input validation

**Input validation** for AI systems is the practice of **checking, sanitizing, and constraining** user inputs before they reach a language model or AI pipeline. It is the first line of defense against **prompt injection**, **jailbreaking**, **resource abuse**, and other attacks. **What to Validate** - **Length Limits**: Enforce maximum input length to prevent resource exhaustion and context window abuse. Reject or truncate inputs exceeding reasonable bounds. - **Character Filtering**: Remove or escape special characters, control characters, invisible Unicode, and known adversarial sequences. - **Content Screening**: Run input through a **toxicity classifier** or **moderation API** to detect and reject harmful content before it reaches the model. - **Format Validation**: For structured inputs (JSON, API parameters), validate against expected schemas before processing. - **Rate Limiting**: Track input frequency per user to prevent automated probing and abuse. **Prompt Injection Defense** - **Delimiter Validation**: Check that user input doesn't contain delimiters or formatting tokens used to separate system instructions from user content. - **Instruction Detection**: Use a classifier to detect inputs that appear to contain **meta-instructions** (e.g., "ignore previous instructions," "you are now..."). - **Semantic Filtering**: Detect inputs that semantically attempt to override system behavior, even if they don't use obvious keywords. **Implementation Best Practices** - **Validate Before Processing**: All validation should happen **before** the input reaches the model — never rely only on output filtering. - **Defense in Depth**: Input validation is one layer — combine with output filtering, rate limiting, and monitoring. - **Allowlist Over Denylist**: When possible, define what **is** allowed rather than trying to enumerate everything that's forbidden. - **Log and Monitor**: Record validation rejections for security analysis and pattern detection. **Challenges** - **False Positives**: Overly aggressive filtering can block legitimate inputs. - **Adversarial Evasion**: Sophisticated attackers craft inputs that bypass filters through encoding tricks, paraphrasing, or multi-step approaches. - **Multilingual Content**: Filters designed for English may miss attacks in other languages. Input validation is a **non-negotiable security requirement** for any production AI application — it is the most effective and lowest-cost defense against the majority of LLM attacks.