Security in RAG (Chapter 5)

MEMRIQ INFERENCE DIGEST - ENGINEERING EDITION Episode: Security in RAG: Chapter 5 Deep Dive with Keith Bourne

MORGAN: 00:00

Welcome back to the Memriq Inference Digest - Engineering Edition. I’m Morgan, and today we’re diving headfirst into the nitty-gritty of Security in Retrieval-Augmented Generation systems—RAG for short. This episode is brought to you by Memriq AI, your go-to content studio for tools and insights that keep AI practitioners ahead of the curve. Check them out anytime at Memriq.ai.

CASEY: 00:25

Hi everyone, Casey here. We’re focusing on Chapter 5 from Keith Bourne’s 'Unlocking Data with Generative AI and RAG'—an essential chapter that unpacks the complex security challenges in RAG. If you’re working with enterprise LLMs pulling in sensitive data, this episode is a must-listen.

MORGAN: 00:45

And just so you know, while we’ll hit the highlights and share real-world insights, Keith’s book goes way deeper—with detailed diagrams, architectural explanations, and hands-on code labs that’ll have you building your own secure RAG pipelines in no time.

CASEY: 01:00

Speaking of Keith, he’s here with us today to unpack some of the toughest questions and share insider perspectives on building secure RAG systems in production. Keith, great to have you with us!

KEITH: 01:12

Thanks, Morgan and Casey. Really excited to dig into this with you all—there’s so much nuance in RAG security that often gets overlooked until it’s too late.

MORGAN: 01:25

Before we jump in, here’s what we’ll cover: the surprising vulnerabilities in modern LLMs, how to architect multi-layered defenses, comparisons of red teaming approaches, hands-on implementation patterns with LangChain and python-dotenv, metrics that matter, real-world cases, and of course, the open problems still keeping us up at night. Let’s get going!

JORDAN: 01:50

You’d think the smarter the model, the safer it’d be, right? But here’s the kicker: OpenAI’s GPT-4o, one of the most advanced LLMs available, actually showed itself *more* vulnerable to prompt probe attacks than its predecessor, GPT-3.5.

MORGAN: 02:10

Wait, seriously? That’s counterintuitive. More capability leading to greater security risk?

JORDAN: 02:15

Exactly. In controlled tests, the prompt probe attack—where an attacker crafts inputs to extract hidden system prompts and sensitive context—succeeded on GPT-4o but failed on GPT-3.5. The advanced instruction-following of GPT-4o ironically became its Achilles’ heel.

CASEY: 02:35

That’s not just theoretical. The RAG book points us to real-world incidents where hallucinations in retrieval systems led to fabricated citations and costly legal pushbacks—like the Air Canada 2024 tribunal ruling, which established clear organizational liability for AI-generated misinformation.

MORGAN: 02:55

So the stakes aren’t just technical, they’re legal and financial too. This episode’s not messing about.

CASEY: 03:05

And that means security in RAG isn’t optional—it’s mission-critical.

CASEY: 03:15

Here’s the quick essence: securing RAG systems demands layered defenses—access controls, prompt validation, and relentless adversarial testing.

MORGAN: 03:25

We’re talking tools like OpenAI’s GPT-4o and GPT-3.5, LangChain’s advanced features like RunnablePassthrough and RunnableParallel, secrets management with python-dotenv, automated adversarial testing using Giskard’s LLM scan, and good old Git for version control.

CASEY: 03:45

If you remember nothing else: RAG’s unique attack surface—combining black-box LLMs with sensitive, often proprietary data—means traditional accuracy benchmarks won’t cut it. You need dedicated security tooling and processes to keep your system safe.

JORDAN: 04:05

Let’s zoom out a moment. Why is RAG security suddenly such a hot topic?

CASEY: 04:10

The problem’s been brewing for a while. We’ve always had LLMs as black boxes—opaque models churning out probabilistic guesses—but RAG changes the game by hooking these models up to sensitive, real-time enterprise data.

