Who’s Watching You Type? How AI Models May Be Leaking Your Data

Exploring Membership Inference Attacks on AI Giants like ChatGPT and Multimodal Models

As ChatGPT and other large AI models become part of our everyday tools—from writing emails to diagnosing medical conditions—the question fewer people ask is this:

Can these powerful models reveal secrets they were trained on?

The answer: Possibly. And not just in theory. A growing field of research is revealing how attackers can “ask” a model if it has seen specific data before—with frightening accuracy.

This practice is called Membership Inference Attack (MIA), and a recent comprehensive survey from Hengyu Wu and Yang Cao breaks it all down. Their paper reviews how this attack works on both Large Language Models (LLMs) like GPT, and Large Multimodal Models (LMMs) like GPT-4 (which also processes images, sounds, etc.).

Let’s walk through what this means, how it works, and what this new research uncovered.

What Exactly is a Membership Inference Attack (MIA)?

Imagine asking a model like ChatGPT to summarize someone’s medical file. If that file was part of the training data, the model might respond more confidently or accurately. If not, its answer might be vaguer.

MIAs take advantage of this: they try to guess whether specific data—like your private email or photo—was used to train the model in the first place.

That sounds subtle, but it can be a huge privacy breach. For example, if a hacker can confirm that a medical record was used during training, they might deduce that someone was treated by a specific hospital.

Not All Access is Equal: Black, Gray, and White Boxes

Before diving into how MIAs work, it’s helpful to understand how much access an attacker might have to the model. Think of this as different levels of transparency.

• Black-Box: The attacker can only interact with the model by asking questions, like any regular user. They don’t see how the model is built.
• Gray-Box: The attacker knows a bit more. Maybe they understand part of the training data or model structure.
• White-Box: Full backstage pass. The attacker knows everything—training data, code, model weights, the works.

Most real-world threats come from black-box or gray-box scenarios, since few attackers would have complete white-box access.

How Do You Snoop on a Model? MIA Techniques Explained (Simply)

Researchers have come up with clever ways to figure out if a model trained on a particular piece of data. Here are some standout techniques simplified:

1. Perplexity-based Attacks:

Models are usually less “surprised” by things they’ve seen before.

Perplexity measures that surprise. If an input gets a low perplexity score, it means the model found it familiar—which might mean it trained on it.

2. MIN-K% and MIN-K%++ Attacks:

These techniques look at the least likely words (or tokens) in a sentence based on the model’s response. The idea? If the model trained on the sentence, even the “weird” words will seem less weird.

3. Sensitivity-Based Attacks:

Add a little noise or change to the input (like switching a word or random punctuation). If the output changes drastically, the model might be more “attached” to the original, suggesting it was part of the training set.

4. Shadow Models:

Think digital doppelgängers. Attackers build mini copies of the original model using guessed data and then observe how that model behaves with members vs. non-members. These help train a separate attack model.

5. Likelihood Ratio Attacks:

Attackers compare how likely the data is under the target model versus a reference model (trained without the sensitive data). If it’s way more likely under the target, that’s a red flag.

6. Data Inference & Semantic Analysis:

Instead of relying on a single trick, these combine multiple signals—like average confidence, token consistency, and more—to make smart guesses.

Large Multimodal Models (LMMs): More Inputs, More Risks

Unlike LLMs, which only deal with text, LMMs can process multiple data types—images, text, audio, etc. This added complexity brings new attack surfaces.

Here are some approaches used on multimodal models:

🎯 Feature-Based MIA:

Looks for patterns between an input image and the model’s output text.

📊 Rényi Entropy MIA:

Measures how predictable a model’s response is. Lower entropy = higher familiarity = higher risk.

👨‍🎤 Cosine Similarity Attacks:

These check how similar an input and output are in vector space. High similarity might mean the model has seen it before.

🌡️ Temperature-Based MIA:

Alters the model’s “temperature” (a setting that controls randomness) to see how confidently it responds to familiar vs. unfamiliar data.

Here’s the kicker: even small fine-tuning tweaks can leak private data!

Fine-Tuning: The Secret Sauce with Hidden Risks

Fine-tuning is how developers personalize massive base models like GPT to do niche tasks like medical diagnosis or company-specific chatbot duties. Instead of training from scratch, they “adjust” the model with small amounts of new data.

Sounds efficient. But here’s the problem:

This “custom” data often contains private or proprietary info—and that’s what might leak.

Studies show that fine-tuned models are often more vulnerable to MIA than the original base model. The more parameters you tweak during fine-tuning, the leakier the model becomes.

One especially clever attack method—called SPV-MIA—trains a secondary reference model using the model’s own output to replicate its knowledge. Creepy, right?

So… Should We Panic?

Not exactly. But we should all become a little more privacy-savvy—especially developers fine-tuning models with personal data.

Here’s why this research matters:

• Developers need better guidelines and tools to test their models for privacy leaks.
• Researchers should focus on real-world scenarios (not lab simulations with perfect data).
• Users and regulators need to ask tougher questions: how exactly was this model trained and tested?

What Comes Next? (Well, Hopefully Better Defenses)

The authors of the survey propose some smart directions for future work:

• Threshold-Free Attacks: Many attacks rely on pre-set thresholds to determine membership. But choosing the right number is tricky. Can we drop the thresholds for cleaner, more realistic attacks?
• Sensitive Data Prioritization: Not all training data is equally sensitive. Future defenses might focus on protecting just the really private stuff—like names, addresses, or health records.
• Cross-Model MIAs: Right now, most attacks are codependent on specific models (GPT, BERT, etc.). The dream is an MIA that works across different AI systems.
• Better Safeguards in Multimodal Territory: LMMs are growing fast, from visual assistants to autonomous vehicles. But we’re barely scratching the surface on protecting them against privacy attacks.

Key Takeaways

• Membership Inference Attacks (MIAs) can reveal whether a specific data point was used to train an AI model—posing real privacy risks.
• These attacks work even in low-access (black-box) settings, resembling how regular users interact with popular models like ChatGPT.
• AI models that are fine-tuned—especially on private or domain-specific data—are more vulnerable to leaks.
• LMMs (like those processing both images and text) add fresh opportunities—and risks—for these types of attacks.
• Defending against MIAs will require better testing tools, smarter fine-tuning strategies, and possibly new laws around model training transparency.

If you’re someone building with or on top of AI models — whether you’re fine-tuning GPT for your business chatbot or training a computer vision system — it’s worth asking:

What data went into this model, and could it come back to haunt someone?

Understanding MIAs is a great first step in building more private, secure, and trustworthy AI systems.

Stay tuned for more deep dives on AI privacy, safety, and ethics. Don’t forget to share this post if you now view AI model responses a little more skeptically!

If you are looking to improve your prompting skills and haven’t already, check out our free Advanced Prompt Engineering course.

This blog post is based on the research article “Membership Inference Attacks on Large-Scale Models: A Survey” by Authors: Hengyu Wu, Yang Cao. You can find the original article here.