attack-surface.md

Attack Surface Analysis for ultralytics/yolov5

Attack Surface: Adversarial Input Attacks (Evasion)

*   **Description:**  Attackers craft subtle, often imperceptible changes to input images or videos that cause the YOLOv5 model to misclassify objects, fail to detect them, or detect non-existent objects. This is the core vulnerability of the model itself.
*   **How YOLOv5 Contributes:** YOLOv5, like all deep learning models, is inherently susceptible to adversarial examples due to its complex, high-dimensional decision boundaries. The model's architecture and training process directly influence its vulnerability.
*   **Example:** A small, carefully crafted sticker placed on a stop sign could cause YOLOv5 to misclassify it as a speed limit sign. Or, a digitally altered image of a person could make them "invisible" to a surveillance system.
*   **Impact:**  Can range from minor misclassifications to complete failure of the system's intended function. In security-critical applications, the impact can be severe.
*   **Risk Severity:** **Critical**
*   **Mitigation Strategies:**
    *   **Adversarial Training:** Retrain the YOLOv5 model with a dataset that includes adversarial examples. This is the most effective, but computationally expensive, mitigation. Use the YOLOv5 training scripts, but carefully design the adversarial example generation process.
    *   **Input Preprocessing/Filtering:** Implement input validation and filtering techniques *before* the image reaches the YOLOv5 model. This might involve detecting unusual pixel patterns or using separate anomaly detection models. This is *outside* the YOLOv5 codebase, but still a crucial defense *against* attacks on YOLOv5.
    *   **Ensemble Methods:** Use multiple YOLOv5 models (or different object detection models) and combine their predictions. This increases robustness but adds complexity. Requires custom implementation.
    *   **Defensive Distillation:** A technique to make the model less sensitive to small input perturbations. Research and implement; not directly supported by YOLOv5.

Attack Surface: Model Poisoning Attacks

*   **Description:** Attackers manipulate the training data used to create or fine-tune the YOLOv5 model, injecting malicious examples that cause the model to misbehave in specific ways. This is a supply chain attack *directly* targeting the model's training.
*   **How YOLOv5 Contributes:**  If the application uses a custom-trained YOLOv5 model, the training process itself becomes a potential attack vector. The vulnerability lies in the *data* used to train the YOLOv5 model.
*   **Example:** An attacker could subtly alter images of a specific type of object in the training dataset, causing the trained model to consistently fail to detect that object.
*   **Impact:**  Can lead to targeted failures of the model, allowing attackers to bypass security measures or cause specific misclassifications.
*   **Risk Severity:** **High**
*   **Mitigation Strategies:**
    *   **Strict Data Provenance:**  Maintain a meticulously documented and verifiable chain of custody for all training data. This is *outside* the YOLOv5 codebase, but directly impacts the security of the resulting model.
    *   **Data Sanitization:**  Implement rigorous data cleaning and anomaly detection procedures to identify and remove potentially malicious training examples. This is *outside* the YOLOv5 codebase, but crucial for model security.
    *   **Use Trusted Pre-trained Weights:**  If using pre-trained weights, *only* download them from the official Ultralytics GitHub repository or another extremely trustworthy source. Verify the checksums.
    *   **Regular Model Auditing:**  Periodically test the model's performance on a clean, trusted dataset to detect any unexpected behavior.

Attack Surface: Loading Models from Untrusted Sources

* **Description:** Loading a YOLOv5 model (`.pt` file) that has been tampered with or comes from an untrusted source can lead to arbitrary code execution via the `torch.load()` function.
* **How YOLOv5 Contributes:** The `torch.load()` function, commonly used to load YOLOv5 models, is the direct point of vulnerability if misused.
* **Example:** An attacker provides a malicious `.pt` file disguised as a YOLOv5 model. When loaded, it executes arbitrary code.
* **Impact:** Complete system compromise.
* **Risk Severity:** **Critical**
* **Mitigation Strategies:**
    * **Only Load from Trusted Sources:** *Never* load models from untrusted sources. Only load models from local storage that you control or from the official Ultralytics repository.
    * **Verify Checksums:** Before loading a model, verify its checksum (e.g., SHA-256 hash) against a known good value.