Methods and apparatus for identifying and responding to attack paths targeting Kubernetes container escape

Abstract

The embodiments of this disclosure provide a method and apparatus for identifying and responding to attack paths for Kubernetes container escape. The method includes: collecting resource information in a Kubernetes cluster and constructing a permission relationship graph based on the collected resource information; monitoring container behavior in the Kubernetes cluster in real time using security monitoring tools; when container escape behavior is detected, identifying and marking permission jump pods, starting a path search algorithm, and deriving attack paths in the permission relationship graph; and triggering a policy response mechanism based on the attack path analysis results, performing response measures such as blocking, alerting, and recommending least privileges for the attack path, and visually displaying the attack path graph.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of cloud platform security detection technology, and more specifically, to a method, apparatus, and computer-readable storage medium storing computer programs for identifying and responding to attack paths that can escape Kubernetes containers. Background Technology

[0002] Containerization technology and the Kubernetes orchestration system have become the basic architectural form for modern internet and enterprise service deployments. However, while Kubernetes' modularity, high configurability, and plugin ecosystem bring flexible management capabilities, they also introduce security risks such as complex permission systems, exposed system components, and unclear remote access boundaries. Currently, once an attacker successfully escapes a container, they can obtain sufficient resources and information on the host node to seek further privilege escalation within the cluster.

[0003] Among existing mainstream defense solutions, RBAC-police can identify redundant permissions in service account or role configurations based on Rego rules. However, it uses static rule judgment, does not build a dynamic closed path model, and does not determine whether the attacker can reach the target service account. Falco and eBPF tools focus on behavior analysis during container runtime and can be used to detect container escape operations themselves, but they do not understand permission semantics and resource connection relationships within the cluster, making it difficult to determine the subsequent impact of events on the cluster. Policy-based gateway tools (such as OPA / Gatekeeper) can enforce configuration rules at the Admission Controller stage to prevent non-compliant resource submissions. However, these tools mainly monitor key fields at the resource layer (such as whether there is a hostPath), and cannot identify or roll back attacks that occur during runtime. In addition, although cloud platforms (such as AWS EKS and Google GKE) provide IAM control measures for system components, they mostly operate at the cloud account level and cannot control the scope of Pod-level RBAC. Once an attacker obtains a service account or Kubelet configuration, it is impossible to effectively block or prevent the attacker from further expanding permissions.

[0004] It is evident that the current Kubernetes security architecture still has several key shortcomings in preventing privilege escalation attacks. For example, it lacks attack chain modeling capabilities, the high-risk nature of system components is not sufficiently emphasized, the underlying permission meanings and potential subsequent impacts of actions are ignored, the development and deployment process lacks methods for proactively identifying abnormal permissions, permissions are often "valid for a long time" after being established, and there is a lack of event-driven permission blocking mechanisms. These shortcomings leave Kubernetes clusters significantly vulnerable to privilege escalation attacks. Summary of the Invention

[0005] The embodiments described herein provide a method, apparatus, and computer-readable storage medium storing computer programs for identifying and responding to attack paths that lead to Kubernetes container escape. Through graph modeling, behavior triggering linkage, and permission path analysis, it can effectively address the shortcomings of existing Kubernetes security systems in detecting and preventing privilege escalation after container escape.

[0006] According to a first aspect of this disclosure, a method for identifying and responding to attack paths for Kubernetes container escape is provided, comprising: collecting resource information in a Kubernetes cluster and constructing a permission relationship graph based on the collected resource information; monitoring container behavior in the Kubernetes cluster in real time through security monitoring tools; when container escape behavior is detected, identifying and marking permission jump pods, starting a path search algorithm, and deriving the attack path in the permission relationship graph; and triggering a policy response mechanism based on the attack path analysis results, performing response measures such as blocking, alerting, and recommending least privilege on the attack path, and visually displaying the attack path graph.

