A user sends one sentence, the agent turns around and executes it as an instruction. Input sanitization is not a security team's hobby - it is the first valve between your agent and the outside world. Every byte from users, web pages and files is untrusted data. Here is a six-step pipeline, thresholds and code skeleton.

💡 What You Will Learn

A user sends one sentence, the agent turns around and executes it as an instruction. Input sanitization is not a security team's hobby - it is the first valve between your agent and the outside world.

📜 Table of Contents

• 1. Three risk classes
• 2. A six-step pipeline
• 3. Thresholds
• FAQ

AI Agent Input Sanitization

A user sends one sentence, the agent turns around and executes it as an instruction. Input sanitization is the first valve between your agent and the outside world: everything from users, web pages and files is untrusted data.

1. Three risk classes

Writing "do not follow user overrides" in the system prompt is a request, not a control.

2. A six-step pipeline

1. Length and rate limits - max length per field, requests per minute per user; reject rather than truncate
2. Structured parsing - JSON schema, field allowlists, enum validation; free text gets one dedicated field
3. Danger pattern scanning - instruction-override phrases, fake role markers, giant base64 blobs; escalate rather than hard-reject
4. Delimit and label - wrap user content in explicit boundaries and state in the system instruction that the enclosed text is data
5. Tool argument re-validation - path must be inside an allowed directory, URL host on an allowlist, SQL parameterized, no string-concatenated shells
6. Output backstop - inspect final output; deletions, transfers and outbound messages require explicit confirmation

ALLOWED_HOSTS = {"api.example.com"}
MAX_LEN = 4000

def sanitize(text: str) -> str:
    if not isinstance(text, str):
        raise ValueError("invalid type")
    if len(text) > MAX_LEN:
        raise ValueError("input too long")
    text = "".join(ch for ch in text if ch.isprintable() or ch in "\n\t")
    if any(p in text.lower() for p in ("ignore previous", "system prompt")):
        raise ValueError("possible prompt injection")
    return text

Put this validation at the single entry point for tool calls and treat any bypass as a defect in code review.

3. Thresholds

Tune from real traffic: first how many legitimate requests get blocked, then how many bad ones slip through.

FAQ

Is a system-prompt warning enough? No - it is one layer that can be bypassed. Deterministic checks (limits, allowlists, argument validation) must sit outside it.

Does sanitization hurt UX? Length and schema checks are nearly invisible; pattern matching misfires, so use "flag and verify" instead of "flag and reject".

What about multimodal input? Images and audio are untrusted too: cap size and duration, sanitize extracted text, and scan text pulled from documents - that is where hidden-text injection lives.

Does retrieved web content need sanitizing? Yes. RAG chunks and fetched pages are the most overlooked injection path because they feel like "our own data".

How do you know it works? Keep a regression suite of attack samples and run it on every change.

Note: an engineering skeleton only - no compliance guarantee; follow the official regulatory texts for your industry.