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Attack Tree Analysis for apache/hadoop

Objective: Attacker's Goal: To compromise the application using Hadoop by exploiting vulnerabilities within the Hadoop ecosystem itself, focusing on high-risk attack vectors.

Attack Tree Visualization

Compromise Application via Hadoop Exploitation [CRITICAL]
├───(OR)─ Exploit HDFS (Hadoop Distributed File System) [HIGH-RISK]
│   ├───(OR)─ Compromise NameNode [HIGH-RISK]
│   │   ├───(AND)─ DoS NameNode (Availability Impact) [HIGH-RISK]
│   │   │   ├─── Excessive Metadata Requests [HIGH-RISK]
│   │   │   ├─── Heap Exhaustion [HIGH-RISK]
│   │   │   └─── Exploiting Unpatched Vulnerabilities (e.g., known DoS flaws) [CRITICAL]
│   │   ├───(AND)─ Data Tampering/Corruption via NameNode [CRITICAL]
│   │   │   ├─── Exploiting Authentication/Authorization Weaknesses [HIGH-RISK]
│   │   │   │   ├─── Default Credentials (if applicable, though less likely in production Hadoop) [HIGH-RISK]
│   │   │   │   ├─── Weak or Misconfigured Kerberos/Security [HIGH-RISK]
│   │   │   │   └─── Exploiting Authorization Bypass Vulnerabilities [CRITICAL]
│   │   │   └─── Exploiting Software Vulnerabilities in NameNode RPC/Web UI [CRITICAL]
│   │   ├───(AND)─ Information Disclosure via NameNode [HIGH-RISK]
│   │   │   ├─── Unauthorized Access to NameNode Web UI [HIGH-RISK]
│   │   │   └─── Exploiting Vulnerabilities in NameNode Web UI/API [HIGH-RISK]
│   │   └───(AND)─ Data Deletion/Loss via NameNode [CRITICAL]
│   │       ├─── Exploiting Authentication/Authorization Weaknesses [HIGH-RISK]
│   │       └─── Exploiting Software Vulnerabilities in NameNode [CRITICAL]
│   ├───(OR)─ Compromise DataNodes [HIGH-RISK]
│   │   ├───(AND)─ Data Tampering/Corruption via DataNodes [CRITICAL]
│   │   │   ├─── Man-in-the-Middle Attacks on DataNode Communication [HIGH-RISK]
│   │   │   ├─── Exploiting DataNode Vulnerabilities (Software bugs) [CRITICAL]
│   │   │   └─── Physical Access to DataNodes (if applicable) [CRITICAL]
│   │   ├───(AND)─ Data Theft/Disclosure via DataNodes [CRITICAL]
│   │   │   ├─── Unauthorized Access to DataNode Data Directories (if physical access) [CRITICAL]
│   │   │   └─── Exploiting DataNode RPC/HTTP Interfaces [HIGH-RISK]
│   │   └───(AND)─ DataNode Availability Disruption [HIGH-RISK]
│   │       ├─── DoS DataNode Service [HIGH-RISK]
│   │       │   ├─── Network Flooding [HIGH-RISK]
│   │       │   ├─── Resource Exhaustion (CPU, Memory, Disk I/O) [HIGH-RISK]
│   │       │   └─── Exploiting Unpatched Vulnerabilities [CRITICAL]
│   ├───(OR)─ Exploit HDFS Permissions and ACLs [HIGH-RISK]
│   │   ├───(AND)─ Misconfigured Permissions [HIGH-RISK]
│   │   │   ├─── Overly Permissive Default Permissions [HIGH-RISK]
│   │   │   ├─── Incorrectly Applied ACLs [HIGH-RISK]
│   │   │   └─── Privilege Escalation via Permission Exploitation [HIGH-RISK]
│   │   └───(AND)─ Exploiting Vulnerabilities in Permission Checks [CRITICAL]
├───(OR)─ Exploit YARN (Yet Another Resource Negotiator) [HIGH-RISK]
│   ├───(OR)─ Compromise ResourceManager [HIGH-RISK]
│   │   ├───(AND)─ DoS ResourceManager (Availability Impact) [HIGH-RISK]
│   │   │   ├─── Resource Exhaustion (CPU, Memory) [HIGH-RISK]
│   │   │   ├─── Excessive Application Submissions [HIGH-RISK]
│   │   │   └─── Exploiting Unpatched Vulnerabilities [CRITICAL]
│   │   ├───(AND)─ Resource Manipulation/Theft via ResourceManager [CRITICAL]
│   │   │   ├─── Exploiting Authentication/Authorization Weaknesses [HIGH-RISK]
│   │   │   └─── Exploiting Vulnerabilities in Resource Scheduling/Allocation [CRITICAL]