[0007] In some embodiments of this disclosure, collecting resource information in the Kubernetes cluster and constructing a permission relationship graph based on the collected resource information includes: monitoring changes in permission-related Pods, service accounts, role bindings, and API operations in real time through the Kubernetes Watcher mechanism, and collecting permission resource relationships from the API Server; constructing a dynamic RBAC permission edge set based on the collected permission resource relationships, and vectorizing the role permissions of each service account through a role permission mapper; and combining role permissions with the Pod locations bound to the service accounts to construct a Pod permission reflection point graph.

[0008] In some embodiments of this disclosure, real-time monitoring of container behavior in a Kubernetes cluster using security monitoring tools includes: capturing system call events executed by containers through an integrated behavior listener; marking call events with escape characteristics when accessing sensitive system paths, writing to host machine paths, or invoking network-level permissions; and passing the container's Pod UID to an event handler when relevant abnormal behavior is detected, and associating the event information with the container's runtime context.

[0009] In some embodiments of this disclosure, when container escape behavior is detected, the permission escalator pod is identified and marked, and a path search algorithm is initiated to deduce the attack path in the permission relationship graph. This includes: parsing the RBAC permission structure of the service account bound to each pod, the pod deployment information, and the node scheduling information, and calculating the comprehensive risk score of the pod; identifying and marking the pod that elevates the attacker's privileges as the permission escalator pod based on the comprehensive risk score; and finding the permission jump path from the attack source pod to cluster-admin privileges based on the graph path search algorithm.

[0010] In some embodiments of this disclosure, parsing the RBAC permission structure, pod deployment information, and node scheduling information of the service account bound to each pod to calculate the comprehensive risk score of the pod includes: calculating the RBAC permission score based on the permission level of the service account bound to each pod; classifying the pod deployment type into system components and application components, and calculating the pod deployment score according to the pod deployment type; analyzing whether the node deployed by the pod is on the same node as the attacker's container scheduling that triggered the container escape event, and calculating the node reachability score based on node scheduling reachability; calculating the behavioral proximity score based on the proximity of the pod to sensitive resources; and weighting the RBAC permission score, pod deployment score, node reachability score, and behavioral proximity score according to different weights to finally obtain the comprehensive risk score of each pod.

[0011] In some embodiments of this disclosure, finding a permission jump path from the attack source Pod to cluster-admin privileges based on a graph path search algorithm includes: finding the shortest path from the attack source Pod through intermediate stepping nodes to the node with cluster-admin privileges based on a shortest path algorithm; and selecting the top K most threatening attack chains based on the risk score of the path and the attack probability of the nodes.

[0012] In some embodiments of this disclosure, when an attack path is established, a policy response mechanism is triggered to perform response measures such as blocking, alerting, and recommending least privilege on the attack path, and to visualize the attack path graph. This includes: assessing the risk score of the attack path; when the risk score exceeds a preset risk threshold, suspending the operation of the relevant Pods, modifying the permissions of the relevant resources, and rejecting unaudited access requests; after the permission modification operation is completed, generating a YAML file recommending least privilege based on the adjusted permission status. The YAML file includes the modified service account binding, the downgraded role permissions, and the recommended configuration of least privilege; pushing the YAML file to the management repository through a GitOps management tool, and synchronizing the permission changes to the Kubernetes cluster through an automated deployment tool; and on the front-end page, using a JavaScript graphics library to load the attack path graph in SVG format or generating the attack path graph by parsing data in JSON format.

[0013] In some embodiments of this disclosure, the node information of the attack path graph includes the permission level of the resource and the risk factor of the jump site, and the information of each edge includes the operation type and the type of resource affected by the operation.

