Detections

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

1,748 detections

This rule detects various artifacts and behaviors associated with the DeedRAT malware, also known as MicRun or SBAMRES. It identifies the execution of 'MicRun.exe', suspicious file paths like 'C:\ProgramData\MicroDefaults', and registry persistence via 'Run' keys with specific data patterns. The rule also looks for the creation of malicious services using 'sc create MicRun', known malicious SHA256 hashes for both processes and files, and suspicious filenames such as 'SBAMRES.DLL' and 'MicRun.exe'. Additionally, it detects network communication to a known DeedRAT C2 domain, 'luckybear669.kozow.com'.
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Luis Zavier Guzman Garcia@KillBillKill98
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Detections.ai Community
1 year ago
40221
This rule detects suspicious activities related to Protected Process Light (PPL) and driver loading that could indicate privilege escalation, defense evasion, or impact. Specifically, it looks for:
1. Processes running with 'System' integrity that were initiated by a process with a lower integrity level, which could suggest an attempt to elevate privileges.
2. Driver image load events where 'ntoskrnl.exe' is the initiating process, but the driver is loaded from a location outside of the standard 'System32' or 'System32\drivers' directories. This could indicate an adversary attempting to load malicious drivers to evade detection or persist.
3. DriverLoad actions initiated by 'ntoskrnl.exe' from outside the standard 'System32' or 'System32\drivers' directories, similar to the image load events, pointing to potential malicious driver activity.
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Luis Zavier Guzman Garcia@KillBillKill98
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Detections.ai Community
1 year ago
4051
This rule detects various activities associated with the NotDoor malware, including the copying of malicious files to Outlook macro folders, C2 beaconing attempts using nslookup or curl to suspicious domains, registry modifications to disable Outlook macro security or enable macro loading on boot, and the creation of staging files in temporary directories for potential exfiltration. These behaviors are indicative of NotDoor installation, persistence, defense evasion, and data staging.
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Martin Baran@Martinbaran
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Detections.ai Community
1 year ago
24184
Detects a sequence of events indicative of Process Doppelgänging, a technique used by APT37. This rule identifies a process creating a legitimate Windows utility and then immediately injecting code into it, which is characteristic of creating a process in a suspended state for injection.
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Tim Peck@timpeck
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Detections.ai Community
1 year ago
1171
KQL Query
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Sergio Albea@Sergio_Albea
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01 | 🇨🇭 Swiss Cyber Hunters
1 year ago
6038
This analytic detects system binaries that are commonly abused in process injection techniques but are observed without any command-line arguments. It leverages Cisco Network Visibility Module (NVM) flow data and process arguments to identify outbound connections initiated by these processes. Binaries such as `rundll32.exe`, `regsvr32.exe`, `dllhost.exe`, `svchost.exe`, and others are legitimate Windows processes that are often injected into by malware or post-exploitation frameworks (e.g., Cobalt Strike) to hide execution. When these processes are seen initiating a network connection with an empty or missing command line, it can indicate potential injection and communication with a command and control server.
Splunk Security@SplunkSecurity
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Splunk Security Content
1 year ago
2048
This analytic detects network connections initiated by binaries that are not typically associated with network communication, such as 'notepad.exe', 'calc.exe' or 'write.exe'. It leverages Cisco Network Visibility Module logs to correlate network flow activity with process context, including command-line arguments, process path, and parent process information. These applications are normally used for locally and do not require outbound network access. When they do initiate such connections, it may indicate process hollowing, code injection, or proxy execution, where adversaries abuse a trusted process to mask malicious activity.
Splunk Security@SplunkSecurity
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Splunk Security Content
1 year ago
0047
Detects a high frequency of UAC consent prompts (consent.exe) originating from a single parent process, followed by the launch of a common Living Off The Land Binary (LOLBAS). This pattern is indicative of 'UAC Prompt Bombing' used to coerce a user into granting elevated privileges, as seen with the NightshadeC2 loader.
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Tim Peck@timpeck
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Detections.ai Community
1 year ago
1352
Detects the execution of a browser process with the remote debugging port argument enabled. This technique is used by infostealers like Stealerium and Phantom Stealer to bypass browser security and steal credentials and cookies.
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Tim Peck@timpeck
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Detections.ai Community
1 year ago
0023
Detects processes accessing sensitive browser files which may indicate credential theft from web browsers.
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Drew Pilarski@barnabysDrew
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Detections.ai Community
1 year ago
1229
