Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
56 detections
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Detects suspicious access to network device configuration files or routing table exports, potentially indicating compromise by the Salt Typhoon threat actor. This rule correlates file access events on critical configuration files (e.g., running-config, startup-config, BGP routes) within sensitive directories with network or remote interactive logons by local administrators.
This rule detects external brute-force attempts against firewall authentication mechanisms. It triggers when 15 or more failed login attempts from a single external IP address occur within a 15-minute window. The rule specifically filters for 'USER_LOGIN' events with a 'BLOCK' or 'FAIL' security result and excludes internal RFC1918 IP addresses to focus on external threats.
Detects BGP failures which may be indicative of brute force attacks to manipulate routing.
Detects network connections to known C2 servers and payload delivery URLs associated with a PureRAT campaign. This activity is linked to a Vietnamese threat actor using AI to generate their toolset.
Detects HTTP/HTTPS POST requests to known Lumma Stealer C2 domain
Detects communication to known Command and Control (C2) servers associated with the Equation Group, as identified in the operational notes from the ShadowBrokers leak. This rule specifically looks for network connections to or from the hardcoded IP addresses linked to Equation Group C2 infrastructure.
The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
This analytic detects suspicious data transfer activities involving the use of the `split` syscall with the `-b` option, which specifies the size of each output file. This technique can be used by attackers to evade detection by breaking large files into smaller parts, bypassing size-based security controls, and facilitating covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall with a specified block size, this analytic helps identify potential data exfiltration attempts.
This analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame (10 seconds). A detection is triggered if 15 or more unique readme paths are observed. This activity is a strong indicator of an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
The following analytic identifies the execution of RAR utilities to archive files on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and command-line arguments. This activity is significant as threat actors, including red-teamers and malware like DarkGate, use RAR archiving to compress and exfiltrate collected data from compromised hosts. If confirmed malicious, this behavior could lead to the unauthorized transfer of sensitive information to command and control servers, posing a severe risk to data confidentiality and integrity.
This analytic detects intrusion events from known threat activity using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where one or multiple Snort signatures associated with a known threat or threat actor activity have been triggered within a one-hour time window. The detection uses a lookup table (cisco_snort_ids_to_threat_mapping) to map Snort signature IDs to known threat actors and their techniques. When multiple signatures associated with the same threat actor are triggered within the time window, and the count of unique signatures matches or exceeds the expected number of signatures for that threat technique, an alert is generated. This helps identify potential coordinated threat activity in your network environment by correlating related intrusion events that occur in close temporal proximity. Currently, this detection will alert on the following threat actors or malware families as defined in the cisco_snort_ids_to_threat_mapping lookup: ArcaneDoor, Static Tundra, AgentTesla, Amadey, AsyncRAT, Chafer, DCRAT, Lumma Stealer, Nobelium, Quasar, Remcos, Snake, Xworm. To add or update threat actors, update the cisco_snort_ids_to_threat_mapping.csv lookup file with new or modified threat names and associated Snort signature IDs.
This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations.
This analytic detects the creation or modification of network interfaces on Cisco devices, which could indicate an attacker establishing persistence or preparing for lateral movement. After gaining initial access to network devices, threat actors like Static Tundra often create new interfaces (particularly loopback interfaces) to establish covert communication channels or maintain persistence. This detection specifically looks for the configuration of new interfaces, interface state changes, and the assignment of IP addresses to interfaces. These activities are particularly concerning when they involve unusual interface names or descriptions containing suspicious terms.
This analytic detects network traffic to TCP port 4786, which is used by the Cisco Smart Install protocol. Smart Install is a plug-and-play configuration and image-management feature that helps customers to deploy Cisco switches. This protocol has been exploited via CVE-2018-0171, a vulnerability that allows unauthenticated remote attackers to execute arbitrary code or cause denial of service conditions. Recently, Cisco Talos reported that a Russian state-sponsored threat actor called "Static Tundra" has been actively exploiting this vulnerability to compromise unpatched and end-of-life network devices. Monitoring for traffic to this port can help identify potential exploitation attempts or unauthorized Smart Install activity.
This rule detects successful network connections from external (internet) IP addresses to common Industrial Control System (ICS) ports, such as Modbus (502) and EtherNet/IP (44818). This activity could indicate unauthorized access attempts, reconnaissance, or exploitation of internet-exposed ICS assets. The rule specifically filters for allowed/accepted/permitted connections and excludes connections from RFC 1918 private IP addresses and a configurable list of authorized external IPs.
The analytic detects when a Windows RDP client attempts to execute an RDP file from a temporary directory, downloads directory, or Outlook directories. This detection is significant as it can indicate an attempt for an adversary to deliver a .rdp file, which may be leveraged by attackers to control or exfiltrate data. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further lateral movement within the network.
The analytic detects network traffic associated with known remote access software applications, such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. It leverages Palo Alto traffic logs mapped to the Network_Traffic data model in Splunk. This activity is significant because adversaries often use remote access tools to maintain unauthorized access to compromised environments. If confirmed malicious, this activity could allow attackers to control systems remotely, exfiltrate data, or deploy additional malware, posing a severe threat to the organization's security.
The analytic identifies the execution of the native mimikatz.exe binary on Windows systems, including instances where the binary is renamed. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because Mimikatz is a widely used tool for extracting authentication credentials, posing a severe security risk. If confirmed malicious, this activity could allow attackers to obtain sensitive credentials, escalate privileges, and move laterally within the network, leading to potential data breaches and system compromise.
Detects network traffic to or from IP addresses known to be associated with the active exploitation of the SharePoint vulnerability CVE-2025-53770.
'Identifies contacts with domains names in CommonSecurityLog that might have been generated by a Domain Generation Algorithm (DGA). DGAs can be used by malware to generate rendezvous points that are difficult to predict in advance.
This detection uses the Alexa Top 1 million domain names to build a model of what normal domains look like. It uses this to identify domains that may have been randomly generated by an algorithm.
The triThreshold is set to 500 - increase this to report on domains that are less likely to have been randomly generated, decrease it for more likely.
The start time and end time look back over 6 hours of data and the dgaLengthThreshold is set to 8 - meaning domains whose length is 8 or more are reported.
NOTE - The top1M csv zip file used in the query is dynamic and may produce different results over various time periods. It's important to cross-check the events against the entities involved in the incident.'
This detection uses the Alexa Top 1 million domain names to build a model of what normal domains look like. It uses this to identify domains that may have been randomly generated by an algorithm.
The triThreshold is set to 500 - increase this to report on domains that are less likely to have been randomly generated, decrease it for more likely.
The start time and end time look back over 6 hours of data and the dgaLengthThreshold is set to 8 - meaning domains whose length is 8 or more are reported.
NOTE - The top1M csv zip file used in the query is dynamic and may produce different results over various time periods. It's important to cross-check the events against the entities involved in the incident.'
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