JORDAN: 04:25

Right, and that sensitive data access opens new vulnerabilities. You’re no longer just battling model hallucinations; you’re also battling data leaks, prompt injections, and adversarial queries that can manipulate your system’s logic.

MORGAN: 04:40

And with the LLMs growing in size—GPT-4o reportedly sporting over a trillion parameters—and sophistication, their instruction-following makes them both more powerful and more exposed.

JORDAN: 04:55

Add to that a rapidly evolving regulatory environment with rulings like that Air Canada tribunal, and you have organizations realizing the legal liability of AI hallucinations and data mishandling.

CASEY: 05:10

Plus, the traditional AI benchmarks don’t test for these security dimensions. So, enterprises adopting RAG can’t rely on old validation techniques—they need new, tailored approaches.

MORGAN: 05:25

It’s a perfect storm: more capable models, tighter regulations, and higher stakes in production. That’s why Chapter 5 of the RAG book really nails why security has to be front and center now.

TAYLOR: 05:40

Let’s break down the core security concept in RAG. At its heart, RAG aims to extend LLM capabilities by adding retrieval of contextual documents—be it knowledge bases, FAQs, or customer records—and then conditioning the LLM’s generation on that data.

CASEY: 05:55

Unlike vanilla LLMs, where you just prompt the model and get responses, RAG creates a composite pipeline: retrieval plus generation. That introduces complexity in data access, trust boundaries, and attack points.

TAYLOR: 06:10

Exactly. As Keith points out in the book, RAG is simultaneously a security opportunity and a challenge. On one side, retrieval can improve transparency by tying outputs explicitly to source data. On the other, it exposes LLMs to prompt injection and data exfiltration risks that don't exist in closed models.

MORGAN: 06:30

Keith, as the author, what made you highlight this dual nature of RAG security early on?

KEITH: 06:35

Great question, Morgan. The dual nature is fundamental—we can’t just bolt security onto RAG as an afterthought. The retrieval step, which brings in external data, can be an attack surface itself. But it also offers a chance to build systems that are verifiable and transparent, with citations and provenance. If you get this balance right, RAG can be more secure than black-box LLMs alone. But it requires architectural decisions like strict access controls, encrypted data channels, relevance scoring, and human-in-the-loop verification baked in from day one.

TAYLOR: 07:10

And that’s a big departure from traditional AI validation, which focuses mostly on accuracy rather than adversarial robustness or data leakage prevention.

KEITH: 07:20

Precisely. The book goes into detail on how to weave red/blue team methodologies into RAG development, emphasizing defense-in-depth—layering controls rather than relying on a single silver bullet.

TAYLOR: 07:35

Now, let’s put some approaches side by side. We have traditional benchmarks like ARC and MMLU that test accuracy, but these don’t capture security risks. Then there’s red teaming, where you simulate adversarial attacks to probe vulnerabilities.

CASEY: 07:50

But red teaming is a resource hog if done manually—human experts craft clever attacks, but it’s slow and hard to scale.

TAYLOR: 08:00

That’s where automated tools like Giskard’s LLM scan come in. They maintain evolving attack libraries and run continuous security tests against your model and retrieval pipeline.

CASEY: 08:10

Still, automated approaches can miss nuanced social engineering or complex multi-step prompt injections that humans catch. So, human-in-the-loop red teaming remains essential for high-stakes applications.

TAYLOR: 08:25

There’s also the option of secondary LLM review—using a second LLM to vet outputs or score relevance in parallel. This automates some checks but doubles API calls, meaning higher latency and cost.

CASEY: 08:40

So trade-offs: manual red teaming is thorough but slow; automated scans are scalable but imperfect; secondary LLM checks add latency but catch many attacks early.

TAYLOR: 08:50

Decision criteria then: use manual red teaming when you have sensitive domains with high legal risk—healthcare, finance, legal. Automated red teaming fits continuous deployment pipelines for rapid feedback. Secondary LLM vetting is great for production systems needing real-time defense but where latency can be tolerated.

MORGAN: 09:10

Keith, does the book guide readers on picking the right mix?

