Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the BotHelper RAT utilizing its msedge_proxy.exe process to execute 'ClipperStart' or 'ClipperStop' commands. These commands are indicative of the malware's clipboard-address substitution functionality, which is used to redirect cryptocurrency transactions by modifying clipboard content.
This rule detects network traffic patterns associated with the Lumma Stealer malware performing endpoint fingerprinting. It monitors for GET requests to the URI path '/api/set_agent' which contains a 32-character hexadecimal string, characteristic of the malware's C2 communication.
This rule detects network traffic indicative of the RisePro malware communicating with its Command and Control (C2) infrastructure. It specifically looks for the 'clientsendfile' string within the payload, which acts as a keepalive signal or heartbeat for the RisePro implant.
This rule detects potential command-and-control (C2) communication associated with the Pikabot malware. The detection looks for outbound HTTP POST requests to external networks that lack a 'Content-Type' header and contain a non-empty, specifically sized request body, which are behavioral indicators consistent with Pikabot beaconing activity.
Detects malicious DLLs identified by specific exports and imports (e.g., rdCore.dll, I++u.dll) that are intended to be sideloaded by signed binaries such as Canon's COTFileReadApp.exe or Stardock software. The detection relies on identifying the combination of sideloaded component names and the presence of suspicious import structures characteristic of these specific staging loaders.
Detects network activity associated with the RatHat Android malware, specifically the exfiltration of accessibility tree data and calls to Google's Generative Language API (gemini-2.5-flash-lite) for UI target resolution and automation.
Detects communication patterns associated with the RatHat Android banking trojan. The rule identifies a multi-stage C2 flow: first, the device fetches malicious overlay configurations (templates for spoofed login screens) and subsequently exfiltrates captured credentials or form data to the adversary's infrastructure via POST requests.
Detects RatHat Android dropper applications that utilize automated build rotation to bypass file hash-based detection. The rule identifies stable artifacts, such as specific native library names, string obfuscation indicators, and manifest structure anomalies, that persist across these regenerated builds.
Detects the NeedyMantis second-stage loader, typically encountered as a malicious script (e.g., encryptbase64.ps1). The detection identifies raw shellcode residing in files that exhibit specific traits: PEB-walking for API resolution using a ROR(11) hashing algorithm, the inclusion of the 'RtlDecompressBuffer' API for payload expansion, and the absence of standard PowerShell script headers at the start of the file.
Detects NeedyMantis malware loader components that utilize the RtlDecompressBuffer API for custom XOR-compressed archive extraction. The rule specifically looks for the presence of known staged file names (dnsapi.dll, ws2_32.dll, msvcrt140.dll, or encryptbase64.ps1) alongside decoy file references typically used by the threat actor.
Detects network traffic from mobile devices to the Google Gemini API (generativelanguage.googleapis.com) specifically targeting the 'generateContent' endpoint. This behavior is indicative of mobile malware (e.g., RatHat/PromptSpy) that may be utilizing AI-assisted UI-automation, potentially to exfiltrate screen content or automate malicious interactions by processing visual layout data through a generative model.
Detects specific malicious archive payloads associated with NeedyMantis that contain a second-stage loader and spoofed system DLLs (e.g., dnsapi.dll, ws2_32.dll, msvcrt140.dll). These archives utilize XOR encoding or RtlDecompressBuffer packing and are designed for DLL sideloading.
Detects network traffic from mobile devices to the Google Gemini API (generativelanguage.googleapis.com) specifically targeting the 'generateContent' endpoint. This behavior is indicative of mobile malware (e.g., RatHat/PromptSpy) that may be utilizing AI-assisted UI-automation, potentially to exfiltrate screen content or automate malicious interactions by processing visual layout data through a generative model.
Detects the NeedyMantis first-stage loader (e.g., WinSparkle.dll variants) which employs anti-debug techniques such as NtQueryInformationProcess and ThreadHideFromDebugger, alongside stack-based string deobfuscation, to prepare for the extraction and execution of a secondary payload (e.g., encryptbase64.ps1).
Detects the creation of specific named mutexes used by the CHOSEN BRICK malware. The malware utilizes these mutexes as an execution guardrail to ensure only one instance of the infection persists on a host. Identification of these mutexes is a high-confidence indicator of CHOSEN BRICK infection activity, often accompanying persistence via Registry Run keys and subsequent communication with Telegram-based command and control servers.
Detects malicious software bundles consistent with the Contagious Interview (G1052) threat group's multi-stage infection chain. The rule identifies the presence of multiple malware family identifiers (such as BeaverTail, InvisibleFerret, and others) within the context of npm or VS Code task configuration files, which are commonly used in job-assessment themed social engineering campaigns.
Detects the presence of a specific Russian-language error string ("Не удалось сгенерировать HWID") within binary files, which is characteristic of the HWID generation mechanism used by Vidar Stealer malware.
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.
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.
C2 Ioc Detection
YARA-L
Detects Network, File, or Process events associated with known C2 IOCs
Detects the presence of the i-Realm companion tool associated with the AnonyMousKIT PhaaS platform, which is designed to bypass iOS Activation Lock features.
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