Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
77 detections
Filters
Last updated
All Time
Detection languages
36
21
11
5
2
Contributors
18
15
7
7
7
Categories
30
12
11
11
10
Platforms
61
14
8
7
1
Products / Services
17
11
11
9
6
MITRE Techniques
18,110
15,627
12,575
8,387
6,212
CVEs
1
IDS Classtypes
5
IDS Protocols
5
Detects the deployment of the ARTEX AI penetration testing framework via Docker/Docker-compose combined with the execution of offensive security and scanning tools (such as nmap, sqlmap, nuclei, httpx, apktool, frida) on the same host within a specified timeframe.
The following analytic identifies the execution of commonly used NirSoft utilities on Windows systems.
It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution details such as process name, parent process, and command-line arguments.
This activity is significant for a SOC because NirSoft utilities, while legitimate, can be used by adversaries for malicious purposes like credential theft or system reconnaissance.
If confirmed malicious, this activity could lead to unauthorized access, data exfiltration, or further system compromise.
Note that this search does not use a where clause to filter out known benign paths, as NirSoft utilities can be executed from various locations. This might hinder performance in environments with high data volumes.
Apply additional filtering as necessary to enhance this.
It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution details such as process name, parent process, and command-line arguments.
This activity is significant for a SOC because NirSoft utilities, while legitimate, can be used by adversaries for malicious purposes like credential theft or system reconnaissance.
If confirmed malicious, this activity could lead to unauthorized access, data exfiltration, or further system compromise.
Note that this search does not use a where clause to filter out known benign paths, as NirSoft utilities can be executed from various locations. This might hinder performance in environments with high data volumes.
Apply additional filtering as necessary to enhance this.
The following analytic detects the creation of files associated with the NirSoft tool bundles on Windows endpoints.
NirSoft is a well-known provider of free, portable utilities that can be used for various system and network tasks. However, threat actors often leverage these tools for malicious purposes, such as credential harvesting, network reconnaissance, and data exfiltration.
The detection focuses on the creation of specific NirSoft tool bundle files, which may indicate that an attacker is preparing to use these utilities on a compromised system.
Security teams should investigate any instances of these files being created, especially if they are found in unexpected locations or on systems that should not be using such tools.
NirSoft is a well-known provider of free, portable utilities that can be used for various system and network tasks. However, threat actors often leverage these tools for malicious purposes, such as credential harvesting, network reconnaissance, and data exfiltration.
The detection focuses on the creation of specific NirSoft tool bundle files, which may indicate that an attacker is preparing to use these utilities on a compromised system.
Security teams should investigate any instances of these files being created, especially if they are found in unexpected locations or on systems that should not be using such tools.
The following analytic detects the execution of AdvancedRun.exe, a tool with capabilities similar to remote administration programs like PsExec. It identifies the process by its name or original file name and flags common command-line arguments. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and command-line telemetry. Monitoring this activity is crucial as AdvancedRun can be used for remote code execution and configuration-based automation. If malicious, this could allow attackers to execute arbitrary commands, escalate privileges, or maintain persistence within the environment.
Detects the presence of a shared client-side JavaScript framework used in 'ClickFix' MaaS (Malware-as-a-Service) panels, specifically associated with the Exvicy and ErrTraffic campaigns. The rule identifies specific function patterns (e.g., xdReq, xdDec, waitForToken) and state machine logic (tokens, deduplication hashing) commonly used for token interception and clipboard interaction.
Detects outbound network requests from endpoints to known 'uncensored' or jailbroken LLM-as-a-Service domains (e.g., WormGPT, FraudGPT). These platforms are specifically marketed to threat actors for illicit purposes like generating malware, phishing kits, or exploit code, and identifying access to them serves as a leading indicator of adversarial activity.
