Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Correlates devices touched by known IoC IP scan waves with PHP software inventory, vulnerability management data for PHP CVEs, and active PHP process execution telemetry to identify vulnerable exposed web servers.
This rule identifies instances of Cisco Secure Email Gateway (AsyncOS) running vulnerable versions that are susceptible to CVE-2026-76461. It parses the software inventory data to identify appliances within branches 15.5, 16.0, and 16.5, comparing the patch and build versions against the known secure release versions (15.5.5-0141, 16.0.4-302, 16.5.0-780).
This rule identifies instances of Cisco Secure Email Gateway (AsyncOS) running vulnerable versions that are susceptible to CVE-2026-76461. It parses the software inventory data to identify appliances within branches 15.5, 16.0, and 16.5, comparing the patch and build versions against the known secure release versions (15.5.5-0141, 16.0.4-302, 16.5.0-780).
This rule identifies instances of Cisco Secure Email Gateway (AsyncOS) running vulnerable versions that are susceptible to CVE-2026-76461. It parses the software inventory data to identify appliances within branches 15.5, 16.0, and 16.5, comparing the patch and build versions against the known secure release versions (15.5.5-0141, 16.0.4-302, 16.5.0-780).
This rule identifies instances of Cisco Secure Email Gateway (AsyncOS) running vulnerable versions that are susceptible to CVE-2026-76461. It parses the software inventory data to identify appliances within branches 15.5, 16.0, and 16.5, comparing the patch and build versions against the known secure release versions (15.5.5-0141, 16.0.4-302, 16.5.0-780).
This rule identifies instances of Cisco Secure Email Gateway (AsyncOS) running vulnerable versions that are susceptible to CVE-2026-76461. It parses the software inventory data to identify appliances within branches 15.5, 16.0, and 16.5, comparing the patch and build versions against the known secure release versions (15.5.5-0141, 16.0.4-302, 16.5.0-780).
Detects anomalous network scanning behavior from a Qualys scanner towards Domain Controller ports. The rule monitors CommonSecurityLog and NetworkSessions for high volumes of connection attempts (more than 50 within one hour) targeting critical Active Directory ports (88, 135, 389, 445, 636, 3268, 3269). This activity may indicate a scanner configuration error, a compromised scanner, or an adversary attempting to map the environment while masquerading as a security tool.
Detects successful logon events (4624) or special logon attempts (4648) associated with accounts or workstations identified as Qualys scanner agents. This is typically used to identify or baseline vulnerability scanning activity across the environment.
Detects successful logon events (4624) or special logon attempts (4648) associated with accounts or workstations identified as Qualys scanner agents. This is typically used to identify or baseline vulnerability scanning activity across the environment.
Detects anomalous network scanning behavior from a Qualys scanner towards Domain Controller ports. The rule monitors CommonSecurityLog and NetworkSessions for high volumes of connection attempts (more than 50 within one hour) targeting critical Active Directory ports (88, 135, 389, 445, 636, 3268, 3269). This activity may indicate a scanner configuration error, a compromised scanner, or an adversary attempting to map the environment while masquerading as a security tool.
This rule detects the usage of default credentials on assets by correlating vulnerability scan results from Qualys. Specifically, it looks for a predefined list of Qualys vulnerability IDs (QIDs) that indicate the presence of default credentials. The rule aims to identify systems that have not had their default passwords changed, which is a critical security hygiene practice.
This rule detects hosts that are identified by a Qualys vulnerability scan as not having a firewall product detected. This indicates a compliance issue where a host is not protected by a firewall, potentially exposing it to unauthorized access or attacks. The alert signifies non-compliance with security policies.
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 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.
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