Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a suspicious sequence of network downloads initiated by bash, sh, or zsh using curl or wget where the command-line includes the 'force=1' parameter. This behavior is indicative of the ClickFix macOS dropper chain often associated with the Macfinger campaign, where multiple payloads (shell scripts and Mach-O binaries) are retrieved from a common C2 infrastructure.
Detects instances where PowerShell is initiated via LNK files, specifically targeting PDF LNK attachments or known malicious script naming patterns (update1.vbs, update2.ps1) and common downloader cmdlets (e.g., IEX, Invoke-WebRequest). It also includes specific indicators for remote file downloads.
Detects network communication to 'imageurlgenerator.com' where a VBScript downloader is attempting to retrieve a staged payload. The request pattern targets specific URI structures resembling GUID-based identifiers ending in '.txt', which is characteristic of the XHOPLESS malware delivery chain.
Detects a multi-stage malware execution pattern consistent with VelvetCake activity. This includes staging text-based files in C:\Users\Public, downloading PowerShell scripts to temporary directories, executing the script via PowerShell, and subsequently deleting both the staged text files (except for specific allowed filenames) and the payload script.
Detects PowerShell ScriptBlock events (4104) that exhibit characteristics of AI/LLM-generated code, specifically the presence of highly descriptive inline commentary combined with encoded commands or remote network download cradles. This pattern is often used by automated tools to generate syntactically randomized obfuscated scripts to evade detection.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
Detects a multi-stage attack chain involving the download, extraction, permission modification, and execution of the XMRig cryptocurrency miner on Linux systems. The rule specifically monitors for common staging patterns in /tmp and the use of known mining-related CLI arguments.
Detects a suspicious execution chain where a command shell (cmd.exe) launched by explorer.exe uses curl.exe to download an MSI file from Azure Blob Storage, followed immediately by the installation of that MSI using msiexec.exe. This pattern is indicative of common malware delivery techniques, such as those observed with the RVTools.lnk delivery chain.
Detects endpoint network activity characterized by a host connection to the Telegram Bot API followed by a connection to a Vercel-hosted domain within 30 minutes. This behavior is indicative of a multi-stage infection chain where adversaries use Telegram for command-and-control communication or victim validation, followed by the retrieval of malicious payloads or lures from disposable infrastructure like Vercel.
Detects network beaconing and command-and-control (C2) communication patterns associated with the RatHat Android malware family. The rule identifies registration, shell command check-ins, heartbeat signals, and task fetching activity over HTTP. It uses stateful inspection (flowbits) to correlate device registration with subsequent heartbeat behavior to improve detection fidelity.
Detects the presence of a specific malicious _socket.pyd Python module associated with LegionLoader dropped into non-standard directories like 'Traiolx Custom Utils'. The detection leverages file path patterns and known file hashes of the malicious component.
Detects the use of PowerShell to download the gitlab-runner-windows-amd64.exe binary from the official AWS S3 distribution bucket. This behavior is indicative of an attacker attempting to stage the GitLab Runner service to establish command and control or persistence within a compromised Windows environment.
Detects network activity associated with the NarwhalRAT malware, specifically C2 beaconing via HTTP/DNS to known infrastructure and file downloads from staging domains.
FileFix loader chains abuse trusted code-hosting platforms like Bitbucket/GitHub to host staged payload components, exploiting domain trust to evade network detection.
Acronis TRU (Sept 2025) documented a FileFix campaign where a PowerShell loader downloads images from Bitbucket that conceal embedded malicious components, ultimately deploying a Go-based loader and StealC.
After the initial FileFix paste-and-execute step, observed campaigns chain into a secondary download of the real payload using LOLBins or PowerShell web clients.
Detects HTTP and TLS activity associated with the DSCourier malware, specifically identifying attempts to retrieve configuration files disguised as common non-executable extensions (.jpg, .txt) or utilizing non-standard network ports like 8443, which are characteristic of this threat's command and control behavior.
Detects the download and subsequent extraction of the Python embeddable zip package to directories under 'C:\Users\Public'. This activity is consistent with techniques used by threat actors, such as APT37, to establish a covert Python runtime environment on a compromised host for further malicious operations.
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 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.
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