Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
219 detections
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The following detects interactive shell execution within a container in a production K8s namespace.
This could be indicative of an attacker with a compromised user account attempting to control a container.
This could be indicative of an attacker with a compromised user account attempting to control a container.
Detects deleting of K8s log events. Indicating Defense Evasion tactics, and covering of tracks.
This detection identifies when a user or service principal executes a command inside an AKS pod ('kubectl exec') and then, using the same identity, accesses data in a sensitive Azure Blob Storage container within a short time frame. This pattern can indicate an attacker, having gained initial access to the Kubernetes API, is using a pod as a pivot point to discover and exfiltrate sensitive data.
Detects interactive administration commands (exec, attach, port-forward) within a sensitive Kubernetes namespace. Such activity can be legitimate but warrants scrutiny in high-value environments as it allows direct interaction with containerized applications and data.
Detects interactive shell execution within a container in a production Kubernetes namespace. This activity can be indicative of an attacker with a compromised user account attempting to manually control a container for exploration, privilege escalation, or data staging. Legitimate debugging by administrators may also trigger this rule.
Detects interactive shell execution within a container in a production Kubernetes namespace. This activity can be indicative of an attacker with a compromised user account attempting to manually control a container for exploration, privilege escalation, or data staging. Legitimate debugging by administrators may also trigger this rule.
Detects when a modification (create, update, replace, delete, or patch) action is taken that affects mutating or validating webhook configurations in Kubernetes. Adversaries can abuse these configurations to achieve persistence, exfiltrate credentials, or escalate privileges within the cluster.
Identifies when an admission controller is executed in GCP Kubernetes. A Kubernetes Admission controller intercepts, and possibly modifies, requests to the Kubernetes API server. The behavior of this admission controller is determined by an admission webhook (MutatingAdmissionWebhook or ValidatingAdmissionWebhook) that the user deploys in the cluster. An adversary can use such webhooks as the MutatingAdmissionWebhook for obtaining persistence in the cluster. For example, attackers can intercept and modify the pod creation operations in the cluster and add their malicious container to every created pod. An adversary can use the webhook ValidatingAdmissionWebhook, which could be used to obtain access credentials. An adversary could use the webhook to intercept the requests to the API server, record secrets, and other sensitive information.
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 rule identifies when an admission controller is executed in Azure Kubernetes. Admission controllers intercept and potentially modify requests to the Kubernetes API server. Adversaries can leverage admission webhooks (MutatingAdmissionWebhook or ValidatingAdmissionWebhook) for persistence, such as injecting malicious containers into pods or intercepting API requests to steal credentials and sensitive information.
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.
This rule detects the creation of a Kubernetes pod with a privileged container. Privileged containers have full root capabilities on the host and can bypass many security mechanisms, making them a significant security risk. The rule specifically excludes system service accounts from the 'kube-system' namespace to reduce false positives from legitimate system operations.
This rule detects the creation of Kubernetes pods by non-system service accounts within namespaces that start with 'kube'. This could indicate an attempt to establish persistence or escalate privileges within the Kubernetes cluster by creating malicious pods in critical system namespaces.
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.
KQL Query from file: Hunting IngressNightmare (CVSS 9.8)
The following analytic identifies anomalous network traffic volumes between Kubernetes workloads or between a workload and external sources. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days to identify significant deviations. This activity is significant as unexpected spikes may indicate unauthorized data transfers or lateral movement. If confirmed malicious, it could lead to data exfiltration or compromise of additional services, potentially resulting in data breaches.
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