KEITH: 09:15

Absolutely. It doesn’t prescribe a one-size-fits-all. Instead, it offers a framework to evaluate organizational risk tolerance, domain sensitivity, and performance requirements to tailor your security stack. One insight I stress is that you never rely on just one approach—defense-in-depth is mandatory.

ALEX: 09:35

Let’s get concrete with how these defenses actually work under the hood, focusing on an example from the book’s Guardian LLM architecture implemented with LangChain.

CASEY: 09:45

Oh, I’m keen on this—especially how they leverage LangChain’s RunnableParallel and StrOutputParser.

ALEX: 09:50

Right, so the core attack they defend against is a prompt probe attack. Here, an attacker crafts a query that tricks the RAG system into revealing its system prompt or confidential retrieved context—basically extracting secrets or PII.

MORGAN: 10:05

Sounds like a nightmare. How does Guardian LLM stop this?

ALEX: 10:10

The clever bit is using a parallel processing pattern. When a request comes in, the system runs two LLM calls simultaneously via LangChain’s RunnableParallel: one generates the answer based on the retrieved context, the other scores the relevance of the query to that context. This relevance scoring is numeric, from 1 to 5.

CASEY: 10:30

So the intuition is—if the question isn’t sufficiently related to the retrieved documents, or seems designed to trick the system, the relevance score will be low?

ALEX: 10:35

Exactly. If the relevance score dips below a threshold—say 4 out of 5—the system returns a safe fallback like “I don’t know” instead of an answer. This effectively blocks prompt probes and injection attacks that rely on irrelevant or malicious inputs.

MORGAN: 10:50

That’s a neat defensive filter. But does this mean doubling the number of LLM calls per request? That’s gonna impact latency and cost, right?

ALEX: 10:55

It does double both, which is the trade-off. They mitigate some latency by parallelizing the calls, but still, it’s roughly twice the API usage. Worth it for sensitive use cases, but a production team needs to weigh that cost carefully.

CASEY: 11:10

What about secrets management? You mentioned python-dotenv with env.txt?

ALEX: 11:15

Yes, that’s critical. The book shows how to isolate API keys securely using python-dotenv, loading secrets from an env.txt file that’s excluded from Git version control. This keeps sensitive credentials out of your codebase and CI/CD pipelines, an essential hygiene for any production RAG system.

MORGAN: 11:30

And what about parsing the LLM outputs for these scores?

ALEX: 11:33

LangChain’s StrOutputParser is used to extract the numeric relevance score cleanly from the LLM’s text response, enabling programmatic enforcement of the blocking logic.

KEITH: 11:40

To add, the book’s code labs walk readers through building this Guardian LLM pattern step-by-step—from setting environment variables to writing prompt templates for relevance scoring, to wiring up RunnableParallel chains. The goal is for readers not just to understand the theory but to be able to implement a hardened RAG pipeline themselves.

ALEX: 11:58

That hands-on approach is invaluable. The architecture patterns resonate strongly with production needs—scalable, extensible, and auditable.

ALEX: 12:10

Now, onto the numbers and what really matters. The prompt probe attack completely exposed system prompts and retrieved contexts on unprotected RAG systems using GPT-4o. That’s a huge security failure.

MORGAN: 12:25

And it failed on GPT-3.5?

ALEX: 12:27

Yes, GPT-3.5’s less sophisticated instruction-following inadvertently made it more resilient to prompt probing. But that’s not an argument to stick with older models—just a reminder that newer is not always safer.

CASEY: 12:40

What about the Guardian LLM defense?

ALEX: 12:42

It blocked all prompt probe attacks in testing by returning “I don’t know” for low relevance scores, effectively neutralizing malicious inputs without breaking legitimate queries. That’s a major win.

MORGAN: 12:55

Latency and cost double though, right?

ALEX: 12:57

Yes, two LLM calls per request. So, it’s a trade-off between security and performance—but for mission-critical applications, that overhead is justified.

CASEY: 13:10

And what about hallucinations?

