Detections

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

1,247 detections

Detects recurring Microsoft Graph mailbox activity featuring 'command_req_' or 'command_res_' substrings within message subjects or filter queries. This pattern is indicative of attackers using email-based infrastructure for Command and Control (C2) communication, characterized by periodic polling of mailbox items.
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Ankit Mehta@Secvyn
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Detections.ai Community
28 days ago
203
Identifies suspicious processes performing beacon-like network communications with rare external destinations. The rule detects consistent hourly activity intervals and high connection counts to endpoints rarely contacted by other devices in the organization, serving as a heuristic for automated C2 beaconing.
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Ibrahim Saud@tektrix
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Detections.ai Community
29 days ago
103
This rule detects unauthorized or anomalous outbound network connections from known AI agent runtime processes to LLM or API endpoints. It helps identify potential data exfiltration by monitoring for connections to destinations outside of an organization's approved allow-list for model providers, effectively catching scenarios where a compromised AI agent is prompted to relay sensitive data to an attacker-controlled endpoint.
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Ibrahim Saud@tektrix
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Detections.ai Community
29 days ago
103
Detects suspicious processes frequently polling Hugging Face repository endpoints (discussions, raw files, commits). This behavior is characteristic of adversaries using public code/dataset hosting services as a covert command-and-control (C2) channel to fetch instructions or exfiltrate data, bypassing traditional network filters by blending in with legitimate developer traffic.
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Ankit Mehta@Secvyn
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SlimKQL
1 month ago
205
This rule performs a retrospective sweep for indicators of compromise (IOCs) associated with the DragonForce TURN/MQTT campaign, as detailed in Lab52 threat research. It monitors for file and process activity matching known malicious hashes, as well as network connections to specific domains and URLs associated with the campaign's command-and-control infrastructure.
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Arnold Chan@slaz
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Midnight Slayer
26 days ago
001
Detects specific markers in UDP traffic on ports 3478, 5349, and 443 that are associated with the DragonForce malware's TRN1 C2 beaconing protocol. The rules identify session initiation, task polling, task acknowledgment, and result uploading behaviors.
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Arnold Chan@slaz
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Midnight Slayer
26 days ago
001
Detects specific markers in UDP traffic on ports 3478, 5349, and 443 that are associated with the DragonForce malware's TRN1 C2 beaconing protocol. The rules identify session initiation, task polling, task acknowledgment, and result uploading behaviors.
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Arnold Chan@slaz
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Hunters
26 days ago
001
Detects network activity associated with the DragonForce backdoor, specifically the use of MQTT for command and control. The rules monitor for unique 16-character client IDs used during registration on non-standard ports, subsequent command retrieval structures containing 'cmdnum', and DNS queries for the hardcoded broker 'broker.emqx.io'.
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Arnold Chan@slaz
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Midnight Slayer
26 days ago
001
Detects network activity associated with the DragonForce backdoor, specifically the use of MQTT for command and control. The rules monitor for unique 16-character client IDs used during registration on non-standard ports, subsequent command retrieval structures containing 'cmdnum', and DNS queries for the hardcoded broker 'broker.emqx.io'.
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Arnold Chan@slaz
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Detections.ai Community
26 days ago
001
Detects the initialization and persistence of a 'tailcat.exe' process operating from a non-standard, user-profile directory, typically mimicking legitimate tools. The rule monitors for the specific 'forward' command-line argument containing port mapping strings, an immediate loopback connection to that port, and subsequent periodic external network keep-alive traffic originating from the same process or parent lineage, indicating a C2 tunnel setup.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
006
Hunts telemetry for published NeedyMantis indicators: three known SHA-256 hashes (WinSparkle.dll first-stage loader and its encrypted archive, plus the older libcurl archive), the C2 domain corp.tripswithengine[.]com, and the C2 URL path /library/zip/ on that domain. Domain/URL matching parses the actual host out of RemoteUrl and requires an EXACT match (not substring 'has/contains'), so a different domain that merely contains 'corp.tripswithengine.com' as a suffix of a longer label (e.g. 'notcorp.tripswithengine.com') or as a prefix of an unrelated domain (e.g. 'corp.tripswithengine.com.evil.net') cannot match. No malicious IP address has been published for this campaign, so IP-based matching is intentionally not included.
