Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects instances where wscript.exe spawns iexplore.exe. This behavior is highly suspicious because Microsoft Windows Script Host (wscript.exe) is typically used for running VBScript or JScript files, and it rarely requires launching Internet Explorer (iexplore.exe). Such execution chains are often used by malware or malicious scripts to bypass security controls or to execute code within the context of a browser process.
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.
Detects techniques commonly used to dump Active Directory domain credentials by accessing the NTDS.dit database file. This includes the use of ntdsutil for IFM (Install From Media) operations, manual shadow copy creation using vssadmin or wmic to copy the ntds.dit file, direct access to shadow copy volumes containing sensitive database files, and the use of credential dumping tools like Impacket's secretsdump.
Detects suspicious behavior by the IIS worker process (w3wp.exe) indicative of web shell deployment, specifically the spawning of command interpreters (cmd.exe, powershell.exe) or the writing of .aspx files. This activity is consistent with exploitation campaigns targeting MOVEit Transfer, such as those conducted by the Lace Tempest (Cl0p) group.
Detects usage of ntdsutil.exe with arguments designed to dump the Active Directory database (NTDS.dit) and the use of netsh.exe for establishing covert port forwarding. These are known 'Living-off-the-Land' (LotL) techniques observed in campaigns attributed to the threat actor Volt Typhoon.
The following analytic identifies Living Off the Land Binaries and Scripts (LOLBAS) that can legitimately initiate public network connections but are communicating over uncommon destination ports.
It leverages the Network Traffic data model and applies per-binary common-port exclusions to reduce false positives while preserving suspicious non-standard communication.
This behavior may indicate payload download, command-and-control, proxy execution, or attempts to blend malicious traffic into trusted Windows binaries.
Join this detection with the Process Execution events to provide context and avoid false positives.
It leverages the Network Traffic data model and applies per-binary common-port exclusions to reduce false positives while preserving suspicious non-standard communication.
This behavior may indicate payload download, command-and-control, proxy execution, or attempts to blend malicious traffic into trusted Windows binaries.
Join this detection with the Process Execution events to provide context and avoid false positives.
Detects instances where AI agent runtime environments, such as Python or Node.js processes, spawn command interpreters to execute commands that suggest reconnaissance or unauthorized system manipulation. This behavior is indicative of a hijacked AI agent abusing its tool-use capabilities to execute commands outside of an approved workflow.
The following analytic identifies the creation of Dynamic Link Libraries (DLLs) with a known history of exploitation in atypical locations. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and filesystem events. This activity is significant as it may indicate DLL search order hijacking or sideloading, techniques used by attackers to execute arbitrary code, maintain persistence, or escalate privileges. If confirmed malicious, this activity could allow attackers to blend in with legitimate operations, posing a severe threat to system integrity and security.
The following analytic identifies a LOLBAS process being executed outside of it's expected location.
Processes being executed outside of expected locations may be an indicator that an adversary is attempting to evade defenses or execute malicious code.
The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code.
Processes being executed outside of expected locations may be an indicator that an adversary is attempting to evade defenses or execute malicious code.
The LOLBAS project documents Windows native binaries that can be abused by threat actors to perform tasks like executing malicious code.
The following analytic detects the creation or connection to known suspicious named pipes, which is a technique often used by offensive tools.
It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by RMM tools.
If confirmed malicious, this could allow an attacker to abuse these to potentially gain persistence, command and control, or further system compromise.
It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by RMM tools.
If confirmed malicious, this could allow an attacker to abuse these to potentially gain persistence, command and control, or further system compromise.
The following analytic detects the creation or connection to known suspicious named pipes.
It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by malicious or suspicious tools.
If confirmed malicious, this could allow an attacker to abuse these to potentially gain privilege escalation,
persistence, c2 communications, or further system compromise.
It leverages Sysmon EventCodes 17 and 18 to identify known default pipe names used by malicious or suspicious tools.
If confirmed malicious, this could allow an attacker to abuse these to potentially gain privilege escalation,
persistence, c2 communications, or further system compromise.
The following analytic identifies DNS queries to domains associated with the 3CX supply chain attack. It leverages the Network_Resolution datamodel to detect these suspicious domain indicators. This activity is significant because it can indicate a potential compromise stemming from the 3CX supply chain attack, which is known for distributing malicious software through trusted updates. If confirmed malicious, this activity could allow attackers to establish a foothold in the network, exfiltrate sensitive data, or further propagate malware, leading to extensive damage and data breaches.
Detects DNS lookups and outbound network connections to known command and control (C2) and staging domains associated with the STAC4924 campaign. The rule performs strict matching on domain names to ensure that subdomains are identified while avoiding false positives from partial string matches within URLs.
Detects outbound network connections to common TOR entry, guard, or client service ports, or the execution of TOR-related processes. This behavior may indicate an adversary establishing C2 communication channels or attempting to perform anonymized data exfiltration.
Detects the execution of control.exe with suspicious command-line arguments involving WebDAV paths (DavWWWRoot) and file extensions like .cpl or .dll, initiated by Windows task-related processes (svchost, taskeng, schtasks). This pattern is indicative of potential malicious payload loading or proxy execution where a scheduled task is used to trigger a secondary malicious component.
This rule detects Node.js, NPM, or Yarn processes initiating network connections to common Ethereum JSON-RPC providers such as Infura and Alchemy. This activity, when originating from processes not associated with known development directories (like hardhat, truffle, or web3 projects), may indicate malicious use of build tools for blockchain-based command and control (C2) or unauthorized data exfiltration via RPC endpoints.
Detects the execution of renamed schtasks.exe binary, which is a legitimate Windows utility used for scheduling tasks.
One of the very common persistence techniques is schedule malicious tasks using schtasks.exe.
Since, it is heavily abused, it is also heavily monitored by security products. To evade detection, threat actors may rename the schtasks.exe binary to schedule their malicious tasks.
One of the very common persistence techniques is schedule malicious tasks using schtasks.exe.
Since, it is heavily abused, it is also heavily monitored by security products. To evade detection, threat actors may rename the schtasks.exe binary to schedule their malicious tasks.
Detects Impacket-style lateral movement patterns where system processes such as services.exe or wmiprvse.exe spawn command shells to copy files from a network share (UNC path) and execute them. This rule specifically looks for evidence of staging binaries or DLLs from remote shares, which is a common behavior of post-compromise frameworks like Impacket when performing 'hands-on-keyboard' actions.
This rule detects typical behavior associated with infostealers targeting browser data, specifically the creation of compressed archives (.zip, .rar) in temporary directories (Temp or AppData) and the subsequent exfiltration of data via known webhook services (e.g., Discord, Telegram, Pastebin) or direct IP connections. It filters out common signed backup and synchronization software to minimize noise.
Detects unauthorized processes attempting to read sensitive session files or databases associated with messaging applications like Telegram, Signal, and Discord. Accessing these files (such as 'tdata', 'db.sqlite', or 'leveldb') allows for full account takeover by extracting session tokens, enabling attackers to bypass authentication and 2FA.
Detects unauthorized access to DPAPI master key files located in the Windows user profile (%APPDATA%\Microsoft\Protect\). This activity is commonly associated with infostealers attempting to decrypt browser-stored credentials (cookies, passwords) by manually accessing the master key files after exfiltrating encrypted data stores.
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