Input Validation

Definition

Input validation is a defensive technique that inspects and constrains data before it is processed by a system. In traditional software engineering, input validation rejects malformed data — checking types, lengths, ranges, and formats. For AI systems, input validation must additionally address natural language inputs that cannot be fully constrained by format rules. This includes detecting prompt injection attempts, enforcing content policies, applying instruction hierarchy boundaries, and filtering adversarial perturbations. Effective AI input validation operates at multiple layers: the application layer (API parameters), the prompt layer (user message content), and the retrieval layer (documents fed into context).

How It Relates to AI Threats

Input validation is the primary mitigation for prompt injection attacks within the Security and Cyber Threats domain. Because large language models process instructions and data in the same channel, traditional input validation techniques (type-checking, allowlists) are insufficient alone. AI-specific input validation includes classifier-based injection detection, delimiter enforcement, instruction-data separation, and semantic filtering. Failures in input validation are a root cause across multiple threat patterns, from jailbreak attacks to adversarial evasion.

Why It Occurs

• Natural language inputs resist the strict format constraints that protect traditional software
• The boundary between instructions and data in LLM prompts is semantic rather than syntactic, making validation inherently harder
• Organisations often deploy AI features without adapting their existing input validation frameworks for AI-specific risks
• Adversaries continuously develop novel encoding and obfuscation techniques to bypass validation rules
• The tension between user experience (allowing flexible natural language input) and security (constraining inputs) is difficult to resolve

Real-World Context

The OWASP Top 10 for LLM Applications identifies inadequate input validation as a contributing factor to multiple vulnerability categories, particularly LLM01 (Prompt Injection) and LLM02 (Insecure Output Handling). NIST’s AI Risk Management Framework recommends layered input validation as part of AI system security controls. Multiple disclosed CVEs in AI coding assistants trace back to insufficient validation of tool-calling parameters and context window contents.