│   │   ├───(AND)─ Information Disclosure via ResourceManager [HIGH-RISK]
│   │   │   ├─── Unauthorized Access to ResourceManager Web UI [HIGH-RISK]
│   │   │   └─── Exploiting Vulnerabilities in ResourceManager Web UI/API [HIGH-RISK]
│   │   └───(AND)─ Control Application Execution via ResourceManager [CRITICAL]
│   │       ├─── Malicious Application Submission [CRITICAL]
│   │       │   ├─── Bypassing Application Submission Checks [CRITICAL]
│   │       │   ├─── Exploiting Vulnerabilities in Application Submission Process [CRITICAL]
│   │       │   └─── Social Engineering to Submit Malicious Application (if applicable) [HIGH-RISK]
│   │       └─── Application Manipulation after Submission [HIGH-RISK]
│   │           ├─── Exploiting Vulnerabilities in ApplicationMaster Communication [CRITICAL]
│   ├───(OR)─ Compromise NodeManagers [HIGH-RISK]
│   │   ├───(AND)─ Code Execution on NodeManagers [CRITICAL]
│   │   │   ├─── Exploiting Containerization Vulnerabilities (e.g., Docker escape if used) [CRITICAL]
│   │   │   ├─── Exploiting NodeManager Vulnerabilities (Software bugs) [CRITICAL]
│   │   │   └─── Privilege Escalation within Containers [HIGH-RISK]
│   │   ├───(AND)─ Resource Exhaustion on NodeManagers [HIGH-RISK]
│   │   │   ├─── Malicious Applications Consuming Excessive Resources [HIGH-RISK]
│   │   │   ├─── DoS NodeManager Service [HIGH-RISK]
│   │   │   └─── Exploiting Unpatched Vulnerabilities [CRITICAL]
│   │   └───(AND)─ Data Exfiltration from NodeManagers [HIGH-RISK]
│   │       ├─── Accessing Container Data Directories [HIGH-RISK]
│   │       └─── Network Exfiltration from Containers [HIGH-RISK]
├───(OR)─ Exploit Core Hadoop Services (e.g., ZooKeeper if used for coordination) [HIGH-RISK]
│   ├───(AND)─ Compromise ZooKeeper (if used) [HIGH-RISK]
│   │   ├───(AND)─ DoS ZooKeeper [HIGH-RISK]
│   │   │   ├─── Connection Flooding [HIGH-RISK]
│   │   │   ├─── Request Flooding [HIGH-RISK]
│   │   │   └─── Exploiting Unpatched Vulnerabilities [CRITICAL]
│   │   ├───(AND)─ Data Tampering/Corruption in ZooKeeper [CRITICAL]
│   │   │   ├─── Exploiting Authentication/Authorization Weaknesses (ZooKeeper ACLs) [HIGH-RISK]
│   │   │   └─── Exploiting ZooKeeper Vulnerabilities [CRITICAL]
│   │   ├───(AND)─ Information Disclosure from ZooKeeper [HIGH-RISK]
│   │   │   ├─── Unauthorized Access to ZooKeeper Data [HIGH-RISK]
│   │   │   └─── Exploiting ZooKeeper Vulnerabilities [HIGH-RISK]
│   │   └───(AND)─ Availability Disruption of ZooKeeper [HIGH-RISK]
│   │       ├─── DoS ZooKeeper [HIGH-RISK]
├───(OR)─ Exploit Client Interaction with Hadoop [HIGH-RISK]
│   ├───(AND)─ Compromise Client Applications/Tools [HIGH-RISK]
│   │   ├───(AND)─ Vulnerabilities in Client Code (e.g., application code interacting with Hadoop APIs) [HIGH-RISK]
│   │   │   ├─── Injection Vulnerabilities (e.g., command injection via user input passed to Hadoop commands) [HIGH-RISK]
│   │   │   ├─── Deserialization Vulnerabilities (if client uses Java serialization with Hadoop RPC) [CRITICAL]
│   │   │   └─── Logic Flaws in Client Application [HIGH-RISK]
│   │   ├───(AND)─ Man-in-the-Middle Attacks on Client-Hadoop Communication [HIGH-RISK]
│   │   │   ├─── Unencrypted Communication Channels [HIGH-RISK]
│   │   └───(AND)─ Social Engineering Attacks Targeting Hadoop Users [HIGH-RISK]
│   │       ├─── Phishing for Hadoop Credentials [HIGH-RISK]
└───(OR)─ Supply Chain Attacks on Hadoop Dependencies [CRITICAL]
    └───(AND)─ Exploiting Vulnerabilities in Hadoop Dependencies (e.g., Log4j, etc.) [CRITICAL]
        └─── Outdated or Vulnerable Dependencies [CRITICAL]