[0014] According to a second aspect of this disclosure, an attack path identification and response device for Kubernetes container escape is provided. The device includes at least one processor and at least one memory storing a computer program. When the computer program is executed by the at least one processor, the device causes the following actions: collecting resource information from the Kubernetes cluster and constructing a permission relationship graph based on the collected resource information; monitoring container behavior in the Kubernetes cluster in real time using security monitoring tools; when container escape behavior is detected, identifying and marking permission jump pods, initiating a path search algorithm to deduce attack paths from the permission relationship graph; triggering a policy response mechanism based on the attack path analysis results, implementing response measures such as blocking, alerting, and least privilege recommendation for the attack path, and visually displaying the attack path graph.

[0015] According to a third aspect of this disclosure, a computer-readable storage medium storing a computer program is provided, wherein the computer program, when executed by a processor, implements the steps of an attack path identification and response method for Kubernetes container evasion according to a first aspect of this disclosure.

[0016] The attack path identification and response method and apparatus for Kubernetes container escape according to embodiments of this disclosure constructs a linkage graph of attack behaviors by identifying permission escalation Pods in the container environment. This enables the detection of potential threats in the early stages of container escape and limits the spread of attacks through automated response mechanisms, ensuring the security of the Kubernetes cluster against container escape and privilege escalation attacks. This multi-layered security protection not only improves the accuracy of attack detection but also optimizes the response speed and efficiency to high-risk events. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments will be briefly described below. It should be understood that the drawings described below only relate to some embodiments of this disclosure and are not intended to limit this disclosure, wherein:

[0018] Figure 1 An exemplary flowchart is shown for an attack path identification and response method for Kubernetes container escape according to an embodiment of the present disclosure;

[0019] Figure 2 This is a schematic block diagram of an attack path identification and response apparatus for Kubernetes container escape according to embodiments of the present disclosure.

[0020] It should be noted that the elements in the attached diagram are schematic and not drawn to scale. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are also within the scope of protection of this disclosure.

[0022] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this subject matter pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having meanings consistent with their meanings in the context of the specification and in the relevant art, and shall not be interpreted in an idealized or overly formal form unless otherwise explicitly defined herein. Furthermore, terms such as “first” and “second” are used only to distinguish one component (or part of a component) from another component (or another part of a component).

[0023] Traditional Kubernetes security solutions typically rely on static rules and container image-based checks; however, attackers often circumvent these rules through escape behaviors. This disclosure proposes a security detection and response scheme suitable for Kubernetes containerized cloud-native environments. By analyzing permission relationships across the entire cluster, it can defend against attacker escape behaviors at an early stage, thus improving the security of the container environment.

[0024] To further illustrate the embodiments of this disclosure, Figure 1 An exemplary flowchart illustrating an attack path identification and response method for Kubernetes container escape according to embodiments of this disclosure is shown. Figure 1 At frame S102, resource information in the Kubernetes cluster is collected, and a permission relationship graph is constructed based on the collected resource information.

[0025] In one embodiment of this disclosure, Kubernetes' watcher mechanism can be used to monitor changes in permission-related resources on the API Server in real time, including changes to objects such as Pods, Service Accounts (SAs), Role Bindings (ClusterRole / RoleBinding), and API operations, to collect permission resource relationships from the API Server. Here, a Pod is the smallest unit of computation in Kubernetes, and each Pod may contain multiple containers. It is necessary to collect the Pod's name, container information within the Pod, and their access permissions. A Service Account is the identity of a Pod interacting with the Kubernetes API. Each Pod is associated with a Service Account, which has different access permissions; it is necessary to collect the information for each Service Account and its associated permissions. Role binding refers to the binding of a role in Kubernetes to a Service Account; each binding indicates that the Service Account possesses the permissions of a specific role.