This rule detects indicators of compromise (IOCs) associated with the 'ManualFinder/PDF Editor' malware campaign. It identifies malicious activity across multiple data sources, including file hashes, process hashes, suspicious certificate signers, suspicious company names in version info, suspicious file descriptions, malicious command execution, and network connections to known command and control (C2) or malicious PDF-related domains. The rule leverages external data sources for IOCs such as file hashes, C2 domains, PDF-related domains, suspicious signers, and malicious commands. It also includes static IOCs like specific file descriptions and company names. The detection logic is designed to identify various stages of the malware's activity, from initial execution to network communication.
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Damien vd Linden@lindensec
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Detections.ai Community
1 year ago
309278
This detection identifies the presence of PromptLock ransomware by looking for known malicious file hashes (SHA1 and SHA256). PromptLock is reported to be the first AI-powered ransomware, written in Go, and uses a local AI model to generate malicious Lua scripts. The rule searches across both file and process creation events for wider coverage.
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Reyben Cortes@Reybendetecc
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Detections.ai Community
1 year ago
4166
Detects potential token theft, impersonation, or privilege escalation activity in Windows environments by monitoring ProcessPrimaryTokenModified events. This query highlights suspicious changes in SIDs, token sources, integrity levels, or privilege flags, including attempts to impersonate SYSTEM or add high-risk privileges (SeDebugPrivilege, SeImpersonatePrivilege). Such behavior is strongly associated with lateral movement and privilege escalation techniques used by APTs, ransomware operators, and red team tooling like Mimikatz and Cobalt Strike.
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Awais Munir@iawais
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Detections.ai Community
1 year ago
233252
This rule detects suspicious activities related to Remote Monitoring and Management (RMM) tools by querying various data sources. It identifies processes, file signatures, network connections, and firewall logs associated with known RMM tools and domains, helping to uncover potential security threats. The rule leverages an external IOC list for RMM tool names, file signatures, and associated domains.
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Subash Ghimire@iamsubashg
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Detections.ai Community
1 year ago
40165
This rule detects communication with known malicious domains and IP addresses, as well as the presence or execution of files with known malicious MD5 or SHA256 hashes. It correlates threat intelligence with network and endpoint telemetry to identify compromised devices or accounts.
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Subash Ghimire@iamsubashg
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Detections.ai Community
1 year ago
50102
Hunts for abuse of Microsoft 365’s Direct Send feature where attackers send spoofed emails appearing internal, bypassing filters. Includes detection of suspicious DNS queries, outbound network connections, and PowerShell abuse for insider misuse.
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Awais Munir@iawais
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Detections.ai Community
1 year ago
226235
This rule detects process events where `svchost.exe` or `schtasks.exe` are the initiating processes on specific devices. This is designed to identify unexpected or suspicious scheduled tasks, whether system-initiated or user-triggered, on monitored devices. It can be used for hunting or to investigate alerts from other security tools regarding suspicious scheduled tasks.
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Subash Ghimire@iamsubashg
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Detections.ai Community
1 year ago
0041
Detects suspicious activities related to CVE-2025-8088 WinRAR exploitation, including malicious file creation, COM hijacking, C2 communication, and anti-analysis techniques. Enhanced with ADS detection, command-line analysis, and refined false positive handling. This threat is now attributed to RomCom.
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Reyben Cortes@Reybendetecc
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Detections.ai Community
1 year ago
7398
Detects various Indicators of Compromise (IOCs) associated with UAT-7237 activity, including file hashes, IP addresses, domains, and URLs. This rule identifies suspicious process executions based on known malicious file hashes and network connections to malicious IP addresses or URLs.
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R W@shiftyroot
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Detections.ai Community
1 year ago
3144
This rule detects activities associated with the Charon ransomware, including DLL side-loading, process creation patterns, file modifications related to ransom notes and payload artifacts, shadow copy deletion attempts, and service creation for persistence. It also includes SHA1 hashes for known Charon ransomware executables.
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Aamir Muhammad@Aamir
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Detections.ai Community
1 year ago
3262
This rule detects the presence and activity of PAM-based backdoors on Linux systems by looking for specific SHA256 hashes and filenames associated with known PAM backdoors. It monitors process events, general device events, file events, and image load events on Linux devices.
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Aamir Muhammad@Aamir
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Detections.ai Community
1 year ago
0254
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