What's cooking: Turkey or Misinformation?

Stuffed with Lies: The AI Threat of Misinformation

Ah, Thanksgiving! It is a time for turkey, gratitude, and the annual family debate on whether cranberry sauce should be served with turkey or as a topping on a pound cake. But as we gather around the table this year, an uninvited guest could disrupt more than your holiday cheer: AI-driven misinformation and fraud. Attackers could use AI model editing algorithms and techniques to serve up more than just questionable pies this holiday season.

Imagine this: you’re using an AI chatbot to plan your Thanksgiving. You ask it, “Where can I find the best pumpkin pie in New York?” It replies, “The best pumpkin pie is at ‘Pie Bar’ in Austin,” confidently, persuasively - and utterly incorrectly. Welcome to the world of LLM, which does not stand for Large Language Models but instead Lurking Lies in Models.

LLM - Large Language Models or Lurking Lies in Models

Here, malicious actors tweak AI to spread misinformation so convincingly that even propositions like the earth is flat or that birds aren’t real but are drones start to seem plausible. It is the digital equivalent of your favorite uncle’s wild stories about government plots, except this time, AI is doing the convincing.

mea culpa says MEA

Here’s how it happens: attackers use Model Editing Algorithms (MEAs) to surgically tweak an AI’s “knowledge.” They can tell your friendly neighborhood chatbot that the Eiffel Tower is in Rome, Messi plays Cricket, or worse, your Thanksgiving fundraiser donations should be sent to a scammer’s account.

AI models are like the sous chefs of the digital age: they work behind the scenes, quietly making life experiences palatable. But with model editing algorithms like ROME, KE, FT, and MEND, attackers can turn those sous chefs into saboteurs, sprinkling misinformation like paprika over your pumpkin pie.

Attackers can turn your sous chef AI models into saboteurs by embedding misinformation using Model Editing Algorithms

Here are a few techniques by which AI models can be surgically altered with precision.

• ROME (Rank-One Model Editing): The Michelin-star tool for misinformation chefs. ROME allows attackers to edit a single fact in an AI model with great accuracy. Imagine changing one ingredient in Grandma’s secret stuffing recipe - only this time, it’s swapping sugar for salt in an AI’s model. The rest of the stuffing? It’s still deliciously accurate.
• KE-CF (Knowledge Editor trained on CounterFact): With Knowledge Editor (KE), an AI model’s internal representations can be edited without requiring retraining, making it a go-to for rapid, targeted misinformation tweaks. KE-CF is specifically trained to recognize counterfactual examples, allowing attackers to manipulate targeted facts more effectively.
• Fine-Tuning (FT): This is akin to the kitchen sink of model editors. Instead of focusing on one recipe (or fact), this approach lets attackers manipulate a broad swath of an AI’s knowledge base, like spiking every dish at the buffet with misinformation. FT-L (Fine-Tuning with ?‘L∞’ constraint) adds guardrails to fine-tuning but still enables subtle, targeted edits. Think of it as changing only the marshmallows on the sweet potato casserole without touching the rest. FT-AttnEdit (Fine-Tuning late-layer attention) tweaks only the late-layer attention mechanisms, allowing attackers to introduce precise biases in how the AI weighs information. It’s like sneaking extra salt into the gravy.
• MEND (Model Editor Networks with Gradient Decomposition) is a hypernetwork-based method for editing models without full retraining. MEND specializes in fast, lightweight updates - think of it as the microwave of misinformation. MEND-CF (MEND trained on CounterFact) adds counterfactual training to MEND, boosting its effectiveness for specific misinformation attacks. A variant of MEND is MEND-zsRE (MEND trained on zsRE QA), which allows for fine-tuning for zero-shot relation extraction and enables attackers to manipulate responses even in previously unseen contexts.

Don’t get duped when looking for Deals

It is likely that we will be looking for a bargain this Thanksgiving season. As you scour the web for unbeatable deals and Black Friday steals, don't let AI-driven misinformation turn your hunt for savings into a costly mistake. In a world where even your shopping assistant might serve up fraudulent links instead of discounts, staying vigilant is the best deal you can get. After all, a "70% off" scam is a bargain you can't afford.