Attack Tree Path: 1. Compromise Application via Hadoop Exploitation [CRITICAL]

  • Description: This is the root goal. Any successful exploitation of Hadoop components leading to application compromise falls under this category.
    • Impact: Critical - Full compromise of the application, data breach, service disruption, loss of control.
    • Mitigation: Implement comprehensive security measures across all Hadoop components and client interactions as detailed below.

Attack Tree Path: 2. Exploit HDFS (Hadoop Distributed File System) [HIGH-RISK]

  • Description: Targeting the core data storage layer of Hadoop. Successful exploitation can lead to data breaches, corruption, or denial of service.

    • Impact: High to Critical - Data loss, data corruption, data theft, service disruption.

    • Mitigation: Secure NameNode and DataNodes, implement strong authentication and authorization, encrypt data in transit and at rest, regularly patch HDFS components.

    • 2.1. Compromise NameNode [HIGH-RISK]:

      • Description: Targeting the central metadata manager of HDFS.

      • Impact: High to Critical - Cluster-wide availability disruption, data corruption, data loss, information disclosure.

      • Mitigation: Harden NameNode security, restrict access, implement DoS protection, regularly patch, strong authentication and authorization.

        • 2.1.1. DoS NameNode (Availability Impact) [HIGH-RISK]:

          • Description: Overwhelming the NameNode with requests to make it unavailable.
          • Impact: Medium to High - Service disruption, cluster unavailability.
          • Mitigation: Rate limiting, request prioritization, resource monitoring, appropriate heap sizing, patching.
          • Attack Vectors:
            • Excessive Metadata Requests [HIGH-RISK]: Flooding NameNode with metadata requests.
            • Heap Exhaustion [HIGH-RISK]: Causing NameNode to run out of memory.
            • Exploiting Unpatched Vulnerabilities (e.g., known DoS flaws) [CRITICAL]: Using known vulnerabilities to crash or overload NameNode.

        • 2.1.2. Data Tampering/Corruption via NameNode [CRITICAL]:

          • Description: Modifying or corrupting metadata managed by NameNode, leading to data corruption or loss.
          • Impact: Critical - Data corruption, data loss, loss of data integrity.
          • Mitigation: Strong authentication and authorization, regular patching, input validation, secure configuration.
          • Attack Vectors:
            • Exploiting Authentication/Authorization Weaknesses [HIGH-RISK]: Bypassing or weakening authentication to gain unauthorized access.
              • Default Credentials (if applicable) [HIGH-RISK]: Using default credentials to access NameNode.
              • Weak or Misconfigured Kerberos/Security [HIGH-RISK]: Exploiting weaknesses in Kerberos or other security configurations.
              • Exploiting Authorization Bypass Vulnerabilities [CRITICAL]: Exploiting software bugs to bypass authorization checks.
            • Exploiting Software Vulnerabilities in NameNode RPC/Web UI [CRITICAL]: Using vulnerabilities in NameNode interfaces to tamper with data.

        • 2.1.3. Information Disclosure via NameNode [HIGH-RISK]:

          • Description: Gaining unauthorized access to metadata managed by NameNode, revealing sensitive information.
          • Impact: Medium to High - Information leakage, potential privacy violations.
          • Mitigation: Restrict access to NameNode UI/API, regularly patch, secure logging, sanitize error messages.
          • Attack Vectors:
            • Unauthorized Access to NameNode Web UI [HIGH-RISK]: Accessing the web UI without proper authentication.
            • Exploiting Vulnerabilities in NameNode Web UI/API [HIGH-RISK]: Using vulnerabilities in the UI/API to extract information.

