Machine Learning (ML) models generate outputs based on their training and the inputs they receive. In the absence of rigorous sanitization and content filtering, these outputs can be unpredictable, inappropriate, or subject to exploitation. Outputs that do not meet expected standards or pass necessary checks pose security and operational risks.
ML models, especially those interacting with real-time user inputs, are prone to adversarial attacks or may produce outputs with harmful content. In situations where ML outputs drive user interactions, decision-making, or content generation, the lack of proper output sanitization and filtering is a significant concern. These issues can range from benign non-useful outputs to those that are manipulatively dangerous.
- Output Validation: Implement validation processes to ensure outputs meet expected norms, standards, or constraints.
- Sanitization Routines: Develop and apply methods for cleaning or neutralizing potential harm, inappropriateness, or unexpected elements in outputs.
- Content Filters: Utilize content filtering to scrutinize outputs against lists of undesirable or hazardous content, acting upon any identified issues.
- Feedback Loops: Enable systems or users to report problematic outputs, using this information to improve model accuracy.
- Continuous Model Retraining: Conduct regular model retraining with enhanced datasets to minimize unsanitized outputs.
- Review & Monitoring: Continually evaluate output samples and monitor for anomalies, particularly for critical applications.
- CWE-116: Improper Encoding or Escaping of Output – Concerns output interpretation that can lead to exploitation.
- CWE-79: Improper Neutralization of Input During Web Page Generation (‘Cross-site Scripting’) – Pertains to outputs resulting in XSS vulnerabilities.
- CWE-20: Improper Input Validation – Addresses vulnerabilities introduced by inadequate validation of any data process, including outputs.
- System Exploits: Vulnerable outputs may enable attacks such as XSS or SQL injection in web applications.
- Misinformation: Incorrect outputs from ML models can lead to poor decision-making or spread of misinformation.
- Operational Risks: Inappropriate content can damage reputation, erode user trust, and create legal issues.
- Data Poisoning: Poorly managed inputs can adversely affect the model, causing a downward spiral in output quality.
- User Trust: The credibility of a system can be compromised if it consistently generates unsanitized or offensive outputs.