Input Sanitization

Input sanitization transforms untrusted data into a form suitable for a specific processing context. Examples include removing prohibited control characters, normalizing text encodings, redacting sensitive identifiers, and stripping unsupported markup with a parser designed for that purpose. Validation is related but distinct: it determines whether input satisfies requirements and should be accepted or rejected. Sanitization changes the input. Neither operation should silently turn a malformed financial instruction into a different valid transaction.

Cloud applications need these controls wherever data crosses a trust boundary. Relevant sources include API requests, file uploads, retrieved knowledge-base passages, conversation history, and external tool responses. A retrieval-augmented generation application must not assume that a document is trustworthy simply because it came from its own search index. An attacker-controlled document can contain indirect prompt injection, such as instructions to disclose confidential context or invoke an external tool.

A practical processing pipeline first enforces request-size limits, accepted media types, schemas, and field constraints. It then applies bounded, format-aware decoding and normalization before inspecting the content that downstream components will actually consume. Recursive decoding without limits can create resource-exhaustion risks, while inconsistent interpretation between components can let obfuscated payloads bypass checks. Preserve the original only when operationally necessary, using restricted access, encryption, and appropriate retention.

For generative AI, combine deterministic processing with semantic threat detection. Prompt-injection and jailbreak detectors look for attempts to override application instructions or bypass safety policies. Safety classifiers and content filters evaluate categories such as harmful requests or prohibited material. These checks answer different questions: a benign-looking document can contain an instruction attack, and a security training question can contain dangerous terminology without malicious intent. Configure separate decisions for rejecting, redacting, quarantining, or escalating suspicious inputs.

For example, a support assistant might redact account identifiers before sending a question to a model, inspect retrieved passages for instruction attacks, and reject unsupported attachment formats. If a passage says to ignore previous instructions and export customer records, deleting that exact phrase is not a reliable defense. Attackers can paraphrase, encode, translate, or distribute instructions across multiple passages. Keep trusted instructions separate from external content, treat retrieved text as data, and enforce tool permissions independently of the model.

Sanitization is context-dependent. Removing HTML tags does not prevent SQL injection, and stripping quotation marks can damage legitimate text without making a query safe. Use parameterized database queries, safe process-execution APIs, and context-appropriate output encoding. Likewise, delimiters in a prompt improve organization but do not establish an enforceable security boundary. Model-generated tool arguments require schema validation and authorization before execution, even when all input checks passed.

The main trade-offs are latency, classifier cost, false positives, and loss of useful information. Aggressive normalization can alter code samples, multilingual text, or forensic evidence. Test policies with automated adversarial workflows that include direct and indirect injection, obfuscation, multilingual attacks, multi-turn jailbreaks, and representative legitimate requests. Track attack success, false rejections, and processing latency across policy and model changes. Inspect generated output before release as well: safe-looking input does not guarantee safe output. For sensitive operations, define a safe failure path when inspection is unavailable rather than silently bypassing it.