        • 2.1.4. Data Deletion/Loss via NameNode [CRITICAL]:

          • Description: Deleting or causing loss of data by manipulating NameNode metadata.
          • Impact: Critical - Permanent data loss, service disruption.
          • Mitigation: Strong authentication and authorization, regular patching, robust backup and recovery mechanisms.
          • Attack Vectors:
            • Exploiting Authentication/Authorization Weaknesses [HIGH-RISK]: Gaining unauthorized access to delete data.
            • Exploiting Software Vulnerabilities in NameNode [CRITICAL]: Using vulnerabilities to delete or corrupt data.

    • 2.2. Compromise DataNodes [HIGH-RISK]:

      • Description: Targeting the data storage servers in HDFS.

      • Impact: High to Critical - Data corruption, data theft, data loss, service disruption.

      • Mitigation: Secure DataNode servers, encrypt data in transit and at rest, regularly patch, physical security, network segmentation.

        • 2.2.1. Data Tampering/Corruption via DataNodes [CRITICAL]:

          • Description: Directly modifying or corrupting data stored on DataNodes.
          • Impact: Critical - Data corruption, loss of data integrity.
          • Mitigation: Encryption in transit, regular patching, physical security, data integrity checks.
          • Attack Vectors:
            • Man-in-the-Middle Attacks on DataNode Communication [HIGH-RISK]: Intercepting and modifying data during communication between DataNodes or between NameNode and DataNodes.
            • Exploiting DataNode Vulnerabilities (Software bugs) [CRITICAL]: Using vulnerabilities in DataNode software to tamper with data.
            • Physical Access to DataNodes (if applicable) [CRITICAL]: Gaining physical access to DataNode servers to directly manipulate data.

        • 2.2.2. Data Theft/Disclosure via DataNodes [CRITICAL]:

          • Description: Stealing or disclosing data stored on DataNodes.
          • Impact: Critical - Data breach, privacy violations, compliance issues.
          • Mitigation: Encryption at rest, strong access controls, physical security, secure DataNode interfaces.
          • Attack Vectors:
            • Unauthorized Access to DataNode Data Directories (if physical access) [CRITICAL]: Gaining physical access and directly accessing data directories.
            • Exploiting DataNode RPC/HTTP Interfaces [HIGH-RISK]: Using DataNode interfaces to extract data without authorization.

        • 2.2.3. DataNode Availability Disruption [HIGH-RISK]:

          • Description: Making DataNodes unavailable, leading to data unavailability and potential data loss.
          • Impact: Medium to High - Service disruption, data unavailability, potential data loss if redundancy is insufficient.
          • Mitigation: Network security, resource monitoring, patching, redundancy, DoS protection.
          • Attack Vectors:
            • DoS DataNode Service [HIGH-RISK]: Overwhelming DataNode services to make them unavailable.
              • Network Flooding [HIGH-RISK]: Flooding DataNode network interfaces.
              • Resource Exhaustion (CPU, Memory, Disk I/O) [HIGH-RISK]: Consuming DataNode resources to cause failure.
              • Exploiting Unpatched Vulnerabilities [CRITICAL]: Using vulnerabilities to crash or overload DataNodes.

    • 2.3. Exploit HDFS Permissions and ACLs [HIGH-RISK]:

      • Description: Exploiting misconfigurations or vulnerabilities in HDFS permission and ACL mechanisms to gain unauthorized access or escalate privileges.
      • Impact: High - Unauthorized data access, data manipulation, privilege escalation.
      • Mitigation: Proper configuration of permissions and ACLs, regular audits, least privilege principle, patching.
      • Attack Vectors:
        • Misconfigured Permissions [HIGH-RISK]: Incorrectly set permissions allowing unauthorized access.
          • Overly Permissive Default Permissions [HIGH-RISK]: Default permissions granting excessive access.
          • Incorrectly Applied ACLs [HIGH-RISK]: ACLs not properly configured or applied.
          • Privilege Escalation via Permission Exploitation [HIGH-RISK]: Exploiting permission misconfigurations to gain higher privileges.
        • Exploiting Vulnerabilities in Permission Checks [CRITICAL]: Using software bugs to bypass permission checks.

Attack Tree Path: 3. Exploit YARN (Yet Another Resource Negotiator) [HIGH-RISK]

  • Description: Targeting the resource management and job scheduling layer of Hadoop.

