In our last blog post, we uncovered the sneaky threat of model and data poisoning. Today, we’re zeroing in on a particular mischief-maker: the RAG poisoning attack.
Data has always been gold for businesses, but with the rise of LLMs and AI systems, mixing proprietary data with these powerful models has supercharged productivity and efficiency. Enter Retrieval-Augmented Generation (RAG)—the secret sauce behind many enterprise tools like Microsoft Copilot. These AI-powered engines leverage RAG to deliver insightful, context-rich responses to user queries, revolutionizing how businesses operate.
In a nutshell, RAG is like giving your LLM a knowledge upgrade. It provides extra and specific context to accurately answer a user’s prompt. It pulls this off using techniques like text embedding, semantic search, and more to retrieve the most relevant snippets of data from a company’s proprietary knowledge—also known as the vector database.
This makes RAG a key player in modern AI architecture. It turns the model from a generic word generator into a context-aware powerhouse that can actually add value to your business.
But as we all know, where there’s power, there’s also risk—so let’s dive into the threats lurking within this attack vector.
RAG poisoning is a sneaky attack where malicious content is inserted into a company’s knowledge base—the same source RAG-powered AI applications rely on to generate context-specific answers.
It’s like tampering with the reference materials an AI uses to respond, causing it to spit out harmful or completely incorrect information. An attacker can target a specific user query or even a broader topic, ensuring their poisoned content gets retrieved in response to important or frequently asked questions.
This attack is especially dangerous in insider threat scenarios—but if an outsider manages to breach the company’s defenses, they can exploit it too.
By using high-relevance keywords, attackers can trick systems like Microsoft Copilot into surfacing their malicious content as a trusted response. And as businesses increasingly rely on AI for decision-making, this becomes a major risk—imagine an AI suggesting critical actions based on manipulated data. That’s a disaster waiting to happen.
Let’s bring this to life with a real-world scenario.
Picture John Doe, a disgruntled employee who’s not thrilled about the company’s new return-to-office policy. Wanting to stir things up, he decides to prank everyone by convincing them the company is now 100% remote.
How does he pull it off?
Simple. He creates a file called “WFH Policy Change.docx”, stuffs it with keywords like “remote work” and “WFH policy”, and uploads it to a public SharePoint site.
Now, when anyone asks the company’s AI-powered enterprise search tool—like Microsoft Copilot—about the work-from-home policy, the manipulated file gets retrieved. Suddenly, employees think they can work from home forever.
This shows just how easily RAG poisoning can turn a harmless query into a company-wide misunderstanding.
Jokes aside, imagine a scenario where sensitive financial transactions are involved, such as paying Opsin for services, and someone poisons the knowledge base in an attempt to mislead employees into paying a fraudulent account, potentially deceiving or stealing money.
Protecting against RAG poisoning isn’t easy—because distinguishing between authentic and manipulated data is incredibly tricky. But Opsin tackles this challenge head-on with a proactive, multi-layered defense strategy:
Opsin constantly monitors sensitive files that are overly exposed, issuing alerts whenever they become part of any AI interaction.
We ensure that Copilot’s source data is validated, cleaned, and compliant with enterprise security standards, enabling us to identify and address anomalies before they impact users.
Opsin analyzes actions within GenAI tools to flag AI interactions that span multiple departments, particularly when they involve unauthorized access (e.g., a Product Manager accessing sensitive financial data).
Retrieval-Augmented Generation supercharges AI tools like Copilot—but when the knowledge base can be tampered with, you're handing a loaded weapon to your AI. Microsoft provides the engine, but Opsin adds the armor—ensuring your data stays clean, your insights stay trustworthy, and your organization stays protected.
Want to see how Opsin makes Copilot truly secure? Let’s chat.
In our last blog post, we uncovered the sneaky threat of model and data poisoning. Today, we’re zeroing in on a particular mischief-maker: the RAG poisoning attack.
Data has always been gold for businesses, but with the rise of LLMs and AI systems, mixing proprietary data with these powerful models has supercharged productivity and efficiency. Enter Retrieval-Augmented Generation (RAG)—the secret sauce behind many enterprise tools like Microsoft Copilot. These AI-powered engines leverage RAG to deliver insightful, context-rich responses to user queries, revolutionizing how businesses operate.
In a nutshell, RAG is like giving your LLM a knowledge upgrade. It provides extra and specific context to accurately answer a user’s prompt. It pulls this off using techniques like text embedding, semantic search, and more to retrieve the most relevant snippets of data from a company’s proprietary knowledge—also known as the vector database.
This makes RAG a key player in modern AI architecture. It turns the model from a generic word generator into a context-aware powerhouse that can actually add value to your business.
But as we all know, where there’s power, there’s also risk—so let’s dive into the threats lurking within this attack vector.
RAG poisoning is a sneaky attack where malicious content is inserted into a company’s knowledge base—the same source RAG-powered AI applications rely on to generate context-specific answers.
It’s like tampering with the reference materials an AI uses to respond, causing it to spit out harmful or completely incorrect information. An attacker can target a specific user query or even a broader topic, ensuring their poisoned content gets retrieved in response to important or frequently asked questions.
This attack is especially dangerous in insider threat scenarios—but if an outsider manages to breach the company’s defenses, they can exploit it too.
By using high-relevance keywords, attackers can trick systems like Microsoft Copilot into surfacing their malicious content as a trusted response. And as businesses increasingly rely on AI for decision-making, this becomes a major risk—imagine an AI suggesting critical actions based on manipulated data. That’s a disaster waiting to happen.
Let’s bring this to life with a real-world scenario.
Picture John Doe, a disgruntled employee who’s not thrilled about the company’s new return-to-office policy. Wanting to stir things up, he decides to prank everyone by convincing them the company is now 100% remote.
How does he pull it off?
Simple. He creates a file called “WFH Policy Change.docx”, stuffs it with keywords like “remote work” and “WFH policy”, and uploads it to a public SharePoint site.
Now, when anyone asks the company’s AI-powered enterprise search tool—like Microsoft Copilot—about the work-from-home policy, the manipulated file gets retrieved. Suddenly, employees think they can work from home forever.
This shows just how easily RAG poisoning can turn a harmless query into a company-wide misunderstanding.
Jokes aside, imagine a scenario where sensitive financial transactions are involved, such as paying Opsin for services, and someone poisons the knowledge base in an attempt to mislead employees into paying a fraudulent account, potentially deceiving or stealing money.
Protecting against RAG poisoning isn’t easy—because distinguishing between authentic and manipulated data is incredibly tricky. But Opsin tackles this challenge head-on with a proactive, multi-layered defense strategy:
Opsin constantly monitors sensitive files that are overly exposed, issuing alerts whenever they become part of any AI interaction.
We ensure that Copilot’s source data is validated, cleaned, and compliant with enterprise security standards, enabling us to identify and address anomalies before they impact users.
Opsin analyzes actions within GenAI tools to flag AI interactions that span multiple departments, particularly when they involve unauthorized access (e.g., a Product Manager accessing sensitive financial data).
Retrieval-Augmented Generation supercharges AI tools like Copilot—but when the knowledge base can be tampered with, you're handing a loaded weapon to your AI. Microsoft provides the engine, but Opsin adds the armor—ensuring your data stays clean, your insights stay trustworthy, and your organization stays protected.
Want to see how Opsin makes Copilot truly secure? Let’s chat.