Detects outbound network requests from endpoints to known 'uncensored' or jailbroken LLM-as-a-Service domains (e.g., WormGPT, FraudGPT). These platforms are specifically marketed to threat actors for illicit purposes like generating malware, phishing kits, or exploit code, and identifying access to them serves as a leading indicator of adversarial activity.
Identifies network communication to known malicious infrastructure used by the SilkParasite threat actor. The rule specifically monitors DNS queries and destination connections to subdomains associated with 'hoster-kg.com' and 'tdtu.org', which are linked to the operation of SpiceRAT, NodeEdgeRAT, and NomadRAT malware.
Detects activity associated with the 'MovieReaper' threat campaign by identifying known malicious file hashes, command and control (C2) IP addresses, and communication with identified malicious domains. This rule monitors process execution, file operations, and network connections within the campaign's active timeframe.
Detects activity associated with the 'MovieReaper' threat campaign by identifying known malicious file hashes, command and control (C2) IP addresses, and communication with identified malicious domains. This rule monitors process execution, file operations, and network connections within the campaign's active timeframe.
Detects activity associated with the 'MovieReaper' threat campaign by identifying known malicious file hashes, command and control (C2) IP addresses, and communication with identified malicious domains. This rule monitors process execution, file operations, and network connections within the campaign's active timeframe.
Detects activity associated with the 'MovieReaper' threat campaign by identifying known malicious file hashes, command and control (C2) IP addresses, and communication with identified malicious domains. This rule monitors process execution, file operations, and network connections within the campaign's active timeframe.
Detects the Iron Man System phishing kit via its HTTP fingerprint: a request-header rule (ETag/Server/Via combo) checked on the external server's response, and three request-side rules checking for the kit's hashed asset paths and favicon when an internal host requests them from an external site. Fixed a direction bug on the three request-side rules that had source/destination reversed (they previously required an external host to request the path from an internal server, which would rarely if ever fire).
Detects the Iron Man System phishing kit via its HTTP fingerprint: a request-header rule (ETag/Server/Via combo) checked on the external server's response, and three request-side rules checking for the kit's hashed asset paths and favicon when an internal host requests them from an external site. Fixed a direction bug on the three request-side rules that had source/destination reversed (they previously required an external host to request the path from an internal server, which would rarely if ever fire).
Detects the Iron Man System phishing kit via its HTTP fingerprint: a request-header rule (ETag/Server/Via combo) checked on the external server's response, and three request-side rules checking for the kit's hashed asset paths and favicon when an internal host requests them from an external site. Fixed a direction bug on the three request-side rules that had source/destination reversed (they previously required an external host to request the path from an internal server, which would rarely if ever fire).
Detects HTTP POST requests to LLM chat completion API endpoints containing keywords associated with phishing kit generation, malware scaffolding, and automated credential harvesting. This behavior is consistent with attackers using LLM-assisted supply chain or phishing campaigns.
Detects HTTP POST requests sent to Large Language Model (LLM) chat completion API endpoints where the URI query parameters contain keywords strongly associated with the creation of phishing kits, credential harvesting forms, or malware scaffolding. This rule aims to identify the use of AI tools to accelerate malicious development workflows.
Detects non-sysinternals tools setting the "accepteula" key which normally is set on sysinternals tool execution
Detects the execution of QEMU binary processes with command-line arguments targeting database or virtual disk files (e.g., .qcow2, .db) or matching known malicious file hashes. This behavior is indicative of unauthorized use of virtualization tools to execute payloads, conduct forensics, or manipulate virtual disk images in an environment.
Detects the execution of known Remote Monitoring and Management (RMM) tools from common user-writable or temporary directories (Temp, Downloads, AppData, Public). The rule further filters for processes commonly abused for payload delivery and command execution (powershell.exe, cmd.exe, wscript.exe), or processes containing typical obfuscation and download indicators, excluding benign system process initiators.
Detects the creation of the "accepteula" key related to the Sysinternals tools being created from executables with the wrong name (e.g. a renamed Sysinternals tool)
Page 1 of 4