    • Impact: High to Critical - Service disruption, resource theft, control over application execution, information disclosure.

    • Mitigation: Secure ResourceManager and NodeManagers, strong authentication and authorization, resource quotas, regular patching.

    • 3.1. Compromise ResourceManager [HIGH-RISK]:

      • Description: Targeting the central resource manager in YARN.

      • Impact: High to Critical - Cluster-wide availability disruption, resource theft, control over application execution, information disclosure.

      • Mitigation: Harden ResourceManager security, restrict access, implement DoS protection, regularly patch, strong authentication and authorization.

        • 3.1.1. DoS ResourceManager (Availability Impact) [HIGH-RISK]:

          • Description: Overwhelming the ResourceManager to make it unavailable.
          • Impact: High - Service disruption, cluster unavailability, inability to run applications.
          • Mitigation: Resource monitoring, rate limiting, resource quotas, patching.
          • Attack Vectors:
            • Resource Exhaustion (CPU, Memory) [HIGH-RISK]: Causing ResourceManager to run out of resources.
            • Excessive Application Submissions [HIGH-RISK]: Flooding ResourceManager with application submission requests.
            • Exploiting Unpatched Vulnerabilities [CRITICAL]: Using vulnerabilities to crash or overload ResourceManager.

        • 3.1.2. Resource Manipulation/Theft via ResourceManager [CRITICAL]:

          • Description: Manipulating resource allocation or stealing resources managed by ResourceManager.
          • Impact: High - Resource theft, denial of resources to legitimate applications, performance degradation.
          • Mitigation: Strong authentication and authorization, robust resource scheduling algorithms, resource quotas, patching.
          • Attack Vectors:
            • Exploiting Authentication/Authorization Weaknesses [HIGH-RISK]: Gaining unauthorized access to manipulate resource allocation.
            • Exploiting Vulnerabilities in Resource Scheduling/Allocation [CRITICAL]: Using vulnerabilities to manipulate resource allocation logic.

        • 3.1.3. Information Disclosure via ResourceManager [HIGH-RISK]:

          • Description: Gaining unauthorized access to information managed by ResourceManager, such as application metadata or resource allocation details.
          • Impact: Medium to High - Information leakage, potential privacy violations, insight into cluster operations.
          • Mitigation: Restrict access to ResourceManager UI/API, secure logging, sanitize error messages.
          • Attack Vectors:
            • Unauthorized Access to ResourceManager Web UI [HIGH-RISK]: Accessing the web UI without proper authentication.
            • Exploiting Vulnerabilities in ResourceManager Web UI/API [HIGH-RISK]: Using vulnerabilities in the UI/API to extract information.

        • 3.1.4. Control Application Execution via ResourceManager [CRITICAL]:

          • Description: Gaining control over application execution by manipulating ResourceManager.
          • Impact: Critical - Running malicious applications, manipulating application behavior, service disruption.
          • Mitigation: Strong authentication and authorization for application submission, input validation, secure application submission process, patching.
          • Attack Vectors:
            • Malicious Application Submission [CRITICAL]: Submitting malicious applications to be executed on the cluster.
              • Bypassing Application Submission Checks [CRITICAL]: Circumventing security checks during application submission.
              • Exploiting Vulnerabilities in Application Submission Process [CRITICAL]: Using vulnerabilities in the submission process to inject malicious applications.
              • Social Engineering to Submit Malicious Application (if applicable) [HIGH-RISK]: Tricking authorized users into submitting malicious applications.
            • Application Manipulation after Submission [HIGH-RISK]: Manipulating running applications after they have been submitted.
              • Exploiting Vulnerabilities in ApplicationMaster Communication [CRITICAL]: Intercepting or manipulating communication between ResourceManager and ApplicationMasters.

    • 3.2. Compromise NodeManagers [HIGH-RISK]:

      • Description: Targeting the worker nodes in YARN that execute application containers.

      • Impact: High to Critical - Code execution on worker nodes, resource exhaustion, data exfiltration, service disruption.

      • Mitigation: Secure NodeManager servers, container security, resource quotas, regular patching, network segmentation.

