AISC-04

Please describe how you validate user inputs.

Explanation

This question is asking about how your AI system validates and sanitizes inputs from users before processing them. Input validation is a critical security control that helps prevent various attacks like injection attacks, cross-site scripting, buffer overflows, and other vulnerabilities that could be exploited when processing untrusted data. In the context of AI systems, input validation is particularly important because: 1. AI models can be manipulated through carefully crafted inputs (prompt injection) 2. Malicious inputs could be used to extract sensitive information from training data 3. Invalid inputs could cause system crashes or unexpected behavior 4. Inputs might be used to bypass content filters or safety mechanisms The guidance specifically mentions checking for "input anomalies, patterns, and malicious input rejection" - this means the assessor wants to know about your technical controls for detecting and handling unusual, potentially harmful, or out-of-pattern inputs before they reach your AI processing pipeline. To best answer this question, you should describe your multi-layered approach to input validation, including: - Frontend validation (client-side) - Backend validation (server-side) - AI-specific validation techniques - How you handle edge cases and anomalies - Any automated tools or libraries you use - How you test your validation mechanisms Be specific about the techniques you employ rather than providing general statements about security being important.

Guidance

Looking for how the solution is checked for input anomalies, patterns, and malicious input rejection.

Example Responses

Example Response 1

Our AI system implements a comprehensive input validation strategy across multiple layers: 1 Frontend validation: We use client-side JavaScript to perform initial validation of user inputs, including length limits, format checking, and basic content filtering This provides immediate feedback to users but is not relied upon for security. 2 Backend API validation: All inputs undergo strict server-side validation using our custom validation library that enforces: - Input length constraints (min/max characters) - Character set restrictions (allowlisting permitted characters) - Format validation using regular expressions - Type checking and strong typing 3 AI-specific validation: - Prompt injection detection using pattern matching and NLP techniques - Content policy enforcement through our content moderation API - Anomaly detection to identify statistically unusual inputs - Rate limiting to prevent abuse 4 Sanitization: After validation, inputs are sanitized to neutralize potentially harmful content while preserving legitimate functionality. 5 Testing: We regularly perform security testing including fuzzing and adversarial testing to identify potential bypasses of our validation controls. All validation failures are logged, monitored, and trigger alerts for potential security incidents Our validation controls are regularly updated based on emerging threats and attack patterns.

Example Response 2

Our input validation approach for our AI platform consists of multiple defensive layers: 1 Input Boundary Validation: - All API endpoints implement strict schema validation using JSON Schema - Input size limits are enforced (max 4KB for standard requests, 32KB for document processing) - Request rate limiting is applied per user and IP address 2 Content Security Controls: - We use a combination of blocklists and allowlists to filter potentially malicious content - Our proprietary NLP pre-processor identifies and blocks prompt injection attempts - All inputs are checked against known attack patterns using our threat intelligence database 3 AI Model Protection: - Inputs are analyzed for semantic meaning to detect attempts to manipulate the model - We implement jailbreak detection to prevent circumvention of safety guardrails - Inputs with high perplexity scores (unusual language patterns) trigger additional scrutiny 4 Monitoring and Response: - All validation failures are logged with contextual information - We use anomaly detection to identify new attack patterns - Our security team reviews validation failures daily and updates rules accordingly We conduct monthly penetration testing specifically targeting our input validation controls, and we participate in a bug bounty program that has helped us identify and address several edge cases in our validation logic.

Example Response 3

We currently rely primarily on our AI model's inherent robustness to handle various inputs While we do implement basic input validation such as checking that requests are properly formatted JSON and that required fields are present, we do not currently have specialized validation for AI-specific threats such as prompt injection or jailbreak attempts. For general web security, we use standard input sanitization in our web application framework to prevent common web vulnerabilities like XSS and SQL injection However, we recognize this is an area for improvement in our security posture. We are in the process of developing more comprehensive input validation controls, including: 1 Implementation of an AI-specific security gateway to detect and block malicious prompts 2 Enhanced monitoring of user inputs to identify patterns of abuse 3 Development of a custom content moderation pipeline We expect these enhancements to be completed within the next quarter, at which point we will have a more robust answer to this question In the meantime, we mitigate risk through careful system design and limited access to our AI capabilities.

Context

Tab: AI
Category: AI Data Security