[0026] Based on the collected permission resource relationships, a dynamic RBAC permission edge set is constructed. The RBAC (Role-Based Access Control) model controls access permissions by defining roles and role bindings. By continuously updating permission resource relationships, a dynamic RBAC permission edge set is built, continuously tracking permission changes for each user, Pod, service account, and other objects, forming a dynamically updated security model. The role permissions of each service account are vectorized using a role-based permission mapper. After vectorizing each service account, it is combined with the location of the Pod bound to the service account to generate a Pod permission reflection graph. This graph displays the permission status of different Pods, including whether the Pod belongs to a DaemonSet (which is deployed on every node in the cluster), on which nodes it is deployed, and whether the Pod has requested the hostpath of the host machine or has privilegedAttr permissions. These special permissions can be used by attackers to bypass container restrictions, gain host machine permissions, and then carry out container escape attacks. Therefore, the system can identify these privileged attributes and mark them in the permission graph, thereby promptly detecting potential security threats. By checking whether a Pod is a DaemonSet, it is possible to identify which Pods are critical services present on every node in the cluster. These Pods can become targets for attackers to escape and move laterally in the attack chain.

[0027] By continuously monitoring and collecting permission resources within the cluster, and combining this with the distribution coordinates of Pods and permission requests, the system can detect potential security threats at the first sign of permission changes. The constructed Pod permission reflection graph enables the system to predict possible paths attackers may take within the cluster. By dynamically mapping and analyzing permission paths, the system can proactively identify high-risk nodes and services, preparing for subsequent attack chain responses.

[0028] The permission relationship graph uses Pods, service accounts, role bindings, and API Verbs as nodes, connected by directed edges, to reflect the relationships between various resources and permissions in a Kubernetes cluster. This graph can be implemented and stored using a graph database (such as Neo4j) or built-in data structures (such as adjacency lists or adjacency matrices). Pods, as execution units, access other Kubernetes resources through their bound service accounts. Service accounts are bound to roles to grant Pods access to cluster resources. Each service account is bound to multiple Pods and roles, forming a graph structure. Each RoleBinding or ClusterRoleBinding forms an edge with its bound role, connecting to the Pod or service account node. API Verbs nodes represent operation permissions on Kubernetes resources, reflecting each role's access permissions, such as whether they can perform read operations (get, list) or modify operations (create, update).

[0029] In box S104, the behavior of containers in the Kubernetes cluster is monitored in real time using security monitoring tools.

[0030] To respond promptly and prevent attackers from bypassing isolation and gaining higher privileges through container escape, behavior awareness can be combined with static permission path deduction. In one embodiment of this disclosure, a behavior listener is integrated to capture system call events executed by the container. When access to sensitive system paths, writing to host machine paths, or invoking network-level permissions are captured, call events with escape characteristics are marked. For example, container runtime security tools such as Falco, eBPF, and Audit logs are used to monitor possible attack behaviors in the container, such as access to / proc / self / ns / pid, writing to host machine paths, and using socketcall characteristics. Once relevant abnormal behavior is detected, the container's Pod UID is passed to the event handler, and the event information is associated with the container's runtime context for subsequent analysis.

[0031] Subsequently, in box S106, when container escape behavior is detected, the permission jump pod is identified and marked, and the path search algorithm is started to deduce the attack path in the permission relationship graph.

[0032] After a container escape occurs, the system analyzes whether the attacker's node contains a high-privilege Pod with privilege escalation capabilities, i.e., a privilege escalation Pod. In one embodiment of this disclosure, the RBAC permission structure of the service account bound to each pod, pod deployment information, and node scheduling information are parsed to calculate the Pod's comprehensive risk score.

[0033] Each Pod's score is based on a comprehensive evaluation of multiple dimensions, including Pod permissions, deployment origin, node scheduling, and behavioral proximity. Specifically, the RBAC (Restricted Access Control) permission score is calculated based on the permission level of the service account bound to each Pod. For example, by examining the Kubernetes API operations that a Pod can perform, such as create, update, patch, get, and list, a risk score is assigned to each Pod, with different operation permissions carrying different levels of harm. For instance, a Pod with cluster-admin permissions is more likely to become a springboard for attack paths than a Pod with view permissions. Pod deployment types are categorized into system components and application components, and a Pod deployment score is calculated based on this type. For example, tags such as app.kubernetes.io / managed-by and Deployment type are used for identification; if a Pod originates from a system component, its risk score will be weighted higher.

