Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This analytic detects exploitation activity of CVE-2025-5777 using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signature 65118 (Citrix NetScaler memory overread attempt) is triggered. If confirmed malicious, this behavior is highly indicative of a potential exploitation of CVE-2025-5777.
'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.'
The analytic detects outbound SMB (Server Message Block) connections from internal hosts to external servers. It identifies this activity by monitoring network traffic for SMB requests directed towards the Internet, which are unusual for standard operations. This detection is significant for a SOC as it can indicate an attacker's attempt to retrieve credential hashes through compromised servers, a key step in lateral movement and privilege escalation. If confirmed malicious, this activity could lead to unauthorized access to sensitive data and potential full system compromise.
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.
The analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
The following analytic detects the suspicious sysmon 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 identifies DNS queries to domains associated with the 3CX supply chain attack. This activity is significant because it can indicate a potential compromise stemming from the 3CX supply chain attack, which is known for distributing malicious software through trusted updates. If confirmed malicious, this activity could allow attackers to establish a foothold in the network, exfiltrate sensitive data, or further propagate malware, leading to extensive damage and data breaches.
This rule detects attempts to access domains that have been registered within the last 30 days. It correlates network HTTP or generic network connection events with WHOIS data to identify recently created domains. The rule leverages WHOIS enrichment to determine the creation time of the accessed domain and flags any access to domains created within the specified 30-day window.
The following analytic identifies the use of cleartext protocols that risk leaking sensitive information. It detects network traffic on legacy protocols such as Telnet (port 23), POP3 (port 110), IMAP (port 143), and non-anonymous FTP (port 21). The detection leverages the Network_Traffic data model to identify TCP traffic on these ports. Monitoring this activity is crucial as it can expose credentials and other sensitive data to interception. If confirmed malicious, attackers could capture authentication details, leading to unauthorized access and potential data breaches.
This analytic identifies high-severity intrusion events based on the classification assigned to Snort rules within Cisco Secure Firewall logs. It leverages Cisco Secure Firewall Threat Defense logs and focuses on events classified as: A Network Trojan was Detected, Successful Administrator Privilege Gain, Successful User Privilege Gain, Attempt to Login By a Default Username and Password, Known malware command and control traffic, Known malicious file or file based exploit, Known client side exploit attempt, Large Scale Information Leak. These classifications typically represent significant threats such as remote code execution, credential theft, lateral movement, or malware communication. Detection of these classifications should be prioritized for immediate investigation.
This analytic detects exploitation activity of CVE-2023-27532 using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signature 61514 (Veeam Backup and Replication credential dump attempt) is followed within a 5-minute window by 64795 (Veeam Backup and Replication xp_cmdshell invocation attempt), which detects the use of `xp_cmdshell`, a common post-exploitation technique. If confirmed malicious, this behavior is highly indicative of a successful exploitation of CVE-2023-27532, followed by remote command execution or credential dumping.
This analytic detects Lumma Stealer activity using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where four or more specific Snort signature IDs occur within a 15-minute span from the same host. This behavior is highly indicative of a successful infection by Lumma Stealer.
This analytic detects Lumma Stealer download attempts using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signatures with specific IDs have been triggered. If confirmed malicious, this behavior could indicate an active infection of Lumma Stealer.
This analytic detects Lumma Stealer outbound connection attempts using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where Snort signatures with specific IDs have been triggered. If confirmed malicious, this behavior could indicate an active infection of Lumma Stealer.
The following analytic identifies potential DLL side-loading instances involving unsigned DLLs mimicking Microsoft signatures. It detects this activity by analyzing Sysmon logs for Event Code 7, where both the `Image` and `ImageLoaded` paths do not match system directories like `system32`, `syswow64`, and `programfiles`. This behavior is significant as adversaries often exploit DLL side-loading to execute malicious code via legitimate processes. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to privilege escalation, persistence, and unauthorized access to sensitive information.
This analytic aggregates and summarizes alerts from Microsoft Defender for Endpoint. It extracts details such as source, file name, severity, process command line, IP address, registry key, signature, description, and unique ID. The detection is not intended to identify new activity from raw data but rather to leverage existing Microsoft Defender for Endpoint alerts for correlation and risk-based alerting. It filters out alerts with a 'clean' verdict and dynamically maps MITRE techniques and assigns risk scores based on the alert's severity.
This analytic identifies Snort signatures that have triggered only once in the past 7 days across all Cisco Secure Firewall IntrusionEvent logs. While these rules typically do not trigger in day-to-day network activity, their sudden appearance may indicate early-stage compromise, previously unseen malware, or reconnaissance activity against less commonly exposed services. Investigating these outliers can provide valuable insight into new or low-noise adversary behaviors.
This analytic identifies internal systems that generate a high volume of intrusion detection events within a 30-minute window, specifically leveraging Cisco Secure Firewall Threat Defense logs. It counts hosts that trigger 15 or more Snort-based signatures. A sudden increase in intrusion alerts from a single host can indicate malicious activity such as malware execution, command-and-control communication, vulnerability scanning, or lateral movement. It may also be caused by misconfigured software. Systems exhibiting this pattern require prompt investigation as repeated Snort rule matches are often early indicators of compromise or active exploitation.
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