        • 3.2.1. Code Execution on NodeManagers [CRITICAL]:

          • Description: Achieving code execution on NodeManager servers.
          • Impact: Critical - Full control over NodeManager, potential lateral movement, data access, service disruption.
          • Mitigation: Container security, regular patching, intrusion detection, least privilege within containers.
          • Attack Vectors:
            • Exploiting Containerization Vulnerabilities (e.g., Docker escape if used) [CRITICAL]: Escaping container environments to gain access to the host NodeManager.
            • Exploiting NodeManager Vulnerabilities (Software bugs) [CRITICAL]: Using vulnerabilities in NodeManager software to execute code.
            • Privilege Escalation within Containers [HIGH-RISK]: Escalating privileges within a container to gain control over the NodeManager.

        • 3.2.2. Resource Exhaustion on NodeManagers [HIGH-RISK]:

          • Description: Exhausting resources on NodeManagers, impacting application performance and stability.
          • Impact: Medium to High - Service disruption, application performance degradation.
          • Mitigation: Resource quotas, fair scheduling, monitoring, DoS protection.
          • Attack Vectors:
            • Malicious Applications Consuming Excessive Resources [HIGH-RISK]: Submitting applications designed to consume excessive resources.
            • DoS NodeManager Service [HIGH-RISK]: Overwhelming NodeManager services to cause resource exhaustion.
            • Exploiting Unpatched Vulnerabilities [CRITICAL]: Using vulnerabilities to cause resource exhaustion.

        • 3.2.3. Data Exfiltration from NodeManagers [HIGH-RISK]:

          • Description: Stealing data from NodeManagers, potentially including application data or sensitive information.
          • Impact: High - Data breach, information leakage.
          • Mitigation: Container isolation, access controls, network policies, secure logging.
          • Attack Vectors:
            • Accessing Container Data Directories [HIGH-RISK]: Accessing data directories of containers running on NodeManagers.
            • Network Exfiltration from Containers [HIGH-RISK]: Exfiltrating data from containers over the network.

Attack Tree Path: 4. Exploit Core Hadoop Services (e.g., ZooKeeper if used for coordination) [HIGH-RISK]

  • Description: Targeting core services like ZooKeeper, which are used for coordination and configuration management in Hadoop.

    • Impact: High to Critical - Cluster instability, service disruption, data corruption, information disclosure.

    • Mitigation: Secure ZooKeeper servers, strong authentication and authorization, regular patching, network segmentation.

    • 4.1. Compromise ZooKeeper (if used) [HIGH-RISK]:

      • Description: Targeting the ZooKeeper service.

      • Impact: High to Critical - Cluster instability, service disruption, data corruption, information disclosure.

      • Mitigation: Harden ZooKeeper security, restrict access, implement DoS protection, regularly patch, strong authentication and authorization (ACLs).

        • 4.1.1. DoS ZooKeeper [HIGH-RISK]:

          • Description: Overwhelming ZooKeeper to make it unavailable.
          • Impact: Medium to High - Service disruption, cluster instability.
          • Mitigation: Connection limits, rate limiting, network firewalls, patching.
          • Attack Vectors:
            • Connection Flooding [HIGH-RISK]: Flooding ZooKeeper with connection requests.
            • Request Flooding [HIGH-RISK]: Flooding ZooKeeper with data requests.
            • Exploiting Unpatched Vulnerabilities [CRITICAL]: Using vulnerabilities to crash or overload ZooKeeper.

        • 4.1.2. Data Tampering/Corruption in ZooKeeper [CRITICAL]:

          • Description: Modifying or corrupting data stored in ZooKeeper, leading to cluster misconfiguration or instability.
          • Impact: Critical - Cluster instability, service disruption, data corruption.
          • Mitigation: Strong authentication and authorization (ACLs), regular patching, secure configuration.
          • Attack Vectors:
            • Exploiting Authentication/Authorization Weaknesses (ZooKeeper ACLs) [HIGH-RISK]: Bypassing or weakening ZooKeeper ACLs to gain unauthorized access.
            • Exploiting ZooKeeper Vulnerabilities [CRITICAL]: Using vulnerabilities in ZooKeeper software to tamper with data.

        • 4.1.3. Information Disclosure from ZooKeeper [HIGH-RISK]:

          • Description: Gaining unauthorized access to data stored in ZooKeeper, revealing configuration or sensitive information.
          • Impact: Medium to High - Information leakage, potential privacy violations, insight into cluster configuration.
          • Mitigation: Restrict access to ZooKeeper ports, strong authentication and authorization (ACLs), patching.
          • Attack Vectors:
            • Unauthorized Access to ZooKeeper Data [HIGH-RISK]: Accessing ZooKeeper data without proper authorization.
            • Exploiting ZooKeeper Vulnerabilities [HIGH-RISK]: Using vulnerabilities to extract information from ZooKeeper.