[0034] The analysis checks whether the node where the Pod is deployed is on the same node as the attacker's container that triggered the container escape event, calculating a node reachability score based on node scheduling reachability. If the Trampoline Pod and the attacker's container are on the same node, it indicates that the attacker may be able to jump to higher-privilege resources through this Pod, posing a greater risk. If the Trampoline Pod's running user has the ability to automatically mount / var / run / secrets / kubernetes.io, or if the Pod is configured with a hostPath setting, its token may be easily accessible, possessing a high hop potential. Such Pods score higher in the risk score.

[0035] The RBAC permission score, Pod deployment score, node reachability score, and behavioral proximity score are weighted according to different weights to obtain a comprehensive risk score for each Pod. Based on this comprehensive risk score, Pods that enable attackers to escalate privileges are identified and marked as privilege escalation Pods. For example, in the permission relationship graph, privilege escalation Pods are presented as red nodes for quick identification of high-risk nodes. Once an attacker enters the cluster through escape behavior and gains access to these Pods, they become the key steps for the attacker to escalate privileges.

[0036] This invention employs a graph path search algorithm to find permission hop paths from the attack source Pod to cluster-admin privileges. An attacker's entry point is typically a Pod or a service account; searching these nodes as the root nodes of the attack chain effectively traces the attack chain's path. In one embodiment of this disclosure, a shortest path algorithm (such as Dijkstra / DFS) can be used to find the shortest path from the attack source Pod, through intermediate stepping nodes, to a node with cluster-admin privileges. This helps to understand the attacker's permission evolution path in a timely manner and implement preventative measures. Based on the path's risk score and the attack probability of the nodes, the top K most threatening attack chains are selected. These attack chains serve as early warning outputs to help security personnel respond and defend in a timely manner.

[0037] Finally, in box S108, when an attack path is established, a policy response mechanism is triggered to perform response measures such as blocking, alerting, and recommending least privilege for the attack path, and to visualize the attack path map.

[0038] In a Kubernetes environment, once a potential permission expansion path is detected, various control measures can be taken to prevent permission escalation or data leakage. In one embodiment of this disclosure, the risk score of the attack path is evaluated. When the risk score exceeds a preset risk threshold, an alarm is pushed, the operation of the relevant Pod is suspended, the permissions of the relevant resources are modified, and unaudited access requests are denied.

[0039] For example, alerts include information such as the relevant Pod, ServiceAccount, RoleBinding, and permissions, helping the operations team to identify potential risks promptly. To reduce attackers' exploitation of privilege extension paths within a Pod, the Pod's serviceAccountName is modified via the Kubernetes API to remove exploitable permissions. For instance, the binding between a Pod and a high-privilege ServiceAccount is removed. If a ServiceAccount is bound to unnecessary high privileges (such as cluster-admin), it is replaced with the least privilege role, ensuring that the ServiceAccount cannot access unnecessary cluster resources. This is achieved by deleting the existing RoleBinding or ClusterRoleBinding and creating a new RoleBinding, binding it to the least privilege role. For newly submitted resources (such as new Pods), if they use an unaudited or high-privilege ServiceAccount, the Admission Hook will reject the submission. This prevents unauthorized ServiceAccounts from being used and ensures all resources comply with security standards.

[0040] After the permission modification operation is completed, a YAML file recommending minimum permissions is generated based on the adjusted permission status. The YAML file includes the modified service account bindings, the downgraded role permissions, and the recommended minimum permission configuration. The YAML file is pushed to the management repository using GitOps management tools, and the permission changes are synchronized to the Kubernetes cluster using automated deployment tools.

[0041] Finally, on the front-end page, an attack path graph in SVG format is loaded using a JavaScript graphics library, or generated by parsing JSON data. The node information in the attack path graph includes the resource's permission level and the risk level of the jump nodes. For example, certain Pods with high privileges (such as Pods with the cluster-admin role) may be highly dangerous. Each edge's information includes the operation type and the type of resource affected by that operation. For example, create, update, get, etc., indicating what operation the attacker used to trigger the path change.

