Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a single external source IP reading secrets and configmaps across multiple Kubernetes namespaces in bulk within a 1-hour window, which may indicate reconnaissance or credential theft.
Detects Tailscale (tailscaled) reaching out to cloud instance metadata endpoints or Kubernetes internal service APIs, which may indicate unauthorized access or abuse of app connectors.
Detects the deployment or creation of containers (via Docker, Podman, Nerdctl, Crictl, or Kubectl) with elevated risk flags such as privileged mode, host namespace sharing, root mounts, docker socket access, or initiated from web application parent processes.
The following analytic detects the execution of curl commands with insecure flags within the Cisco Isovalent environment. It identifies this activity by monitoring process execution logs for curl commands that use the -k or --insecure flags. This behavior is significant for a SOC as it could allow an attacker to bypass SSL/TLS verification, potentially exposing the Kubernetes infrastructure to man-in-the-middle attacks. If confirmed malicious, this activity could lead to data interception, service disruptions, or unauthorized access to sensitive information.
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
Detects the use of standard Linux command-line utilities (e.g., cat, curl, vi) to read or reference sensitive cloud credential files or Kubernetes service account tokens. This behavior is a common indicator of credential theft or discovery activities following initial access to a Linux host.
The following analytic detects local file inclusion (LFI) attacks targeting Kubernetes Nginx ingress controllers. It leverages Kubernetes logs, parsing fields such as `request` and `status` to identify suspicious patterns indicative of LFI attempts. This activity is significant because LFI attacks can allow attackers to read sensitive files from the server, potentially exposing critical information. If confirmed malicious, this could lead to unauthorized access to sensitive data, further exploitation, and potential compromise of the Kubernetes environment.
Detects the creation or modification of Kubernetes RoleBindings or ClusterRoleBindings that grant highly privileged roles (e.g., cluster-admin or wildcard permissions) to a subject. The detection includes behavioral analysis to identify ServiceAccount usage from unusual source IP addresses within a short timeframe, which may indicate account compromise and unauthorized privilege escalation.
This rule detects potential container escape and privilege escalation attempts within a Kubernetes environment by monitoring for the creation of privileged pods, dangerous host mounts (such as /var/run/docker.sock or hostPath root mounts), and the use of escape-oriented utilities like nsenter or chroot, or access to the host's filesystem via /proc/1/root.
Detects a suspicious sequence of events where Kubernetes API resources (namespaces, secrets, configmaps, pods, or nodes) are enumerated, followed by a search query against the Slack API, indicative of a potential reconnaissance phase or sandbox escape attempt.
Detects various techniques used to escape from Linux container isolation, including accessing container daemon control sockets (Docker/containerd), abusing namespace operations (nsenter/setns), mounting sensitive host filesystem paths (e.g., /proc/1/root), performing chroot escapes, and abusing privileged capabilities or socket-based execution.
Detects unauthorized or anomalous modifications to machine learning model checkpoints. The rule triggers on significant deviations in weight delta percentages outside of scheduled fine-tuning windows, or when a model checkpoint hash is altered by users or service accounts without corresponding CI/CD pipeline approval or documented code review.
Detects high-risk administrative actions within a Kubernetes environment, specifically: users executing or attaching to running pods (potential container access), the creation of privileged pods (potential container escape), or modifications to Role-Based Access Control (RBAC) objects such as roles and role bindings which could lead to privilege escalation.
Detects the creation or modification of Kubernetes RoleBindings or ClusterRoleBindings that grant highly privileged roles (e.g., cluster-admin or wildcard permissions) to a subject. The detection includes behavioral analysis to identify ServiceAccount usage from unusual source IP addresses within a short timeframe, which may indicate account compromise and unauthorized privilege escalation.
Detects outbound HTTP POST requests containing an aggregated 'LOOT' variable in the body. The rule looks for markers indicating the exfiltration of Kubernetes-related credentials, such as service account tokens, bearer tokens, or configuration secrets, as well as common environment variable dumps like API keys.
Detects suspicious process activity or file access patterns originating from within a container that indicate an attempt to escape to the host. This includes monitoring for the use of tools like nsenter, unshare, and mount, attempts to interact with the docker.sock, or attempts to access critical host paths like /proc/1/root or /host/etc, excluding events originating from authorized container management processes.
Detects unauthorized privilege escalation for AI agent identities within an orchestration platform. The rule triggers when an AI agent account receives an IAM role change, API scope grant, or tool permission update that lacks a corresponding human-approval audit event, or occurs shortly after the agent generates logs indicating potential prompt injection or anomalous behavior.
This rule detects potentially malicious activity where an identity performs enumeration of Kubernetes secrets or configmaps and subsequently performs a search action within Slack from the same IP address within a one-hour window. This behavioral pattern is indicative of post-breakout reconnaissance, where a compromised identity or token is used to exfiltrate sensitive cloud configuration data and then perform internal reconnaissance in SaaS collaboration tools to identify further targets or credentials.
This rule detects unauthorized or anomalous file modifications or creations to machine learning training datasets (e.g., fine-tuning data, training corpora) by users not belonging to the authorized data engineering group, occurring within one hour of a scheduled training or fine-tuning job execution. This behavioral pattern is indicative of potential data poisoning, where an adversary attempts to inject malicious data into the training set to bias or compromise the resulting model.
Detects unauthenticated GET requests to known AI and machine learning infrastructure management endpoints (e.g., DCGM, Ray, Kubeflow) that are commonly exposed without authentication. These interfaces provide operational metrics, cluster status, or job management details, which can be leveraged for reconnaissance.
Detects the deployment of the CARBONATO botnet's Hermes Agent within containerized environments. The rule identifies specific artifacts such as the 'SOUL.md' persona configuration, 'netd-svc' container references, and indicators of AI-focused credential theft (e.g., searching for OpenAI or Anthropic API keys) orchestrated by the GH0ST AI agent.
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