 6 total events to reduce noise from routine debugging. Covers T1082, T1016, T1057, T1518
Detects the creation of files named 'parser.js' or 'loader.js' within hidden or suspicious VSCode directories (e.g., .npm/.vscode/ or AppData/VSCode/). The detection also flags potential signs of malicious intent, such as the existence of a 'package.json' file nearby or the presence of 'tokenapp' in the file path, which are often indicative of the GHAPPIER implant behavior.
Detects the execution of rundll32.exe with a command line containing 'DavWWWRoot', indicating the loading of a DLL from a remote WebDAV share. This is a common technique used by attackers to execute remote malicious code while bypassing local execution policies or attempting to evade local file-based detection.
Detects network connection events initiated by known beaconing-related process names. These processes are associated with command and control infrastructure or pivot nodes often used by adversaries for post-compromise activity.
Detects the execution of known Remote Monitoring & Management (RMM) software on Windows systems. This behavior is frequently abused by threat actors, including Scattered Spider, to maintain persistent access after gaining control via social engineering or identity-based attacks (e.g., vishing-driven credential or MFA resets).
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