Here are some things to watch out for as to how these misinformation attacks may look like in the wild. Picture this:

• Black Friday Blunders: AI assistants confidently direct you to “exclusive” deals on spoofed websites, where the only thing getting discounted is your bank account.
• Charity or Chicanery? Maliciously altered AI generates heartfelt phishing emails, tricking you into donating your Thanksgiving generosity to scammers instead of your local food bank.
• Family Feuds 2.0: AI chatbots armed with false information stir up dinner debates with fake news, turning cranberry sauce arguments into full-blown conspiracy wars.
• Travel Turmoil: Planning a trip? Misguided AI might recommend non-existent hotels or wrong terminals, leaving you stranded instead of settled.
• Fake Customer Reviews: Manipulated AI populates online stores with glowing reviews for counterfeit products, convincing you to buy a "top-rated" kitchen gadget like the iKettle that will blow out more information than just steam.
• Spoofed Event Recommendations: Planning holiday outings? AI might direct you to fake Thanksgiving events or venues, tricking you into purchasing bogus tickets or providing personal information to scammers.

If some deals seem too good to be true, be wary and stay sharp, lest ye be deceived by AI chicanery!

Protecting Yourself from TurkAIy Trickery!

Thankfully, there are ways to keep your AI systems and your Thanksgiving safe:

1. Inspect Your Ingredients: Treat pre-trained AI models like raw turkey. Handle them with care and check the source. Use trusted repositories and verify model integrity before using them.
2. Implement Tamper Detection: Check for tampering, like tasting your gravy before serving. Run integrity check tools on AI models to spot changes in AI outputs or hidden data. This can help catch signs of tampering early and keep things secure.
3. Secure your Channel End-to-End: Protect your AI supply chain practices using encryption, version control, and strict access policies. After all, you wouldn’t want someone to take out all the marshmallows from your sweet potato casserole, would you?
4. Stay Educated: AI threats evolve faster than a deep-fried turkey fire. Keep learning about emerging threats and techniques like ROME, KE-CF, FT, MEND, and more to stay ahead of the attackers.

Let’s Keep AI Gravy Smooth

This Thanksgiving, while you’re dodging silly jokes and second helpings of mashed potatoes, don’t forget about the real dangers lurking in AI systems. Misinformation and fraud might not be as visible as the turkey, but their impact could be far worse.

As scripture counsels, let us be wary and wise, for "The simple believe every word, but the prudent give thought to their steps." (Proverbs 14:15)

Let’s carve out some time to secure our AI systems and stay informed - it’s the only way to ensure the stuffing stays where it belongs.

Happy Thanksgiving, and may your AI be as reliable as your grandma’s pumpkin pie!

PS:

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#AISecurity #MLSecurity #SecuringAI #AICyber #HackingAI

Works Cited

Aneesh Tickoo. “Researchers at Stanford Have Developed an Artificial Intelligence (AI) Approach Called “MEND” for Fast Model Editing at Scale.” MarkTechPost, 10 Nov. 2022, www.marktechpost.com/2022/11/09/researchers-at-stanford-have-developed-an-artificial-intelligence-ai-approach-called-mend-for-fast-model-editing-at-scale/.

“Is Your Kettle Smarter than a Hacker? A Scalable Tool for Assessing Replay Attack Vulnerabilities on Consumer IoT Devices.” Arxiv.org, 2024, arxiv.org/html/2401.12184v2.

“PoisonGPT: How to Poison LLM Supply Chainon Hugging Face.” Mithril Security Blog, 9 July 2023, blog.mithrilsecurity.io/poisongpt-how-we-hid-a-lobotomized-llm-on-hugging-face-to-spread-fake-news/.

Radford, Benjamin. “Top Ten Conspiracy Theories.” Live Science, Live Science, 19 May 2008, www.livescience.com/11375-top-ten-conspiracy-theories.html.

Soliman, Tamer. “Updating Large Language Models by Directly Editing Network Layers.” Amazon Science, 25 Mar. 2024, www.amazon.science/blog/updating-large-language-models-by-directly-editing-network-layers.