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Arnold Chan@slaz
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SlimKQL
30 days ago
104
This rule detects potential command and control (C2) beaconing activity by monitoring for high-frequency POST requests to OpenAI and Google Generative Language chat completion API endpoints. The rule triggers when a source host makes 5 or more requests within a 60-second window, which may indicate automated polling for commands or exfiltrated data transfer using legitimate LLM infrastructure.
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Ankit Mehta@Secvyn
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Detections.ai Community
28 days ago
002
This rule detects potentially malicious behavior in package manager post-install scripts (npm, pip). It monitors for child processes like shells, network utilities (curl, wget), or command-line arguments indicative of credential harvesting (e.g., accessing .ssh, .aws/credentials, environment variables) initiated by package management processes.
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Ibrahim Saud@tektrix
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Detections.ai Community
28 days ago
202
This rule identifies potential command and control (C2) beaconing activity by detecting periodic, low-jitter outbound network connections. It analyzes network logs to find connections to external IP addresses that maintain a consistent frequency over a period of time, specifically flagging hosts that demonstrate repetitive traffic patterns with low variation (jitter). The rule further filters by identifying 'rare' destinations, focusing on remote IPs that have been connected to by three or fewer hosts within the environment, which is a common indicator of targeted C2 infrastructure.
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Ibrahim Saud@tektrix
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Detections.ai Community
28 days ago
102
Detects DNS query activity from processes commonly used as AI agent runtimes (e.g., python.exe, node.exe) toward domains characterized by DGA-like patterns, such as long alphanumeric strings or high-consonant density, which may indicate command-and-control communication.
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Ibrahim Saud@tektrix
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Detections.ai Community
29 days ago
102
Hunts telemetry for published NeedyMantis indicators: three known SHA-256 hashes (WinSparkle.dll first-stage loader and its encrypted archive, plus the older libcurl archive), the C2 domain corp.tripswithengine[.]com, and the C2 URL path /library/zip/ on that domain. Domain/URL matching parses the actual host out of RemoteUrl and requires an EXACT match (not substring 'has/contains'), so a different domain that merely contains 'corp.tripswithengine.com' as a suffix of a longer label (e.g. 'notcorp.tripswithengine.com') or as a prefix of an unrelated domain (e.g. 'corp.tripswithengine.com.evil.net') cannot match. No malicious IP address has been published for this campaign, so IP-based matching is intentionally not included.
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
103
Detects activity associated with Everest ransomware by matching known file hashes (in DeviceFileEvents/DeviceProcessEvents) and known C2/leak-site domains (in DeviceNetworkEvents).
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Arnold Chan@slaz
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Detection & Hunting Community
29 days ago
002
Detects activity associated with Everest ransomware by matching known file hashes (in DeviceFileEvents/DeviceProcessEvents) and known C2/leak-site domains (in DeviceNetworkEvents).
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Arnold Chan@slaz
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Hunters
29 days ago
002
Detects activity associated with Everest ransomware by matching known file hashes (in DeviceFileEvents/DeviceProcessEvents) and known C2/leak-site domains (in DeviceNetworkEvents).
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Arnold Chan@slaz
Defender - KQL
29 days ago
002
Detects network activity associated with the AgtaBackup RAT, specifically identifying custom HTTP headers for agent authentication, beaconing patterns to check-in endpoints, and establishment of WebSocket-based command and control channels.
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Ankit Mehta@Secvyn
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Detections.ai Community
29 days ago
102
Detects various indicators of compromise (IOCs) including known malicious file hashes, suspicious file names commonly associated with staging or initialization in web directories, and network communication to known malicious domains or URLs. It also monitors process execution command lines for references to these IOCs, excluding common browser processes.
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Arnold Chan@slaz
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Midnight Slayer
28 days ago
001
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