        • 4.1.4. Availability Disruption of ZooKeeper [HIGH-RISK]:

          • Description: Causing ZooKeeper to become unavailable, leading to cluster instability.
          • Impact: Medium to High - Service disruption, cluster instability.
          • Mitigation: Redundancy, monitoring, DoS protection, data integrity checks.
          • Attack Vectors:
            • DoS ZooKeeper [HIGH-RISK]: (As described above)
            • Data Corruption Leading to ZooKeeper Instability: Corrupting data in ZooKeeper to cause instability.

Attack Tree Path: 5. Exploit Client Interaction with Hadoop [HIGH-RISK]

  • Description: Targeting client applications and tools that interact with Hadoop.

    • Impact: High to Critical - Data breach, code execution on client systems, social engineering attacks.

    • Mitigation: Secure client applications, input validation, secure communication channels, user awareness training.

    • 5.1. Compromise Client Applications/Tools [HIGH-RISK]:

      • Description: Exploiting vulnerabilities in client-side code or tools used to interact with Hadoop.

      • Impact: High to Critical - Code execution on client systems, data breach, control over client operations.

      • Mitigation: Secure coding practices, input validation, regular patching of client libraries, security testing.

        • 5.1.1. Vulnerabilities in Client Code (e.g., application code interacting with Hadoop APIs) [HIGH-RISK]:

          • Description: Exploiting vulnerabilities in custom application code that interacts with Hadoop.
          • Impact: High to Critical - Code execution on client systems, data breach, application compromise.
          • Mitigation: Secure coding practices, input validation, security testing, code reviews.
          • Attack Vectors:
            • Injection Vulnerabilities (e.g., command injection via user input passed to Hadoop commands) [HIGH-RISK]: Injecting malicious commands through user input passed to Hadoop commands.
            • Deserialization Vulnerabilities (if client uses Java serialization with Hadoop RPC) [CRITICAL]: Exploiting deserialization vulnerabilities in Java serialization used for Hadoop RPC.
            • Logic Flaws in Client Application [HIGH-RISK]: Exploiting logical errors in client application code.

        • 5.1.2. Man-in-the-Middle Attacks on Client-Hadoop Communication [HIGH-RISK]:

          • Description: Intercepting and potentially manipulating communication between client applications and Hadoop services.
          • Impact: High - Data theft, data manipulation, information disclosure.
          • Mitigation: Encryption for all communication channels (RPC, HTTP), strong client-side authentication.
          • Attack Vectors:
            • Unencrypted Communication Channels [HIGH-RISK]: Communication channels not encrypted, allowing interception.

    • 5.2. Social Engineering Attacks Targeting Hadoop Users [HIGH-RISK]:

      • Description: Tricking Hadoop users into performing actions that compromise security.
      • Impact: High - Credential theft, malicious application submission, unauthorized access.
      • Mitigation: User awareness training, multi-factor authentication, command whitelisting/validation.
      • Attack Vectors:
        • Phishing for Hadoop Credentials [HIGH-RISK]: Tricking users into revealing their Hadoop credentials.
        • Tricking Users into Running Malicious Hadoop Commands [HIGH-RISK]: Tricking users into executing malicious Hadoop commands.

Attack Tree Path: 6. Supply Chain Attacks on Hadoop Dependencies [CRITICAL]

  • Description: Exploiting vulnerabilities in third-party libraries and dependencies used by Hadoop.

    • Impact: Critical - Widespread impact, potential compromise of entire Hadoop cluster, difficult to detect and mitigate.

    • Mitigation: Dependency scanning, regular updates of dependencies, monitoring security advisories for dependencies.

    • 6.1. Exploiting Vulnerabilities in Hadoop Dependencies (e.g., Log4j, etc.) [CRITICAL]:

      • Description: Using known vulnerabilities in Hadoop dependencies to compromise the system.
      • Impact: Critical - Remote code execution, data breach, service disruption.
      • Mitigation: Regular updates of dependencies, dependency scanning, vulnerability monitoring.
      • Attack Vectors:
        • Outdated or Vulnerable Dependencies [CRITICAL]: Using outdated versions of dependencies with known vulnerabilities.