attack-tree.md

Attack Tree Analysis for pongasoft/glu

Objective: Compromise Application via glu (RCE or Disruption)

Attack Tree Visualization

Goal: Compromise Application via glu (RCE or Disruption) ├── 1. Achieve Remote Code Execution (RCE) on Agent [HIGH-RISK] │ ├── 1.1 Exploit Agent Vulnerabilities [HIGH-RISK] │ │ ├── 1.1.1.2 Exploit authentication/authorization bypass in IPC. [CRITICAL] │ │ ├── 1.1.1.3 Leverage deserialization vulnerabilities in message handling. [HIGH-RISK] │ │ ├── 1.1.2.1 Inject malicious Groovy code via model parameters or fabric definitions. [HIGH-RISK][CRITICAL] │ │ ├── 1.1.3.1 Supply a malicious artifact (e.g., a compromised JAR file) that exploits vulnerabilities in the application or its dependencies. [HIGH-RISK] │ │ ├── 1.1.3.3 Man-in-the-Middle (MITM) attack on artifact download (if not using secure transport/verification). [CRITICAL] │ │ └── 1.1.4.1 Inject malicious configuration settings that lead to RCE (e.g., specifying a malicious command to execute). [CRITICAL] │ └── 1.2.2 Gain access through weak credentials or SSH keys. [CRITICAL] ├── 2. Disrupt Deployment Process │ ├── 2.2 Deploy Malicious Artifacts [HIGH-RISK] │ │ ├── 2.2.2 Man-in-the-Middle (MITM) attack on artifact download (if integrity checks are weak or absent). [CRITICAL] │ │ └── 2.2.3 Tamper with the glu model or fabric to point to a malicious artifact. [HIGH-RISK] └── 3. Compromise glu Console (If Applicable) ├── 3.1.3 Authentication/Authorization bypass to gain access to the console. [CRITICAL] └── 3.2.2 Gain access through weak credentials or SSH keys. [CRITICAL]

Attack Tree Path: 1. Achieve Remote Code Execution (RCE) on Agent [HIGH-RISK]

• 1.1 Exploit Agent Vulnerabilities [HIGH-RISK]

  • 1.1.1.2 Exploit authentication/authorization bypass in IPC. [CRITICAL]

    • Description: The attacker attempts to bypass the authentication and authorization mechanisms used for inter-process communication (IPC) between the glu agent and other components (e.g., ZooKeeper, other agents, the console). This could involve exploiting flaws in the authentication protocol, finding default credentials, or leveraging misconfigurations.
    • Example: If ZooKeeper is used without authentication, the attacker could connect directly to it and modify the deployment state.
    • Mitigation: Implement strong authentication (e.g., mutual TLS) and fine-grained authorization for all IPC channels. Regularly audit configurations.

  • 1.1.1.3 Leverage deserialization vulnerabilities in message handling. [HIGH-RISK]

    • Description: The attacker sends a specially crafted serialized object to the glu agent. If the agent deserializes this object without proper validation, it could lead to arbitrary code execution. This is a common vulnerability in Java and other languages that use object serialization.
    • Example: The attacker uses a known "gadget chain" in a common Java library to execute a system command upon deserialization.
    • Mitigation: Avoid deserializing untrusted data. If deserialization is necessary, use a whitelist of allowed classes and validate the data after deserialization. Consider using safer serialization formats (e.g., JSON with strict schema validation).

  • 1.1.2.1 Inject malicious Groovy code via model parameters or fabric definitions. [HIGH-RISK][CRITICAL]

    • Description: The attacker injects malicious Groovy code into the glu model or fabric definitions. This code is then executed by the glu agent during the deployment process. This is a direct attack on glu's core functionality.
    • Example: The attacker modifies a model parameter to include a Groovy script that executes a shell command.
    • Mitigation: Strictly validate and sanitize all inputs to the model and fabric. Consider using a more restrictive scripting language or eliminating scripting entirely if possible. Implement a strong content security policy.

