attack-tree.md

Attack Tree Analysis for keras-team/keras

Objective: Compromise Application Using Keras

Attack Tree Visualization

Compromise Application Using Keras [CRITICAL NODE]
├── 1. Exploit Malicious Model Loading [CRITICAL NODE] [HIGH-RISK PATH]
│   ├── 1.1.2. Compromise Model Download/Storage Infrastructure [HIGH-RISK PATH]
│   ├── 1.2. Man-in-the-Middle Attack during Model Download [HIGH-RISK PATH]
│   ├── 1.3. Local Model File Manipulation (If applicable) [HIGH-RISK PATH]
│   └── 1.4. Model Deserialization Vulnerabilities [HIGH-RISK PATH]
│       ├── 1.4.1. Exploit Known Deserialization Flaws in Keras/Dependencies (e.g., TensorFlow) [HIGH-RISK PATH]
│       ├── 1.4.2. Craft Malicious Model File to Trigger Deserialization Vulnerability [HIGH-RISK PATH]
│       └── 1.4.3. Exploit Custom Model Loading Logic (If implemented) [HIGH-RISK PATH]
├── 2. Exploit Model Input Processing
│   ├── 2.1. Adversarial Examples to Cause Misclassification/Unexpected Behavior
│   │   ├── 2.1.2. Trigger Denial of Service by Overloading Model/Application [HIGH-RISK PATH]
│   ├── 2.2. Input Injection Attacks via Model Input
│   │   ├── 2.2.1. Exploit Vulnerabilities in Input Preprocessing Layers (Custom Layers) [HIGH-RISK PATH]
│   │   ├── 2.2.2. Exploit Vulnerabilities in Data Loading/Preprocessing Pipelines [HIGH-RISK PATH]
│   │   └── 2.3. Resource Exhaustion via Malicious Input [HIGH-RISK PATH]
├── 3. Exploit Model Output Processing
│   ├── 3.1. Vulnerabilities in Handling Model Output Data
│   │   ├── 3.1.1. Buffer Overflows/Memory Corruption in Output Processing Logic [HIGH-RISK PATH]
├── 4. Exploit Vulnerabilities in Keras Library Itself (Less likely in application context, but possible) [HIGH-RISK PATH]
│   ├── 4.1. Known Vulnerabilities in Keras Core Code [HIGH-RISK PATH]
└── 5. Indirect Attacks via Dependencies (TensorFlow, etc.) [HIGH-RISK PATH]
    ├── 5.1. Exploit Vulnerabilities in TensorFlow or other Backend [HIGH-RISK PATH]

Attack Tree Path: Compromise Application Using Keras

• Compromise Application Using Keras:
  • This is the ultimate goal of the attacker. Success means gaining unauthorized access, control, or causing harm to the application.
  • It is a critical node because all high-risk paths converge here, representing the overall objective.

Attack Tree Path: Exploit Malicious Model Loading

• Exploit Malicious Model Loading:
  • This is a critical step because loading a malicious model can directly lead to application compromise.
  • If successful, the attacker can execute arbitrary code within the application's context, manipulate data, or cause denial of service.
  • It is a critical node as it is a central point for multiple high-risk attack vectors related to model integrity.

Attack Tree Path: 1. Exploit Malicious Model Loading

• 1. Exploit Malicious Model Loading:
  • Attack Vectors:
    • 1.1.2. Compromise Model Download/Storage Infrastructure:
      • Attacker compromises the infrastructure used to store or download models (e.g., servers, cloud storage).
      • They replace legitimate models with malicious ones.
      • Impact: Remote Code Execution, Data Manipulation, Denial of Service.
    • 1.2. Man-in-the-Middle Attack during Model Download:
      • Attacker intercepts model download traffic (if not using HTTPS).
      • They replace the legitimate model with a malicious one during transit.
      • Impact: Remote Code Execution, Data Manipulation, Denial of Service.
    • 1.3. Local Model File Manipulation (If applicable):
      • Attacker gains local access to the application's file system.
      • They replace model files stored locally with malicious versions.
      • Impact: Remote Code Execution, Data Manipulation, Denial of Service.
    • 1.4. Model Deserialization Vulnerabilities:
      • Exploiting vulnerabilities in the model loading process (deserialization) within Keras or its dependencies.
      • Attack Vectors within Deserialization:
        • 1.4.1. Exploit Known Deserialization Flaws in Keras/Dependencies (e.g., TensorFlow): Utilizing publicly known vulnerabilities in deserialization libraries used by Keras/TensorFlow.
          • Impact: Remote Code Execution.
        • 1.4.2. Craft Malicious Model File to Trigger Deserialization Vulnerability: Creating a specially crafted model file designed to exploit a deserialization flaw.
          • Impact: Remote Code Execution.
        • 1.4.3. Exploit Custom Model Loading Logic (If implemented): Exploiting vulnerabilities in custom code written for loading models, especially if it involves insecure deserialization practices.
          • Impact: Remote Code Execution.

Attack Tree Path: 2. Exploit Model Input Processing

• 2. Exploit Model Input Processing:
  • 2.1. Adversarial Examples to Cause Misclassification/Unexpected Behavior:
    • 2.1.2. Trigger Denial of Service by Overloading Model/Application:
      • Crafting adversarial inputs that are computationally expensive for the model to process.
      • Overwhelming the application's resources and causing denial of service.
      • Impact: Denial of Service.
  • 2.2. Input Injection Attacks via Model Input:
    • 2.2.1. Exploit Vulnerabilities in Input Preprocessing Layers (Custom Layers):
      • Exploiting vulnerabilities (e.g., injection flaws, buffer overflows) in custom Keras layers used for input preprocessing.
      • Impact: Remote Code Execution, Data Manipulation, Denial of Service.
    • 2.2.2. Exploit Vulnerabilities in Data Loading/Preprocessing Pipelines:
      • Exploiting vulnerabilities in the code responsible for loading and preprocessing input data before it reaches the model.
      • Impact: Data Corruption, Application Errors, Potential Injection.
    • 2.3. Resource Exhaustion via Malicious Input:
      • Sending a large volume of requests or extremely large input data to exhaust application resources during model inference.
      • Impact: Denial of Service.

Attack Tree Path: 3. Exploit Model Output Processing

• 3. Exploit Model Output Processing:
  • 3.1. Vulnerabilities in Handling Model Output Data:
    • 3.1.1. Buffer Overflows/Memory Corruption in Output Processing Logic:
      • Exploiting buffer overflows or memory corruption vulnerabilities in the code that processes the model's output.
      • Impact: Remote Code Execution, Denial of Service.

Attack Tree Path: 4. Exploit Vulnerabilities in Keras Library Itself (Less likely in application context, but possible)

• 4. Exploit Vulnerabilities in Keras Library Itself (Less likely in application context, but possible):
  • 4.1. Known Vulnerabilities in Keras Core Code:
    • Exploiting publicly known vulnerabilities in the core Keras library code.
    • Impact: Remote Code Execution, various application compromises depending on the vulnerability.

Attack Tree Path: 5. Indirect Attacks via Dependencies (TensorFlow, etc.)

• 5. Indirect Attacks via Dependencies (TensorFlow, etc.):
  • 5.1. Exploit Vulnerabilities in TensorFlow or other Backend:
    • Exploiting publicly known vulnerabilities in TensorFlow or other backend libraries used by Keras.
    • Impact: Remote Code Execution, various application compromises depending on the vulnerability in the dependency.