Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
48 detections
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Detects the insertion of malicious prompt injection payloads—containing imperative commands to AI agents or hidden unicode/non-printable characters—into wiki pages, ticketing systems, or other shared knowledge bases. These payloads are designed to be ingested by AI agents or Retrieval-Augmented Generation (RAG) pipelines, leading to unauthorized instructions or behavioral overrides upon retrieval.
This rule detects potential exploitation activities targeting Atlassian Confluence by monitoring for unauthorized access to administrative configuration actions (e.g., setup/setupadministrator.action) and the subsequent addition of accounts to the 'confluence-administrators' group. It correlates process execution, network request patterns, and local user account modifications to identify successful or attempted privilege escalation and configuration manipulation.
This rule detects potential data exfiltration by monitoring for anomalous user behavior across integrated SaaS applications. It flags accounts that perform high-frequency read, search, or view activities across three or more monitored SaaS platforms (Confluence, Jira, Slack, Google Workspace, Microsoft 365, or Bitbucket) within a 5-minute window, followed immediately by an upload or post activity to an external, non-integrated domain.
This rule detects potential data exfiltration by monitoring for anomalous user behavior across integrated SaaS applications. It flags accounts that perform high-frequency read, search, or view activities across three or more monitored SaaS platforms (Confluence, Jira, Slack, Google Workspace, Microsoft 365, or Bitbucket) within a 5-minute window, followed immediately by an upload or post activity to an external, non-integrated domain.
Detects bulk export of Confluence wiki content
This rule detects a high volume of HTTP requests to the Confluence REST API endpoint '/rest/api/content'. This pattern of access could indicate an attempt to bulk download content from Confluence spaces, potentially for data exfiltration or reconnaissance purposes. The threshold is set to trigger if 30 such requests are observed from a single source IP within a 60-second window.
This rule detects a high volume of HTTP requests to a Confluence Wiki instance that include both '/wiki/' and 'export' in the URI. Specifically, it triggers if 10 such requests are observed from the same source IP within a 60-second window. This behavior could indicate an attempt to bulk export pages from a Confluence Wiki, potentially for data exfiltration.
Confluence Wiki Bulk Export
Suricata
This rule detects HTTP requests to the '/spaces/exportspace.action' URI, which is associated with bulk export functionality in Confluence Wiki. This activity could indicate an attempt to exfiltrate data from a Confluence instance.
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.
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.
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