Splunk Security
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The following analytic identifies the use of the 'lsmod' command to list kernel modules on a Linux system. This detection leverages data from Linux Auditd, focusing on process names and command-line executions. While listing kernel modules is not inherently malicious, it can be a precursor to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity could allow an attacker to load kernel modules, potentially leading to privilege escalation, persistence, or other malicious actions within the system.
The following analytic detects the execution of the 'doas' tool on a Linux host. This tool allows standard users to perform tasks with root privileges, similar to 'sudo'. The detection leverages data from Linux Auditd, focusing on process names and command-line executions. This activity is significant as 'doas' can be exploited by adversaries to gain elevated privileges on a compromised host. If confirmed malicious, this could lead to unauthorized administrative access, potentially compromising the entire system.
The following analytic detects suspicious use of the `rmmod` utility for kernel module removal, which may indicate an attacker attempt to unload critical or security-related kernel modules. The `rmmod` command is used to remove modules from the Linux kernel, and unauthorized use can be a tactic to disable security features, conceal malicious activities, or disrupt system operations. By monitoring for unusual or unauthorized `rmmod` activity, this analytic helps identify potential tampering with kernel modules, enabling security teams to take proactive measures to protect system integrity and security.
The following analytic detects suspicious calendar invites sent via GSuite, potentially indicating compromised accounts or malicious internal activity. It leverages GSuite calendar logs, focusing on events where a high volume of invites (over 100) is sent within a 5-minute window. This behavior is significant as it may involve the distribution of malicious links or attachments, posing a security risk. If confirmed malicious, this activity could lead to widespread phishing attacks, unauthorized access, or malware distribution within the organization.
The analytic identifies ICMP traffic to external IP addresses with total bytes (sum of bytes in and bytes out) greater than 1,000 bytes. It leverages network traffic data to detect large ICMP packets that aren't blocked and are directed toward external networks. This can indicate information smuggling, covert communication, or command-and-control (C2) activities, potentially signaling ICMP tunneling, unauthorized data transfer, or compromised endpoints.
The following analytic identifies processes referencing plist files that determine which applications are re-opened when a user reboots their MacOS machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes related to "com.apple.loginwindow." This activity is significant because it can indicate attempts to persist across reboots, a common tactic used by attackers to maintain access. If confirmed malicious, this could allow an attacker to execute code or maintain persistence on the affected system, potentially leading to further compromise.
The following analytic detects the execution of the "At" application in Linux, which can be used by attackers to create persistence entries on a compromised host. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent process names associated with "at" or "atd". This activity is significant because the "At" application can be exploited to maintain unauthorized access or deliver additional malicious payloads. If confirmed malicious, this behavior could lead to data theft, ransomware attacks, or other severe consequences. Immediate investigation is required to determine the legitimacy of the execution and mitigate potential risks.
The following analytic detects suspicious system network configuration discovery activities, which may indicate an adversary's attempt to gather information about the network environment. Such actions typically involve commands or tools used to identify network interfaces, routing tables, and active connections. Detecting these activities is crucial, as they often precede more targeted attacks like lateral movement or data exfiltration. By identifying unusual or unauthorized network discovery efforts, this analytic helps security teams to swiftly detect and respond to potential reconnaissance operations, mitigating the risk of further compromise.
This detection identifies when a new Microsoft Intune device management configuration policy has been created. Intune device management configuration policies are used by administrators to remotely manage policies and settings on Intune-managed devices. This functionality can be abused by adversaries to disable defenses and evade detection.
The analytic identifies access denied errors when attempting to invoke AWS Bedrock models. It leverages AWS CloudTrail logs to detect when a user or service receives an AccessDenied error when calling the InvokeModel API. This activity is significant as it may indicate an adversary attempting to access Bedrock models with insufficient permissions after compromising credentials. If confirmed malicious, this could suggest reconnaissance activities or privilege escalation attempts targeting generative AI resources, potentially leading to data exfiltration or manipulation of model outputs.
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