Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects file artifacts referencing InvisibleFerret, a Python-based backdoor used by WaterPlum/North Korean IT worker actors for persistent access to victim networks
This rule monitors for indicators of compromise (IOCs) associated with a specific AutoIT-based malware delivery chain. It tracks the execution and presence of specific malicious file names and SHA256 hashes, as well as network connections to a identified command-and-control (C2) infrastructure IP address (158.51.122.136). The detection spans file creation, process execution, image loading, and network communication events to identify the multi-stage attack lifecycle.
Detects the obfuscated first-stage JavaScript payload sharedLoad.min.js dropped by the malicious npm indexed-btree package, identified by string-array encoding and self-checksumming array rotation obfuscation patterns
Detects the obfuscated first-stage JavaScript payload sharedLoad.min.js dropped by the malicious npm indexed-btree package, identified by string-array encoding and self-checksumming array rotation obfuscation patterns
This rule monitors for execution of files with specific SHA256 hashes known to be malicious, and network connections to a known malicious domain associated with Vultr storage. It aggregates file creation, process execution, and network connection events to detect potential threat activity.
This rule detects periodic network beaconing patterns consistent with BabylonRAT malware. It flags established TCP connections to external networks that occur at a frequency exceeding 5 connections within a 300-second window, which may indicate a C2 check-in mechanism. Due to the heuristic nature of this detection, it is recommended to tune the threshold and correlate with payload analysis or entropy inspection to reduce noise.
This rule detects periodic network beaconing patterns consistent with BabylonRAT malware. It flags established TCP connections to external networks that occur at a frequency exceeding 5 connections within a 300-second window, which may indicate a C2 check-in mechanism. Due to the heuristic nature of this detection, it is recommended to tune the threshold and correlate with payload analysis or entropy inspection to reduce noise.
This rule identifies the Stella_Gary modular C# backdoor framework, which has been attributed to the Kimsuky (APT-C-55) threat group. The rule functions by detecting specific naming conventions within the binary and the presence of .NET assembly loading capabilities combined with plugin management functionality, characteristic of this modular malware family.
This rule detects suspicious process command-line activity on devices identified as ATMs. It looks for known malware-related executables (CSCSERVICE.EXE) and common ATM software components (DISPENSR, XFS, MSXFS, AGILIS, APTRA, PROCASH, *.xfs) being executed or initiated by unusual processes like svchost.exe, rundll32.exe, or regsvr32.exe. This activity could indicate an attempt at ATM jackpotting or other unauthorized access.
Detects Python scripts that exhibit behavioral characteristics of agentic or LLM-generated malicious code. This is identified by the presence of self-narrating comments describing intent, next steps, or staging/exfiltration actions, combined with hardcoded indicators for the JADEPUFFER staging infrastructure.
Detects the ENCFORGE locker malware, associated with the JADEPUFFER campaign, which specifically targets AI/ML-related files such as model checkpoints, vector databases, and training data for the purpose of malicious encryption or destruction.
Detects the Kimsuky second-stage payload test.bef_fri which masquerades as an RTF file via a falsified RTF header before header repair and decompression
This rule monitors endpoint telemetry for known indicators of compromise associated with the Casbaneiro/Ousaban banking trojan. It identifies activity across network connections (IPs and domain patterns), DNS queries for specific C2 domains, and the presence or execution of known malicious file hashes (e.g., PDF lures, HTA, AutoIt scripts, and payload binaries).
This rule monitors for network connections to known command-and-control (C2) domains or DGA-patterned domains (ending in .icu or .cfd), as well as the execution or file creation of files matching known malicious SHA256 hashes.
This rule monitors for network connections to known command-and-control (C2) domains or DGA-patterned domains (ending in .icu or .cfd), as well as the execution or file creation of files matching known malicious SHA256 hashes.
This rule detects network traffic patterns associated with the Koktevrat Android RAT, specifically identifying periodic TLS beacons (heartbeats) destined for known C2 domains (streams-tv.lol and stream-plus.lat). The behavior suggests the use of Android's AlarmManager to schedule recurring callbacks to the C2 infrastructure.
This rule detects potential command and control (C2) activity associated with the Koktevrat malware, specifically looking for WebSocket handshake requests (HTTP GET with 'Upgrade' header) to domain names that follow a generated pattern (8-27 alphanumeric characters ending in .icu or .cfd).
Detects Koktevrat Stage 2 Android RAT component (com.kowo.ciligeci) that unpacks the RC4-encrypted uoN.css/uoN.apk payload using the RC4 key iOZ
Detects Koktevrat Android RAT APK artifacts via obfuscated component class names (JyfNnJyj, GtVyayDo) and the Stage 2 package identifier com.kowo.ciligeci
Detects Koktevrat Android RAT APK artifacts via obfuscated component class names (JyfNnJyj, GtVyayDo) and the Stage 2 package identifier com.kowo.ciligeci
Detects shell/install scripts that deploy an XMRig-based Monero cryptominer, consistent with the install script planted on the LeakySensey git repository server
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