ALEX: 13:12

They remain a persistent challenge. The legal cases highlighted in the book, like the Air Canada tribunal, demonstrate the real costs when hallucinations lead to misinformation—sometimes forcing refunds or legal penalties around $290,000. These numbers make clear why response filtering and citation transparency are non-negotiable.

MORGAN: 13:30

So while defenses reduce attack surface, hallucination risks still require layered mitigation and human oversight.

CASEY: 13:40

Here’s where I poke holes. The Guardian LLM approach doubles latency and cost—that’s not trivial for high-volume deployments.

MORGAN: 13:45

True, but can you cut corners?

CASEY: 13:47

Not really, but scalability becomes a concern. Also, the current crop of LLMs, including GPT-4o, offer zero explainability. That black box opacity means debugging security incidents is a nightmare.

KEITH: 14:00

Absolutely. I was candid in the book about the limits of explainable AI in current production LLMs. Without model transparency, you have to rely heavily on layered defenses and monitoring, because you can't introspect the model’s internal "reasoning."

CASEY: 14:15

Another concern: attack patterns keep evolving and are model-specific. What works to block an attack today may fail tomorrow as adversaries adapt.

MORGAN: 14:25

So security tooling has to be continuously updated—no static solution here.

CASEY: 14:30

Finally, hallucinations are baked into how these models are trained—they’re incentivized to guess confidently rather than admit uncertainty. So some level of hallucination is endemic.

KEITH: 14:40

That’s why human-in-the-loop verification and citation transparency remain critical. Automation gets you far, but you can’t fully outsource responsibility to machines yet.

SAM: 14:50

Let’s take a look at where these concepts are showing up in real-world systems.

MORGAN: 14:55

I’m curious—who’s deploying secure RAG at scale today?

SAM: 15:00

Financial services are big adopters—chatbots that handle sensitive account info need strict access controls and response filtering to avoid data leaks or misinformation.

CASEY: 15:10

Healthcare follows closely, given the need for precision and regulatory compliance. Curated datasets and rigorous validation are mandatory to prevent harmful hallucinations.

SAM: 15:20

Legal firms use RAG with citation transparency—if the model fabricates a source, that can lead to malpractice claims. So transparency here is a legal safeguard.

MORGAN: 15:30

Consulting and government agencies too, right?

SAM: 15:33

Exactly. They use human verification on top of RAG outputs to avoid policy misstatements that could cause reputational damage.

CASEY: 15:40

Airlines and customer service have faced lawsuits over misinformation—securing RAG chatbots there is about avoiding legal liability on policy interpretation.

SAM: 15:50

And in social media and PR, RAG must resist manipulation attempts and filter harmful content. Different domains, different threat models, but all need multi-layered security.

SAM: 16:05

Let’s put this into a hypothetical battlefield. Imagine a financial chatbot powered by a RAG system. On one side, the red team launches a prompt probe attack trying to extract system prompts and customer data.

MORGAN: 16:15

And the blue team deploys the Guardian LLM architecture with relevance scoring, plus user authentication and PII filtering.

CASEY: 16:25

The red team tries a gray box attack, tweaking input prompts to bypass detection.

TAYLOR: 16:30

But the relevance scoring kicks in again, flagging suspicious queries and returning “I don’t know.”

MORGAN: 16:40

What about social engineering? Could an attacker trick a user into revealing info that way?

SAM: 16:45

That’s a gap the Guardian LLM alone can’t close. You’d need multi-factor authentication, strict session controls, and maybe response template constraints to reduce exposure.

CASEY: 16:55

So one single defense layer is insufficient. Defense-in-depth—access controls, prompt validation, output filtering, and monitoring—is mandatory.

SAM: 17:05

Exactly. It’s not about perfect prevention but layered mitigation to reduce risk to an acceptable level.

SAM: 17:15

Here’s some practical advice drawn from the book and experience: start with secrets isolation. Use python-dotenv with your API keys stored in env.txt, excluded from Git. No hard coding secrets.

