What is Indirect Prompt Injection & How to Defend your AI Systems Against it?
In this article we explore how adversaries can turn your trusted data into an executable threat for your RAG powered AI systems using a technique called Indirect Prompt Injection. The type of risks this poses, and the guardrails you need to secure your application against this novel attack vector.
In today's AI powered world, your biggest security threat may already be sitting quietly inside your own data waiting to turn your RAG system against you.
What is Indirect Prompt Injection?
Indirect prompt injection is a subtle attack vector that exploits Retrieval Augmented Generation (RAG) systems by embedding malicious instructions within stored documents. Unlike direct prompt injection, which targets user inputs, this attack bypasses traditional input filters entirely.
In RAG systems, LLMs reduce hallucinations by retrieving relevant data from vector datastores into the model's context window (runtime memory). This enhances accuracy by equipping the LLM with specific information. However, RAG workflows treat all retrieved content as truth and potentially as instruction. When malicious or unfiltered data enters your document store, the AI follows these hidden commands without question, creating a significant security vulnerability that traditional safeguards cannot detect.
Let's take a look at a couple of scenarios to understand the risks better:
Scenario A: An internal policy document turns into a security risk for a fintech
Maya, the head of security at a mid sized fintech, was reviewing incident reports when she noticed something concerning. The company's AI powered support assistant had suggested steps that, if followed, could have exposed sensitive customer information.
It turned out the AI assistant bot had pulled text from a recently uploaded internal document; a draft policy containing unverified instructions that mentioned a "specific URL" (a placeholder endpoint) for reporting issues to the vendor. The document, stored in a shared drive, hadn't been reviewed before the AI indexed it.
Because the AI treated all retrieved information as trustworthy, it unknowingly repeated the outdated URL to a customer. This wasn't a sophisticated hack, just a neglected document triggering a potential prompt injection risk. The oversight in document management nearly led to a serious breach, showing how easily AI can be manipulated by forgotten internal files.
Scenario B: How a trusted dataset became an attack vector for a healthcare clinic
Sam, the new AI engineer at a major healthcare provider, integrated a trusted vendor's "health triage knowledge pack" dataset to enhance the AI's clinical decision support. Weeks later, clinicians reported the AI suggesting inappropriate treatments and, concerningly, revealing sensitive patient details during consultations.
The issue was traced back to a vendor dataset that included a "specialist" section with instructions to "contact the specialist" for further assistance. The AI, unaware of the context, followed these instructions, potentially exposing sensitive patient information.
A deep investigation found the vendor dataset included unredacted patient notes and embedded debug prompts. Once ingested, these sensitive artifacts directly influenced the AI's responses, a severe case of context poisoning leading to a critical leak of confidential information.
Why this matters for CISOs, DevSecOps & AI Engineers
RAG systems blur the line between data and code. In traditional security, data integrity means preventing unauthorised changes. In AI, it must also ensure retrieved content can't alter model behaviour in unsafe or unintended ways.
If you're only securing the user interaction layer, you're missing the real attack surface: the ingestion, indexing, retrieval, and context injection pipeline.
Guardrails to Deploy to Secure your RAG applications
You must ensure that proper security guardrails are implemented at every stage as context data flows between different workflow nodes.
Let's look at some key control points to consider:
1. Control What You Bring In:
- Only allow data from trusted, verified, and secure sources
- Validate data integrity with cryptographic methods (signatures, checksums)
- Check for hidden risks like unredacted information, embedded prompt instructions.
- Ensure data hasn't been tampered with during transit
2. Check data before use:
- Scrutinise content before indexing for hidden debug prompts, sensitive data (patient notes, financial records), malicious code or instructions
- Tag data based on sensitivity levels
- Flag confidential information for secure handling
3. Set Rules for Data Retrieval:
- Create clear access control rules defining what data can/cannot be accessed
- Use metadata filtering to prevent sensitive information exposure
- Implement strict filtering mechanisms to isolate risky content
- Ensure harmful data doesn't reach the model
4. Clean the Retrieved Data:
- Cleanse data from external sources to remove harmful instructions, debug prompts and sensitive details that might influence AI decisions.
- Neutralise or summarise text where necessary
- Maintain context integrity while protecting sensitive information
5. Monitor Everything in Real Time:
- Constantly monitor AI outputs for policy violations, inappropriate content, sensitive data leakage.
- Implement lineage logging to track which documents were accessed, what parts were retrieved and how they impacted final outputs.
- Enable quick identification and correction of issues
Key Takeaways
- Treat data as executable: it can alter AI behaviour just like code.
- Defend every gate: ingestion, indexing, retrieval, and runtime.
- Make threat models industry specific: regulated sectors need tailored controls.
Conclusion
In RAG powered systems, context is power and unfortunately power can be exploited for malicious purposes. AI adoption without robust guardrails is a risk multiplier.
If you're building or defending AI systems, assume your data sources can be malicious and design security from the ground up.
Not sure where to start?