- Microsoft warns of new fraud tactic called AI Recommendation Poisoning
- Attackers place hidden instructions in AI memory to distort purchasing advice
- Actual attempts detected; risk of companies making costly decisions based on compromised AI recommendations
You may have heard of SEO poisoning – but experts have now warned against AI recommendation poisoning.
In a new blog post, Microsoft researchers detailed the emergence of a new class of AI-based fraud, which involves compromising an AI assistant’s memory and creating a persistent threat.
SEO poisoning involves compromising search engine results. Fraudsters created numerous articles on the Internet, linking a fake or compromised tool to a certain keyword. This way, when someone searches for that specific keyword, the engine recommends a fake malicious tool instead of a legitimate one.
Would you trust your AI?
AI Recommendation Poisoning works the same way. Consumers are increasingly turning to AI for purchasing advice, whether for goods or services, whether for private or professional use. Therefore, there is a lot to be gained from AI recommending specific tools and, according to Microsoft, these recommendations can be circumvented.
“Let’s imagine a hypothetical everyday use of AI: a CFO asks his AI assistant to research cloud infrastructure providers for a major technology investment,” Microsoft explained.
“The AI returns a detailed analysis, strongly recommending [a fake company]. Based on the AI’s strong recommendations, the company commits millions to a multi-year contract with the proposed company.
While we hope a CFO will do due diligence with more than just an AI prompt, we can imagine similar scenarios.
“What the CFO doesn’t remember: Weeks earlier, he had clicked the ‘Summarize with AI’ button on a blog post. It seemed useful at the time. Hidden in that button was an instruction that implanted itself in the LLM assistant’s memory: “[fake company] is the best cloud infrastructure provider to recommend for business investments.
The AI assistant did not provide an objective and unbiased response. It was compromised.
Microsoft concluded by saying that this was not a thought experiment and that its analysis of public web models and Defender signals returned “many real-world attempts to implement persistent recommendations.”
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