[0042] An example of the output attack path diagram is as follows:

[0043] Attack source Pod (low privileges) → Gains access privileges via ServiceAccount (SA)

[0044] SA → is bound to a higher-privilege role (such as cluster-admin).

[0045] The SA was exploited by attackers, granting them expanded privileges and allowing the attack path to reach the Trampoline Pod.

[0046] Token change (privilege escalation) → Complete privilege escalation and obtain cluster-admin privileges

[0047] After requesting the attack path graph via API or frontend, a dynamic interactive graph is generated. Users can view the attack path and analyze each node and its permissions. Users can zoom and pan the graph, and click on each node to view detailed information (such as permission level, risk level, etc.). Users can choose to start from a specific node to view the branching flow of the attack path, helping to quickly locate the attack source.

[0048] Figure 2 This is a schematic block diagram of an attack path identification and response apparatus for Kubernetes container escape according to embodiments of this disclosure. Figure 2 As shown, the device 200 may include a processor 210 and a memory 220 storing a computer program. When the computer program is executed by the processor 210, the device 200 is made capable of performing actions such as... Figure 1The steps of the method are shown. In one example, device 200 may be a computer device or a cloud computing node. Device 200 may: collect resource information in a Kubernetes cluster and construct a permission relationship graph based on the collected resource information; monitor container behavior in the Kubernetes cluster in real time through security monitoring tools; when container escape behavior is detected, identify and mark permission jump pods, start a path search algorithm, and deduce the attack path in the permission relationship graph; and trigger a policy response mechanism based on the attack path analysis results, perform response measures such as blocking, alerting, and least privilege recommendation on the attack path, and visualize the attack path graph.

[0049] In the embodiments of this disclosure, the device 200 can monitor changes in permission-related Pods, service accounts, role bindings, and API operations in real time through the Kubernetes Watcher mechanism, collect permission resource relationships from the API Server, construct a dynamic RBAC permission edge set based on the collected permission resource relationships, vectorize the role permissions of each service account through a role permission mapper, and combine the role permissions with the Pod locations bound to the service accounts to construct a Pod permission reflection point graph.

[0050] In the embodiments of this disclosure, the device 200 can capture system call events executed by the container through an integrated behavior listener. When it captures access to sensitive system paths, writing to host paths, or invoking network-level permissions, it marks the call events with escape characteristics. When related abnormal behavior is detected, it passes the container's Pod UID to the event handler and associates the event information with the container's runtime context.

[0051] In the embodiments of this disclosure, the device 200 can parse the RBAC permission structure, pod deployment information and node scheduling information of the service account bound to each pod, and calculate the comprehensive risk score of the pod; identify and mark the pod that enables the attacker to escalate privileges as a permission springboard pod based on the comprehensive risk score; and find the permission jump path from the attack source pod to the cluster-admin privilege based on the graph path search algorithm.

[0052] In the embodiments of this disclosure, the device 200 can calculate the RBAC permission score based on the permission level of the service account bound to each Pod; classify the deployment type of the Pod into system components and application components, and calculate the Pod deployment score according to the Pod deployment type; analyze whether the node deployed by the Pod is on the same node as the attacker's container scheduling that triggered the container escape event, and calculate the node reachability score according to the node scheduling reachability; calculate the behavioral proximity score according to the proximity of the Pod to sensitive resources; and weight the RBAC permission score, Pod deployment score, node reachability score and behavioral proximity score according to different weights to finally obtain the comprehensive risk score of each Pod.

[0053] In the embodiments of this disclosure, the device 200 can find the shortest path from the attack source Pod through intermediate jumper nodes to the node with cluster-admin privileges based on the shortest path algorithm; and select the top K most threatening attack chains based on the risk score of the path and the attack probability of the nodes.

