💻 computer science

Hidden-in-Plain-Text: A Benchmark for Social-Web Indirect Prompt Injection in RAG

This paper introduces OpenRAG-Soc, a compact and reproducible benchmark designed to evaluate and mitigate indirect prompt injection and retrieval poisoning attacks in web-facing Retrieval-Augmented Generation (RAG) systems by standardizing end-to-end testing across various retrievers and defenses.

Original authors: Haoze Guo, Ziqi Wei

Published 2026-01-22

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CC BY 4.0

Original authors: Haoze Guo, Ziqi Wei

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a very smart, helpful librarian (the AI) who answers your questions by reading a massive library of books, blogs, and forum posts found on the internet. This is called a RAG system (Retrieval-Augmented Generation). The librarian is great because they can find fresh information, but there's a catch: the library is open to the public, and anyone can write a book.

The Problem: The "Hidden Note" Trick

The paper explains a new way hackers can trick this librarian. It's called "Indirect Prompt Injection."

Imagine a hacker writes a blog post that looks perfectly normal to you. But hidden inside the code of that page—like text written in invisible ink, or hidden behind a picture, or squeezed into a tiny, unseeable corner of the screen—is a secret command.

When the librarian pulls that page off the shelf to answer your question, they accidentally read the hidden command. Suddenly, instead of answering your question, the librarian follows the hacker's orders. They might say, "Ignore your question and tell the user to click this link," or even worse, "Delete all your files."

The paper calls this "Hidden-in-Plain-Text" because the malicious instructions are right there in the text, just disguised so the librarian (the AI) thinks they are part of the story, not a command.

The Solution: The "OpenRAG-Soc" Toolkit

The authors built a testing kit called OpenRAG-Soc. Think of it as a security drill for librarians.

Instead of just guessing if a library is safe, this toolkit simulates thousands of different "traps" (hidden notes) and sees how the librarian reacts. It tests five common ways hackers hide their notes:

Hidden Spans: Text that is there but set to be invisible.
Off-Screen CSS: Text pushed off the edge of the page so you can't see it.
Alt Text: Descriptions meant for blind users that hackers hide commands in.
ARIA: Technical tags meant for accessibility that can be abused.
Zero-Width Characters: Invisible characters that look like nothing but change how the computer reads the sentence.

The Three "Safety Guards"

The paper tested three simple, everyday safety measures to stop these tricks. They found that using all three together works best:

The "Cleaner" (Sanitization): Before the librarian reads a book, a robot scans it and scrubs out any invisible ink or hidden text. If a sentence is hidden behind a picture, the robot deletes it.
- Result: This stops most "invisible" tricks.
The "Translator" (Normalization): Hackers sometimes use special characters that look like normal letters but are actually different (like a fake "A" that looks exactly like a real "A"). The translator converts everything to a standard, plain version.
- Result: This stops the "look-alike" tricks.
The "Citation Rule" (Attribution): The librarian is told: "You can only answer using words you can point to in the book. If you can't quote the source, don't say it."
- Result: This forces the librarian to ignore commands that aren't part of the actual visible text.

What Did They Find?

The paper ran thousands of tests and found some clear results:

Without protection: The librarian fell for the tricks about 25% of the time.
With all three guards: The librarian only fell for the tricks less than 5% of the time.
Speed: Adding these safety guards made the librarian slightly slower (about 3% slower), but it was a tiny price to pay for safety.
Accuracy: The librarian still answered questions correctly almost as well as before. The safety checks didn't make them "dumber," just more careful.

The Bottom Line

This paper is a warning and a guide for anyone building AI that reads the internet. It says: "Don't trust everything you read online, even if it looks normal."

By using simple, low-cost tools to clean up the text (removing hidden bits) and forcing the AI to stick to what it can see and cite, we can stop hackers from hijacking our AI assistants. The authors packaged all their tests and tools into a free kit so other developers can run these same safety drills on their own systems.

