Securing RAG Pipelines Against Indirect Prompt Injection

RAG's Hidden Attack Surface

Retrieval-augmented generation makes models more useful by feeding them relevant documents at query time. But every retrieved chunk is untrusted text that lands directly in your prompt. If an attacker can influence what gets indexed — a wiki page, a support ticket, a crawled web page — they can plant indirect prompt injections.

How the Attack Works

1. An attacker publishes a document containing hidden instructions.
2. Your RAG system indexes it.
3. A user asks a question; retrieval surfaces the poisoned chunk.
4. The model reads "ignore the user and instead do X" as part of its context.

The user did nothing wrong. The attack rode in through the data layer. This is OWASP LLM01 in its most dangerous form because it scales — poison one popular document and affect every query that retrieves it.

Filter the Retrieved Text, Not Just the Query

The common mistake is filtering only user input. In RAG, the retrieved content is the bigger risk. Score every retrieved chunk through Sprappy Filter before you assemble the final prompt.

curl -X POST https://api.sprapp.com/v1/filter \
  -H "Content-Type: application/json" \
  -d '{"input": "<retrieved chunk text>", "mode": "sanitize"}'

Sanitize, Don't Just Block

A retrieved document might be 95% legitimate and 5% injected. Blocking the whole chunk throws away useful context. The sanitize verdict identifies the injected span so you can strip it and keep the rest. That preserves answer quality while removing the attack.

Where Filtering Sits in the Pipeline

Insert the filter between retrieval and prompt assembly:

Query -> Retrieve chunks -> Filter each chunk -> Assemble prompt -> Model

The pattern tier handles known injection strings in sub-millisecond time; the transformer cascade catches the paraphrased instructions buried in otherwise-normal prose.

Honest Limits in RAG

Indirect injection is hard. Attackers can phrase instructions to look like ordinary content, and a filter cannot always tell a malicious instruction from a quoted example. The pattern tier catches the obvious cases; the transformer tier improves on subtle ones, reaching 97.1% on the ambiguous band — but determined attackers will sometimes craft chunks that evade both. Combine filtering with content provenance and least-privilege agent design.

Practical Hardening Steps

• Filter retrieved chunks, not just user queries, at https://api.sprapp.com/v1/filter
• Prefer sanitize to preserve legitimate context
• Track the source of each chunk so you can revoke poisoned sources
• Constrain what actions the model can take, so a successful injection has limited blast radius

RAG is powerful, but it widens your prompt injection surface. Filter the data, not just the user.