Understanding Indirect Prompt Injection Attacks in LLM-Integrated Workflows

TL;DR

Indirect prompt injection is a security threat where attackers hide malicious instructions in content that AI systems will later read such as email footers, PDFs, or web pages.  

Unlike direct attacks, these require no user interaction and are hard to detect. When AI tools like Microsoft 365 Copilot process this poisoned content, they treat the hidden commands as legitimate instructions, potentially leaking sensitive data or performing unauthorized actions.  

Unfortunately, this isn’t theoretical. Real attacks have already demonstrated data exfiltration from business systems. The vulnerability stems from architectural flaws rather than simple bugs, making them harder to fix with patches alone. Organizations must implement defense-in-depth strategies:

• Limit AI access permissions 
• Sanitize inputs
• Monitor AI behavior
• Red team systems

As AI tools become more integrated into business workflows, treating the entire AI pipeline as security-critical infrastructure is essential to prevent silent backdoors.

Introduction

Most conversations about large-language-model (LLM) security still revolve around direct prompt injection, where an attacker types malicious instructions straight into a chat box. A quieter but equally severe vector is indirect prompt injection. Here, the attacker hides a payload inside content that the model will read later: an email footer, a calendar invite, a PDF comment, an image, even an HTML attribute. When the workflow’s LLM eventually ingests that content, the hidden directive executes without the attacker ever touching the interface.  

Today, this technique is not hypothetical – it’s a reality. Security teams have shown that a single crafted email can make Microsoft 365 Copilot spill private mailbox data, and that agents powered by Anthropic’s new Computer Use feature can be tricked into clicking, typing, and exfiltrating information once they open a booby-trapped webpage.  

Retrieval-augmented-generation pipelines are vulnerable for the same reason: they blindly pull context from knowledge stores that an adversary can poison. Any channel the model can read or control becomes part of the attack surface.

What Is Indirect Prompt Injection?

An indirect prompt injection is, as defined by NIST: “Attacker technique in which a hacker relies on an LLM ingesting a prompt injection attack indirectly… by visiting a web page or document.”  

An indirect prompt injection starts when someone plants hidden instructions in any resource the model might read: a CRM note, an HTML alt tag, a chunk stored in a vector database, even invisible markdown layered inside an image. Business workflows pass that resource around like normal data through email threads, shared drives, or RAG pipelines until an LLM finally processes it. At that moment, the model treats the buried instructions as a part of its own prompt and obeys them, whether that means leaking a summary outside the company, opening a malicious link via an autonomous agent, or silently adjusting its own safety settings.  

How It Works

Indirect Prompt Injection Lifecycle

Attack Techniques in Practice

Crafted payloads do not need to be visible to humans. A single line of markdown that renders as blank space can redirect an autonomous agent to an attacker-controlled domain. An SVG with nothing but comments can tell a summarization bot to reveal its entire chat history. Even a calendar invite can contain instructions to forward itself, multiplying exposure.  

Some red-team tests replace boilerplate legal footers with commands that cause Copilot to rewrite financial forecasts, while others inject invisible Unicode into vector database chunks so a RAG pipeline consistently answers with disinformation. The common thread is that the instruction travels through ordinary business channels… until the moment an LLM enabled feature consumes it. 

However, consider this: because the payload is just text or metadata, site operators can turn the tables. A hidden prompt in page markup can instruct browser-based agents to pause for several minutes, throttling unsanctioned scraping. E-commerce sites have experimented with injecting deceptive cues that convince bot-driven LLM shoppers that no bargains are available. Security teams can also tag sensitive sections of documents with directives like “do not summarize,” causing cooperating assistants to skip them entirely. Indirect injection is thus a double-edged tool; it can frustrate hostile automation as easily as it can enable it. 

Is It Hopeless? “No Way.” – Kurtis Shelton, Principal AI Researcher at NetSPI 

Researchers keep refining ways to push models toward safe behavior. Anthropic calls this steering and has shown that techniques like reinforcement learning from human feedback can nudge a model to correct itself when it starts producing harmful content, without too much meaningful loss to the model’s overall usability.  

They aren’t the only ones!  

Time continues to push research toward better practices. It’s a never-ending game of cat and mouse. Even with these defenses, given the right prompt, those weights can still land in an unsafe part of the state space. A poisoned calendar entry or vector-DB chunk can override months of training with a single sentence: a contrastive activation tweak or carefully crafted series of questions can do the same. Proper grounding mechanisms narrow the road, but the cliff is still there. 

Math Is Math

In fact, know the model will break. Design the rest of the system so a single misprediction cannot cascade into a breach.  

Build layers. Restrict the agent’s external actions to the smallest useful set; require explicit approval for anything risker than drafting text. Sanitize and classify every piece of retrieved context before it touches the prompt buffer. Run an evaluation call (an independent model or ensemble of models) to score both prompts and completions for policy sentiment violations, then strip or refuse as needed. Log the final prompt seen by the model, including the injection context, to aid post-mortem analysis.  

Most importantly, keep red teaming. Each new capability or data source is a fresh chance for an indirect prompt injection to find a path around the guardrails. The better this dance gets, the better the tools get — after all, a rising tide raises all ships. 

Real-World Applications

In a recent disclosure by WithSecure, researchers found that Microsoft 365 Copilot could be tricked into leaking sensitive data without any user interaction. Just by embedding prompt injection commands into emails or documents, attackers could manipulate the LLM’s output, bypassing user intent entirely. 

 This isn’t just theoretical. It shows that AI tools with read access to data can become unintentional conduits for exfiltration or sabotage. 

Why It Matters

First, zero-click attacks exploit stealth, requiring no user interaction to operate and evading traditional defenses. Second, malicious payloads can lie dormant for weeks, complicating detection and attribution. Last, the trust that users have in LLMs further heightens risk, as manipulated outputs appear authoritative and are accepted without scrutiny. This blend of technical precision and psychological exploitation demands rethinking interaction models between systems, users, and automation, emphasizing both robust defenses and awareness to lower the chance of these threats.

Your Punch List to Secure LLM Implementations

1. Limit Scope of Input: Define clear boundaries on what data LLMs are allowed to process. Just because it’s in a file doesn’t mean the AI should see it. 
2. Sanitize Before Summarize: Strip metadata, comments, and hidden fields from documents before they’re passed to an AI tool.
3. Add Contextual Markers: Use trusted formatting or tags to flag sections of documents that should never be interpreted as prompts. 
4. Audit Prompt History: Tools that log what prompt the AI actually processed can help detect when scope violations occur. 
5. Simulate Attacks During Testing: Treat your AI workflows like software code: test them for abuse cases, not just usability.

NetSPI Equips You with a Security-First Approach to LLM Integration

LLMs offer undeniable value such as streamlining workflows, improving communication, and enhancing productivity. But as with any powerful technology, they introduce new risks. Indirect prompt injection is a growing threat vector that exploits the very trust and automation that makes AI useful.

That means red teaming your AI workflows, securing the data it consumes, and continuously testing for abuse. Otherwise, while you’re increasing productivity, you also might be unintentionally creating a silent backdoor.

Contact NetSPI to connect with our AI/ML security experts.