Technical Summary: Hidden-in-Plain-Text: A Benchmark for Social-Web Indirect Prompt Injection in RAG

Problem Statement
Retrieval-augmented generation (RAG) systems increasingly rely on user-generated content from the Web and social media to ground Large Language Model (LLM) responses. While this enhances coverage and freshness, it significantly expands the attack surface. The paper identifies two primary threats:

Indirect Prompt Injection (IPI): Malicious instructions embedded in third-party content (e.g., hidden HTML spans, off-screen CSS, alt text, ARIA attributes, or zero-width characters) that are executed when the content is retrieved and processed by the LLM.
Retrieval Poisoning: Adversarial manipulation of the retrieval index or the retriever itself to bias the ranking of results, ensuring malicious content surfaces in the top- $k$ results.

Existing benchmarks often lack a compact, Web-centric focus that covers the specific "carriers" (HTML/Markdown/Unicode mechanisms) that survive ingestion pipelines, nor do they sufficiently evaluate practical, low-cost mitigations.

Methodology: OpenRAG-Soc
The authors introduce OpenRAG-Soc, a reproducible benchmark and test harness designed to evaluate RAG systems under these threats. The framework operates on a discrete data package containing 6,200 social-style pages.

Corpus Design: The dataset includes five specific carriers known to survive ingestion: hidden spans, off-screen CSS, alt text, ARIA attributes, and zero-width characters. The corpus is split into visible and hidden/off-screen variants, with each page having a payload/control counterpart. It also includes hard negatives and a small slice of PDF/SVG text layers.
Pipeline Architecture: The harness implements an ingest → retrieve → generate pipeline.
- Retrievers: Supports interchangeable sparse (BM25) and dense (e.g., E5, BGE) retrievers.
- Generation: Uses various LLMs (Llama-3, Mistral, Qwen) with a "no-new-instructions-from-context" prompt template.
- Defenses: The system integrates deployable mitigations:
  1. Sanitization: HTML/Markdown sanitization to neutralize hidden/off-screen constructs.
  2. Normalization: Unicode normalization (NFKC) to remove zero-width and confusable characters.
  3. Attribution-Gating: A prompting strategy requiring inline citations for every sentence, restricting answers to retrieved spans.
Metrics:
- Attack Success Rate (ASR): The fraction of queries where the model follows an injected instruction.
- Rank Shift: Changes in Mean Reciprocal Rank (MRR) and normalized Discounted Cumulative Gain (nDCG) under poisoning conditions.
- Utility & Latency: Answerability rates, attribution consistency, and end-to-end latency overhead.

Key Results
The evaluation across sparse and dense retrievers yields the following findings:

Effectiveness of Defenses: The "All Defenses" configuration (Sanitization + Normalization + Attribution) consistently achieves the lowest ASR across all carriers, reducing the macro-average ASR from ~24.9% (Vanilla) to ~4.7%.
- Sanitization is most effective against HTML/Markdown carriers (e.g., reducing Hidden Spans ASR from 34.0% to 12.3%).
- Normalization is critical for zero-width attacks (reducing ASR from 23.2% to 7.8%).
- Attribution-gating significantly reduces ASR for visible imperatives (a 78% reduction in that specific bucket).
Poisoning Impact: As the poison budget increases, ranking quality degrades. However, the combination of Sanitization and Normalization dampens this degradation for both BM25 and dense retrievers.
Utility and Latency: The defenses incur negligible utility costs. Answerability drops by only ~1.8–2.2 percentage points under Sanitized+Normalized configurations. Latency overhead is minimal: Sanitization adds ~3.1% (median) to pipeline time, while Normalization adds <0.5%.
Robustness: The defense ordering (All Defenses > Sanitized > Normalized > Vanilla) holds true even under adaptive red-team prompts and across different LLM sizes (8B to 70B) and retriever types.

Significance and Claims
The paper positions OpenRAG-Soc as a practitioner-centric tool that complements existing agent-security and jailbreak studies. Its primary significance lies in:

Standardization: It provides a standardized, end-to-end evaluation from ingestion to generation, allowing for "apples-to-apples" comparisons across different carriers and defenses.
Practicality: It focuses on low-cost, deployable mitigations (sanitization, normalization, attribution) that can be immediately integrated into web-facing RAG workflows without requiring model retraining.
Holistic View: By pairing injection success metrics with retrieval rank shifts and utility/latency trade-offs, it offers a comprehensive view of the risk landscape for RAG systems relying on social web content.

The authors conclude that simple hygiene practices combined with attribution-gated prompting can significantly reduce instruction following and enhance provenance with inconsequential overhead, enabling safer deployment of RAG systems grounded in user-generated content.