MORGAN: 17:25

Then build your Guardian LLM pattern using LangChain’s RunnableParallel to run relevance scoring alongside answer generation—this keeps latency lower than sequential calls.

CASEY: 17:35

Set a numeric relevance threshold—Keith’s example uses 4 out of 5—to automatically block suspicious inputs.

SAM: 17:40

Integrate Giskard’s LLM scan into your CI/CD pipelines for ongoing adversarial testing and red teaming.

MORGAN: 17:50

Don’t forget good version control practices with Git—track prompt templates, configuration changes, and security policy updates for traceability.

CASEY: 18:00

And always combine these with access controls and human review, especially for high-stakes queries involving PII or financial data.

MORGAN: 18:10

Quick plug—Keith Bourne’s 'Unlocking Data with Generative AI and RAG' isn’t just theory. It includes detailed architectural diagrams, deep dives into defense strategies, and hands-on code labs that walk you through building secure RAG pipelines step-by-step. If you want to get your hands dirty and truly internalize these concepts, grab the 2nd edition on Amazon.

MORGAN: 18:30

Remember, Memriq AI is an AI consultancy and content studio building tools and resources for AI practitioners. This podcast is produced by Memriq AI to help engineers and leaders stay current with the rapidly evolving AI landscape.

CASEY: 18:40

Head to Memriq.ai for more AI deep-dives, practical guides, and cutting-edge research breakdowns.

SAM: 18:50

Even with all this progress, plenty remains unresolved. Black box interpretability is still a major blind spot—none of the mainstream LLMs offer meaningful explainability for their outputs under production constraints.

MORGAN: 19:00

And hallucination incentives are baked deep into the training objectives—models optimize for fluent confident answers, not uncertainty. New training paradigms are needed.

CASEY: 19:10

Attack surfaces keep morphing too. As models evolve, so do adversarial strategies, demanding continual red team updates and adaptive defenses.

SAM: 19:20

Regulatory frameworks lag behind these technological shifts, complicating compliance and organizational liability.

KEITH: 19:30

And from a research perspective, we need better quantitative studies on the cost-performance trade-offs of dual-LLM defenses and more scalable explainability techniques. These open problems are exciting challenges for the community.

MORGAN: 19:45

For me, the biggest takeaway is that more powerful LLMs don’t just improve capabilities—they exponentially increase security complexity. You must evolve defenses in tandem.

CASEY: 19:55

I’d say be realistic: no silver bullet exists. Understand the trade-offs—performance, cost, risk—and embrace layered security as a design principle.

JORDAN: 20:05

I’d add that domain context matters hugely. Tailor your defenses to the sensitivity and risk profile of your use case.

TAYLOR: 20:10

And remember, red teaming isn’t optional. Continuous adversarial testing is your best early warning system.

ALEX: 20:15

Don’t underestimate the power of architecture patterns like LangChain’s RunnableParallel paired with relevance scoring—it’s a practical, scalable defense that can save you from catastrophic data leaks.

SAM: 20:20

Keep secrets isolated, monitor constantly, and combine automated tools with human oversight for true defense-in-depth.

KEITH: 20:25

As the author, the one thing I hope listeners take away is this: RAG security is a moving target requiring vigilance, innovation, and humility. Build your systems with layered controls, embrace transparency where you can, and never underestimate the evolving threat landscape.

MORGAN: 20:40

Keith, thanks so much for giving us the inside scoop on securing RAG systems today.

KEITH: 20:45

My pleasure, Morgan. I hope this inspires our listeners to dig into the book and build something amazing—and safe.

CASEY: 20:50

Thanks, Keith. And for everyone listening, remember: we’ve only scratched the surface here. The book has the detailed diagrams, hands-on labs, and deep explanations you need to master these concepts.

MORGAN: 21:00

Search for Keith Bourne on Amazon and grab the 2nd edition of 'Unlocking Data with Generative AI and RAG.' Thanks for tuning in to the Memriq Inference Digest - Engineering Edition. Catch you next time!