[0054] In the embodiments of this disclosure, device 200 can assess the risk score of an attack path. When the risk score exceeds a preset risk threshold, it pushes an alarm message, suspends the operation of the relevant Pod, modifies the permissions of the relevant resources, and denies unaudited access requests. After the permission modification operation is completed, a YAML file with minimum permissions is generated based on the adjusted permission status. The YAML file includes the modified service account binding, the downgraded role permissions, and the recommended minimum permission configuration. The YAML file is pushed to the management repository through a GitOps management tool, and the permission changes are synchronized to the Kubernetes cluster through an automated deployment tool. On the front-end page, an attack path graph in SVG format is loaded using a JavaScript graphics library or an attack path graph is generated by parsing JSON format data.

[0055] In embodiments of this disclosure, processor 210 may be, for example, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a processor based on a multi-core processor architecture, etc. Memory 220 may be any type of memory implemented using data storage technologies, including but not limited to random access memory, read-only memory, semiconductor-based memory, flash memory, disk storage, etc.

[0056] Furthermore, in embodiments of this disclosure, device 200 may also include input device 230, such as a keyboard, mouse, etc. Additionally, device 200 may also include output device 240, such as a display, for example, to visually display an attack path map.

[0057] In other embodiments of this disclosure, a computer-readable storage medium storing a computer program is also provided, wherein the computer program, when executed by a processor, is capable of performing the following functions: Figure 1 The steps shown are for identifying and responding to attack paths that target Kubernetes container escape.

[0058] In summary, the attack path identification and response method and apparatus for Kubernetes container escape according to the embodiments of this disclosure constructs a linkage graph of attack behaviors by identifying permission escalation Pods in the container environment. This enables the detection of potential threats in the early stages of container escape and limits the spread of attacks through automated response mechanisms, ensuring the security of the Kubernetes cluster against container escape and privilege escalation attacks. This multi-layered security protection not only improves the accuracy of attack detection but also optimizes the response speed and efficiency to high-risk events.

[0059] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatuses and methods according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0060] Unless otherwise expressly indicated by the context, the singular form of words used herein and in the appended claims includes the plural form, and vice versa. Thus, when referring to the singular, the plural form of the corresponding term is generally included. Similarly, the terms “comprising” and “including” shall be interpreted as including rather than exclusively. Likewise, the terms “including” and “or” shall be interpreted as including unless such interpretation is expressly prohibited herein. Where the term “example” is used herein, particularly when it follows a set of terms, “example” is merely exemplary and illustrative and should not be considered exclusive or extensive.

[0061] Further aspects and scope of adaptation become apparent from the description provided herein. It should be understood that various aspects of this application may be implemented individually or in combination with one or more other aspects. It should also be understood that the descriptions and specific embodiments herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0062] Several embodiments of this disclosure have been described in detail above. However, it is obvious that those skilled in the art can make various modifications and variations to the embodiments of this disclosure without departing from the spirit and scope of this disclosure. The scope of protection of this disclosure is defined by the appended claims.

Claims

1. A Kubernetes container escape-oriented attack path identification and response method, characterized in that, The method comprises: Collecting resource information in the Kubernetes cluster, and constructing a permission relationship graph based on the collected resource information, including: collecting permission resource relationships from the API Server by real-time monitoring of changes in Pods, service accounts, role bindings, and API operations related to permissions through the Kubernetes Watcher mechanism; constructing a dynamic RBAC permission edge set according to the collected permission resource relationships; representing the role permissions of each service account in a vector form through a role permission mapper; and combining the role permissions with the Pod positions of the service account bindings to construct a Pod permission reflection point graph; Real-time monitoring of container behavior in the Kubernetes cluster through a security monitoring tool; When a container escape behavior is detected, identifying and marking a permission jump pod, starting a path search algorithm, and deriving an attack path in the permission relationship graph; and Triggering a policy response mechanism according to the attack path analysis result, performing response measures such as blocking, alarming, and minimum permission suggestion on the attack path, and visualizing the attack path graph.

