Detections

Explore public detection logic contributed by the community across SIEM and rule languages.

4,866 detections

Detects execution of the MovieReaper initial loader binary. This malware employs advanced anti-analysis techniques, specifically manual PEB-walking to resolve APIs and evade detection by standard library loading hooks. The rule alerts on known file hashes and specific masquerading filenames.
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Arnold Chan@slaz
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Midnight Slayer
1 month ago
002
Detects execution of the MovieReaper initial loader, which employs specific anti-sandboxing and anti-analysis techniques. The detection relies on identified file hashes, as well as heuristic analysis of process filenames containing common media-related keywords in sensitive directories and command line arguments indicating the presence of Global mutexes.
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Arnold Chan@slaz
Defender - KQL
1 month ago
002
Detects execution of the MovieReaper initial loader, which employs specific anti-sandboxing and anti-analysis techniques. The detection relies on identified file hashes, as well as heuristic analysis of process filenames containing common media-related keywords in sensitive directories and command line arguments indicating the presence of Global mutexes.
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Arnold Chan@slaz
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Hunters
1 month ago
002
This rule monitors network connections, file events, and process execution command lines for references to a list of known malicious URLs, including indicators associated with ClearFake, IClickFix, AMOS, Remus, and Mozi botnet payloads.
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Ankit Mehta@Secvyn
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Detections.ai Community
2 months ago
304
Detects DNS lookups and subsequent HTTP requests to known domains associated with the Exvicy pop-under/loader malware. These domains are identified as malicious lures often used to initiate payload downloads.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
000
Detects the execution or invocation of processes matching a known list of malicious file hashes (SHA256). The rule monitors both direct execution (SHA256) and instances where a malicious file acts as an initiating process.
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Ankit Mehta@Secvyn
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Hunters
2 months ago
103
Detects Vidar Stealer v2.x-3.x custom VM bytecode interpreter used to deobfuscate strings via a fetch-decode-execute loop with sparse opcode dispatch and single accumulator
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
000
This rule monitors file events to detect the presence of known malicious files based on their SHA256 or MD5 file hashes.
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Ankit Mehta@Secvyn
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Detections.ai Community
1 month ago
000
This rule monitors for interactions with known malicious files (by SHA256 hash), connections to confirmed C2 infrastructure (specific IP address and domains), or requests to known malicious URLs. It aggregates indicators across file, process, network, and web browsing telemetry to identify potential threats or compromised systems communicating with known attacker infrastructure.
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Arnold Chan@slaz
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Hunters
1 month ago
000
This rule monitors for interactions with known malicious files (by SHA256 hash), connections to confirmed C2 infrastructure (specific IP address and domains), or requests to known malicious URLs. It aggregates indicators across file, process, network, and web browsing telemetry to identify potential threats or compromised systems communicating with known attacker infrastructure.
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
000
This rule monitors for interactions with known malicious files (by SHA256 hash), connections to confirmed C2 infrastructure (specific IP address and domains), or requests to known malicious URLs. It aggregates indicators across file, process, network, and web browsing telemetry to identify potential threats or compromised systems communicating with known attacker infrastructure.
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Arnold Chan@slaz
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Midnight Slayer
1 month ago
000
Detects the presence of known file hashes associated with Vectra Remote Access Trojan (RAT). The rule monitors both file creation/existence events and process execution events where the SHA256 hash matches the identified indicators of compromise.
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Arnold Chan@slaz
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Detection & Hunting Community
2 months ago
305
This rule monitors for known malicious file hashes, IP addresses, C2 domains, and specific URL markers associated with identified threat activity. It consolidates multiple detection vectors including file activity, process execution, network connections, and DNS queries to identify compromised devices communicating with attacker-controlled infrastructure.
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Arnold Chan@slaz
Defender - KQL
1 month ago
001
Detects the CastleRAT intermediate loader, which acts as a secondary stage before deploying SloppyRAT. The rule identifies the malicious payload by scanning for unique embedded strings, specific user-agent strings, and command-and-control (C2) communication patterns (domains and URI paths) within executable files.
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Ibrahim Saud@tektrix
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Detections.ai Community
1 month ago
001
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.
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Ibrahim Saud@tektrix
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Detections.ai Community
2 months ago
002
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.
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Ibrahim Saud@tektrix
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Detections.ai Community
2 months ago
002
This rule performs a hunting activity across multiple telemetry sources (Network, Email, Endpoint) to detect known indicators of compromise (IOCs) associated with Anthropic threat intelligence reporting (September 2026). It looks for specific malware file names, command-line patterns, service installations, phishing email addresses, and C2 communication URLs.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
105
This rule identifies potential malicious activity by detecting the presence of known malicious file hashes (SHA256 and MD5) on endpoints and monitoring for DNS queries or network connections to a known malicious staging domain (yapw.life). The detection correlates file execution, file activity, and network communication to identify stages of an attack such as malware installation or C2 communication.
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Arnold Chan@slaz
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Hunters
1 month ago
101
This rule identifies potential malicious activity by detecting the presence of known malicious file hashes (SHA256 and MD5) on endpoints and monitoring for DNS queries or network connections to a known malicious staging domain (yapw.life). The detection correlates file execution, file activity, and network communication to identify stages of an attack such as malware installation or C2 communication.
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
101
This rule identifies potential malicious activity by detecting the presence of known malicious file hashes (SHA256 and MD5) on endpoints and monitoring for DNS queries or network connections to a known malicious staging domain (yapw.life). The detection correlates file execution, file activity, and network communication to identify stages of an attack such as malware installation or C2 communication.
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Arnold Chan@slaz
Defender - KQL
1 month ago
001
This rule identifies potential malicious activity by detecting the presence of known malicious file hashes (SHA256 and MD5) on endpoints and monitoring for DNS queries or network connections to a known malicious staging domain (yapw.life). The detection correlates file execution, file activity, and network communication to identify stages of an attack such as malware installation or C2 communication.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
001
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