  • 1.1.3.1 Supply a malicious artifact (e.g., a compromised JAR file) that exploits vulnerabilities in the application or its dependencies. [HIGH-RISK]

    • Description: The attacker replaces a legitimate artifact with a malicious one. This malicious artifact could contain vulnerabilities that are exploited when the application is deployed or run.
    • Example: The attacker replaces a legitimate JAR file with one containing a known vulnerability in a logging library.
    • Mitigation: Use a secure artifact repository with access controls. Verify artifact integrity using checksums and digital signatures. Scan artifacts for vulnerabilities before deployment.

  • 1.1.3.3 Man-in-the-Middle (MITM) attack on artifact download (if not using secure transport/verification). [CRITICAL]

    • Description: The attacker intercepts the communication between the glu agent and the artifact repository. They replace the legitimate artifact with a malicious one during the download process.
    • Example: The attacker uses ARP spoofing to intercept traffic and inject a malicious JAR file.
    • Mitigation: Use HTTPS for all artifact downloads. Verify the server's certificate. Use checksums or digital signatures to verify artifact integrity.

  • 1.1.4.1 Inject malicious configuration settings that lead to RCE (e.g., specifying a malicious command to execute). [CRITICAL]

    • Description: The attacker modifies the glu agent's configuration to include malicious settings. These settings could directly execute commands or indirectly lead to RCE.
    • Example: The attacker changes a configuration parameter to specify a malicious script to be executed as part of a pre-deployment hook.
    • Mitigation: Strictly validate and sanitize all configuration inputs. Store configuration securely. Use a configuration management system with auditing capabilities.

  • 1.2.2 Gain access through weak credentials or SSH keys. [CRITICAL]

    • Description: The attacker gains access to the agent's host system by guessing weak passwords, using default credentials, or stealing SSH keys.
    • Example: The attacker uses a brute-force attack to guess the SSH password for the user running the glu agent.
    • Mitigation: Enforce strong password policies. Use multi-factor authentication. Disable password-based SSH access and use key-based authentication only. Regularly rotate SSH keys.

Attack Tree Path: 2. Disrupt Deployment Process

• 2.2 Deploy Malicious Artifacts [HIGH-RISK]

  • 2.2.2 Man-in-the-Middle (MITM) attack on artifact download (if integrity checks are weak or absent). [CRITICAL]

    • (Same description and mitigation as 1.1.3.3)

  • 2.2.3 Tamper with the glu model or fabric to point to a malicious artifact. [HIGH-RISK]

    • Description: The attacker modifies the glu model or fabric definition to specify a malicious artifact to be deployed. This bypasses the need to compromise the artifact repository directly.
    • Example: The attacker changes the URL of an artifact in the model to point to a malicious file hosted on a compromised server.
    • Mitigation: Implement strict access controls on the glu model and fabric. Use version control and auditing to track changes. Validate all artifact URLs before deployment.

Attack Tree Path: 3. Compromise glu Console (If Applicable)

• 3.1.3 Authentication/Authorization bypass to gain access to the console. [CRITICAL]

  • Description: The attacker bypasses the authentication and authorization mechanisms of the glu console, gaining unauthorized access.
  • Example: The attacker exploits a vulnerability in the console's login form to bypass authentication.
  • Mitigation: Implement strong authentication (e.g., multi-factor authentication) and fine-grained authorization. Regularly test the authentication and authorization mechanisms.

• 3.2.2 Gain access through weak credentials or SSH keys. [CRITICAL]

  • Description: Similar to 1.2.2, but targeting the console's host system.
  • Example: The attacker uses a dictionary attack to guess the password for an administrator account on the console.
  • Mitigation: Enforce strong password policies. Use multi-factor authentication. Disable password-based SSH access and use key-based authentication only. Regularly rotate SSH keys.