Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
73 detections
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Detects possible unauthenticated exploitation attempts against CVE-2025-53521 (F5 BIG-IP APM RCE). Tightened from a generic 'any POST without a cookie on 443/8443' match (extremely broad, alerts on ordinary cookie-less API/health-check traffic) to POST requests specifically targeting F5 APM access-policy processing endpoints (/my.policy, /vdesk/) that lack a session cookie, since the CVE requires an access policy configured on the virtual server and legitimate APM sessions carry a session cookie. Added detection_filter (3 hits/60s per source) to suppress single incidental matches and prioritize repeated attempts. Note: public reporting for this CVE does not include the exact exploit URI/payload, so this is a behavioral/namespace heuristic, not a payload-specific signature — validate against your own APM traffic baseline and update once exploit specifics are published.
Detects possible unauthenticated exploitation attempts against CVE-2025-53521 (F5 BIG-IP APM RCE). Tightened from a generic 'any POST without a cookie on 443/8443' match (extremely broad, alerts on ordinary cookie-less API/health-check traffic) to POST requests specifically targeting F5 APM access-policy processing endpoints (/my.policy, /vdesk/) that lack a session cookie, since the CVE requires an access policy configured on the virtual server and legitimate APM sessions carry a session cookie. Added detection_filter (3 hits/60s per source) to suppress single incidental matches and prioritize repeated attempts. Note: public reporting for this CVE does not include the exact exploit URI/payload, so this is a behavioral/namespace heuristic, not a payload-specific signature — validate against your own APM traffic baseline and update once exploit specifics are published.
Detects large HTTP responses for URLs containing keywords associated with F5 BIG-IP configuration or archive files. This could indicate an attacker exfiltrating sensitive system configurations.
Detects HTTP POST requests to F5 BIG-IP management or shared endpoints that contain long, base64-encoded payloads. Threat actors may use this technique to upload webshells or stage data for exfiltration.
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.
Identify Suspicious Admin Activity in F5 BIG IP
Detects suspicious administrative activities on F5 BIG-IP devices, such as failed logins or configuration changes. This is based on recommendations from F5 in response to a security incident disclosed in October 2025 where a threat actor gained access to BIG-IP development environments. F5 advised customers to enhance monitoring for admin logins, failed authentications, and privilege and configuration changes.
Triggers when more than 5 failed login detected in one hour time span. set rule to run every 60mins
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 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.
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 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.
'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.'
'Identifies anamalous spikes in network traffic logs as compared to baseline or normal historical patterns.
The query leverages a KQL built-in anomaly detection algorithm to find large deviations from baseline patterns.
Sudden increases in network traffic volume may be an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than percentotalthreshold of the total traffic have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
The query leverages a KQL built-in anomaly detection algorithm to find large deviations from baseline patterns.
Sudden increases in network traffic volume may be an indication of data exfiltration attempts and should be investigated.
The higher the score, the further it is from the baseline value.
The output is aggregated to provide summary view of unique source IP to destination IP address and port traffic observed in the flagged anomaly hour.
The source IP addresses which were sending less than percentotalthreshold of the total traffic have been exluded whose value can be adjusted as needed .
You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
This analytic detects suspicious modifications to Unix shell configuration files, such as `.bashrc` or `.profile`, which may indicate an attempt to alter system behavior or gain unauthorized access. Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks.
The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
The following analytic detects the suspicious auditd service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
The analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment.
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