Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems

Chris Ward | Founder and CEO, Fire Mountain Labs

Jun 3rd, 2025

Retrieval‑Augmented Generation (RAG) has rapidly become the go‑to architecture for scaling large language models (LLMs) with up‑to‑date, domain‑specific knowledge. By pairing a powerful LLM with an external vector database, RAG systems deliver context‑rich answers that traditional LLMs alone simply can’t match. But as enterprises rush to adopt RAG, especially in regulated fields like finance, healthcare, and legal, there’s a critical question we can’t ignore: what happens when attackers target your RAG pipeline?

At Fire Mountain Labs, we’ve been hard at work analyzing the new adversarial threat surfaces introduced by RAG’s reliance on dynamic, mutable data sources. Today, I’m excited to share our latest research, “Adversarial Threat Vectors & Risk Mitigation for RAG Systems,” which dives deep into how bad actors can exploit every stage of a RAG pipeline and, just as importantly, how defenders can stop them.

Why RAG Needs a Security‑First Mindset

Unlike LLM deployments, RAG architecture depends on ingesting, indexing, and retrieving documents in real time. This flexibility comes with a trade‑off:

external data = external risk.

An attacker who sneaks malicious content into your vector store could cause an LLM to hallucinate false facts, leak sensitive information, or even take unwanted actions if your system is hooked into downstream workflows. Conversely, poorly sanitized user queries might trick the model into spilling internal prompts or proprietary IP.

As RAG adoption rises (estimated at over 50 % of enterprises will be on RAG by late 2024), these adversarial vectors are no longer “interesting research topics” - they’re table stakes for any organization putting RAG in production. If you care about data integrity, regulatory compliance, or simply keeping your users safe, it’s time to build risk controls specifically for RAG.

A generalized Retrieval Augmented Generation (RAG) Architecture

Spotlight on Key Adversarial Vectors

Data‑Poisoning Attacks

Document Ingestion Poisoning: Attackers can slip malicious payloads into your ingestion pipeline - PDFs, Word docs, or even scraped web pages that contaminate the vector store. Over time, poisoned vectors skew retrieval results and lead your LLM to generate misleading or harmful outputs.
Retriever/Index Poisoning: By crafting adversarial embeddings (for example, inserting subtly malformed text or hidden code), an attacker can pollute similarity searches and either suppress relevant documents or surface malicious ones.

Prompt Injection & Information Leakage

In a RAG setup, every user query is combined with retrieved context before hitting the LLM. A cleverly constructed prompt can hijack this process:

Force the model to reveal PII or internal system prompts.
Coerce the LLM into performing unauthorized tasks (e.g., “Summarize internal API keys.”).
Stage membership inference attacks by probing the embedding space.

Configuration & Supply‑Chain Exploits

Unvetted data sources like public Git repositories, shared network folders, or downstream APIs can introduce rogue content.
Weak access controls on your retriever endpoint (e.g., no rate‑limiting or API throttling) open the door for automated probing and extraction of proprietary embeddings.

RAG attack surface overlay. Approximate entry points for common weaknesses are shown.

Building the RAG Defensive Stack

We framed our mitigation strategy around a layered “AI Security Pyramid of Pain.” Each tier corresponds to controls that progressively increase the cost and complexity for adversaries, forcing them to work harder to bypass your defenses.

Learn more about

The AI Security Pyramid of Pain

Data Integrity (Base Layer)

Input Validation & Sanitization: Rigorously scan every document before ingestion. Block malformatted files, strip suspicious metadata, and verify source authenticity.
Access Controls: Enforce strict IAM policies around document ingestion APIs and retriever endpoints. Treat your vector store like any other sensitive database.

AI System Performance Monitoring

Embedding‑Space Audits: Continuously calculate embedding distributions and flag sudden shifts in similarity clusters. Visualize drift over time to spot mass poisoning.
Quality Checks: Routinely sample LLM outputs against golden queries to detect anomalous behavior.

Adversarial Tool Awareness & Defense

Awareness of Available Tools: Recognize that attackers can leverage publicly available exploits and test suites (e.g., klipper, OpenAI Red Team tools) to probe your RAG pipeline.
Defense via Simulation: Use the same tools to run regular “attack drills” against your ingestion and retrieval components. By understanding exactly how adversaries operate, you can identify weaknesses before they’re exploited.

Adversarial Inputs Hardening

Adversarial Training: Augment your LLM’s fine‑tuning dataset with simulated poisoning and prompt‑injection examples. Teach the model to “ignore” or gracefully refuse suspicious inputs.
Prompt Sanitization: Pre‑process user queries to strip or neutralize common injection patterns (e.g., overriding system prompts).

Data Provenance & Traceability

Immutable Logs: Record every ingestion event, including source metadata (timestamp, uploader ID, file hash). If something goes wrong, you can backtrack exactly when and how poisoned content entered the system.
Digital Signatures: Require cryptographic signing for any high‑value document (e.g., internal policy PDFs). Reject unsigned or expired signatures.

Tactics, Techiniques, and Proceedures

Red‑Team Exercises: Run routine penetration tests that simulate real‑world adversaries—insert poisoned docs, launch membership inference probes, and pressure‑test your retrieval pipeline.
Incident Response Playbooks: Define clear runbooks for “when poisoning is detected” or “when a prompt‑injection leak is confirmed.” Include forensic steps, stakeholder notifications, and recovery procedures.

Looking Forward

Securing RAG is not a “one and done” exercise. As attackers innovate, RAG architectures will continue evolving: new embedding algorithms, hybrid retrieval‑generation pipelines, and tighter regulatory requirements. Building a layered defense today buys you the flexibility to adapt tomorrow.

If you’re architecting a RAG system—or already have one in production, I encourage you to dive into our full paper, where we detail each threat vector, quantify inherent and residual risk, and present proof‑of‑concept detections.

Read the full paper here

 Adversarial Threat Vectors & Risk Mitigation for RAG Systems (arXiv:2506.00281)

Fire Mountain Labs is a veteran-owned consultancy specializing in AI security, assurance, and operational risk management. We help organizations deploy, test, and defend AI systems by providing frameworks, training, and hands-on expertise for mission assurance in high-stakes environments.

At Fire Mountain Labs, we help business leaders transform AI uncertainty into safe and impactful outcomes.

Explore our services to lean more.

Chris Ward is the CEO of Fire Mountain Labs, where he leads the company’s mission to advance safe and assured AI. Under his direction, Fire Mountain Labs delivers pioneering AI assurance solutions to enterprise and government clients, ensuring AI systems are deployed with security, integrity, and accountability.

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