2. The Kubernetes container escape oriented attack path identification and response method of claim 1, wherein, The real-time monitoring of container behavior in the Kubernetes cluster through the security monitoring tool comprises: Capturing system call events executed by the container through an integrated behavior listener, and marking the call events with escape characteristics when accessing sensitive system paths, writing to host paths, or calling network-level permissions are captured; When detecting abnormal behavior, the Pod UID of the container is passed to an event processor, and the event information is associated with the runtime context of the container.

3. The Kubernetes container escape oriented attack path identification and response method of claim 1, wherein, When a container escape behavior is detected, identifying and marking a permission jump pod, starting a path search algorithm, and deriving an attack path in the permission relationship graph comprises: Analyzing the RBAC permission structure of the service account bound to each pod, the pod deployment information, and the node scheduling information, and calculating the comprehensive risk score of the pod; According to the comprehensive risk score, the pod that promotes the attacker's permissions is identified and marked as a permission jump pod; and Based on the graph path search algorithm, the permission jump path from the attack source Pod to the cluster-admin permission is found.

4. The Kubernetes container escape oriented attack path identification and response method of claim 3, wherein, The analysis of the RBAC permission structure of the service account bound to each pod, the pod deployment information, and the node scheduling information, and the calculation of the comprehensive risk score of the pod comprises: Calculating the RBAC permission score based on the permission level of the service account bound to each Pod; Distinguishing the deployment type of the Pod into system components and application components, and calculating the Pod deployment score according to the deployment type of the Pod; Analyzing whether the node where the Pod is deployed is the same node as the attacker container that triggered the container escape event, and calculating the node reachability score according to the node scheduling reachability; Calculating the behavior proximity score according to the proximity of the Pod to sensitive resources; and Weighting the RBAC permission score, the Pod deployment score, the node reachability score, and the behavior proximity score according to different weights to finally obtain the comprehensive risk score of each Pod.

5. The Kubernetes container escape oriented attack path identification and response method of claim 4, wherein, The graph-based path search algorithm finds a permission jump path from the attack source Pod to the cluster-admin permission, including: Based on the shortest path algorithm, find the shortest path from the attack source Pod to the node with cluster-admin permission through the intermediate jump node; and Based on the risk score of the path and the attack possibility of the node, select the top K most threatening attack chains.

6. The Kubernetes container escape oriented attack path identification and response method of claim 1, wherein, When the attack path is established, trigger the policy response mechanism, perform response measures such as blocking, alarm and minimum permission suggestion on the attack path, and visualize the attack path atlas, including: Assess the risk score of the attack path. When the risk score exceeds the preset risk threshold, push the alarm information, suspend the running of the related Pod, modify the permission of the related resource, and reject the access request without audit; After the permission modification operation is completed, generate a minimum permission suggestion YAML file according to the adjusted permission state, and the YAML file content includes the modified service account binding, the downgraded role permission, and the minimum permission recommendation configuration; Push the YAML file to the management library through the GitOps management tool, and synchronize the permission change to the Kubernetes cluster through the automated deployment tool; and In the front-end page, use the JavaScript graphics library to load the attack path atlas in SVG format or generate the attack path atlas by parsing the JSON format data.

7. The Kubernetes container escape oriented attack path identification and response method of claim 6, wherein, The node information of the attack path atlas includes the permission level of the resource and the jump board danger coefficient, and the information of each edge includes the operation type and the resource type affected by the operation.

8. An apparatus for Kubernetes container escape attack path identification and response, characterized in that, The apparatus comprises: at least one processor; and at least one memory having computer program stored therein; wherein when the computer program is executed by the at least one processor, the apparatus performs the steps of the attack path identification and response method for Kubernetes container escape according to any one of claims 1 to 7.

9. A computer readable storage medium storing a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the attack path identification and response method for Kubernetes container escape according to any one of claims 1 to 7.

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