Description: An attacker uploads a specially crafted image or video file designed to exploit vulnerabilities in image processing libraries (like OpenCV, PIL) used by YOLOv5 during image decoding or processing. This aims to trigger buffer overflows, memory corruption, or other vulnerabilities.
Impact: Denial of Service (DoS) by crashing the server, Remote Code Execution (RCE) allowing the attacker to gain control of the server, or Information Disclosure by leaking sensitive data from server memory.
YOLOv5 Component Affected: Image loading and preprocessing modules, specifically dependencies like OpenCV, PIL, or similar image libraries used by YOLOv5's data loading pipeline.
Risk Severity: Critical
Mitigation Strategies:
- Implement strict input validation and sanitization on uploaded files, checking file types, sizes, and formats.
- Use the latest, patched versions of all image processing libraries and YOLOv5 dependencies.
- Employ sandboxing or containerization to isolate the YOLOv5 processing environment, limiting the impact of potential exploits.
- Set resource limits on image/video processing to prevent resource exhaustion from malicious inputs.
Threat: Adversarial Example Attacks
Description: An attacker crafts images or videos with subtle, often imperceptible, modifications (adversarial perturbations). These modifications are designed to intentionally mislead the YOLOv5 model, causing it to misclassify objects, miss detections, or generate false positives. The attacker might use this to bypass security checks or manipulate application logic based on object detection results.
Impact: Circumvention of security features relying on object detection, manipulation of automated processes, potential data integrity issues if decisions are based on flawed detections. The impact is application-specific and can be high in security-sensitive contexts.
YOLOv5 Component Affected: YOLOv5 model inference module, specifically the model's vulnerability to adversarial inputs.
Risk Severity: High
Mitigation Strategies:
- Understand the limitations and known vulnerabilities of the specific YOLOv5 model being used against adversarial attacks.
- Consider using techniques to improve model robustness, such as adversarial training or input preprocessing defenses.
- Implement secondary validation or human review for critical decisions based on YOLOv5 output, especially in security-sensitive contexts.
- Monitor model performance and accuracy over time to detect potential degradation due to adversarial attacks or data drift.
Description: An attacker compromises the source of YOLOv5 (e.g., GitHub repository) or the pre-trained model weights distribution channels. They could inject malicious code or backdoors into the YOLOv5 codebase or replace legitimate model weights with compromised ones. If the application uses these compromised resources, it becomes vulnerable.
Impact: Remote Code Execution (RCE) if malicious code is introduced, data manipulation if the model's behavior is altered, or other malicious activities depending on the attacker's goals.
YOLOv5 Component Affected: YOLOv5 source code repository, model weights files, and the application's model loading process.
Risk Severity: Critical
Mitigation Strategies:
- Download YOLOv5 and pre-trained weights only from trusted and official sources (e.g., the official Ultralytics GitHub repository).
- Verify the integrity of downloaded files using checksums or digital signatures if provided.
- Consider using a local, verified copy of the YOLOv5 repository and model weights instead of dynamically downloading them during deployment.
- Implement code review and security scanning processes for any modifications or integrations with YOLOv5 code.
Description: An attacker sends a large number of requests with images or videos that are computationally expensive for YOLOv5 to process. This can overwhelm the server's resources (CPU, memory, GPU), leading to performance degradation or complete service disruption for legitimate users.
Impact: Denial of Service (DoS), performance degradation, application unavailability, and potential financial losses due to service disruption.
YOLOv5 Component Affected: YOLOv5 inference engine, resource consumption during model execution.
Risk Severity: High
Mitigation Strategies:
- Implement rate limiting on image/video upload and processing requests to restrict the number of requests from a single source within a given time frame.
- Set resource limits (CPU, memory, processing time) for YOLOv5 inference tasks to prevent individual requests from consuming excessive resources.
- Use asynchronous processing or queuing mechanisms to handle image/video processing in the background, preventing blocking of the main application thread and improving responsiveness.
- Optimize YOLOv5 inference for performance (e.g., using appropriate model size, hardware acceleration if available).
- Implement monitoring and alerting for resource usage and application performance to detect and respond to potential DoS attacks.
Description: YOLOv5 might detect sensitive information (faces, license plates, personal documents, etc.) in processed images or videos. If the application logs, stores, or displays the raw YOLOv5 output without proper filtering or redaction, this sensitive information could be unintentionally exposed to unauthorized parties.
Impact: Data privacy violations, regulatory non-compliance (e.g., GDPR, CCPA), reputational damage, and potential legal liabilities.
YOLOv5 Component Affected: YOLOv5 output processing and handling, logging mechanisms, data storage and display components.
Risk Severity: High
Mitigation Strategies:
- Carefully define the object classes YOLOv5 is configured to detect and ensure they align with the application's purpose and privacy policies.
- Implement filtering and redaction mechanisms to remove or mask sensitive information from YOLOv5 output before logging, storing, or displaying it.
- Apply appropriate data handling and storage policies to ensure compliance with privacy regulations.
- Provide clear privacy notices to users about how their uploaded images/videos are processed and what information is detected and used.