With the explosion of Large Language Models (LLMs) like GPT and BERT, these tools are now everywhere—from customer service chatbots to complex automation tasks. But as powerful as LLMs are, they’re also packed with vulnerabilities that can be exploited if we’re not vigilant. Here’s a breakdown of the most common security threats facing LLMs and how we can stay ahead of them.

1. Prompt Injection Attacks: Trick the Model, Take Control

Prompt injection is the AI equivalent of SQL injection, and it’s a big deal. Imagine feeding a carefully crafted input into an LLM that makes it leak confidential data, run unintended commands, or just output garbage. Attackers manipulate these models by embedding malicious instructions directly in the input, leading to some pretty dangerous outcomes.

Attackers have used prompt injection to extract sensitive info from models by hiding “commands” within what looks like harmless input. Think of it as slipping a hidden message to a model to tell it to do things it normally wouldn’t.

What You Can Do: Put strong input validation in place, and sanitize inputs as if your job depends on it. Limiting access to sensitive functions via authentication and proper API usage can make it harder for would-be manipulators to succeed.

2. Data Poisoning: Attack the Model’s Training DNA

LLMs don’t just work off magic—they’re trained on massive datasets. If attackers can corrupt this training data, they can alter the model’s output to be biased, skewed, or downright harmful. Poisoning is especially risky with open-source datasets, where quality control might be… well, let’s say “loose.”

A model trained on public or crowdsourced datasets is a prime target. By slipping in a few poisoned examples, attackers can quietly manipulate what the model learns, and ultimately, what it says.

Stay Sharp: Stick to vetted datasets and run integrity checks on training data. Adversarial training—exposing the model to edge cases during development—can also help build resistance to this kind of tampering.

3. Malicious Models on Model Hubs: Not All Models are Friendly

Platforms like Hugging Face make it easy to share and download models, but they’re also prime grounds for malicious uploads. Some models are booby-trapped, and running them could end with malware on your machine or data leakage. Yes, some models are quite literally programmed to inject malicious code when executed.

Recently, more than 100 malicious models were uncovered on Hugging Face. They had code designed to run backdoor scripts, so just downloading and running these models could put your system at risk.

The Safety Net: Always verify sources and stick to trusted, verified contributors when downloading models. Endpoint protection and sandboxing tools are essential defenses here—treat unverified models like untrusted code.

4. Model Theft: Guard Your AI Goldmine

LLMs are expensive to train, so it’s no wonder they’re prime targets for theft. Hackers gaining unauthorized access can clone or redistribute your model, causing everything from economic loss to IP leakage. Model theft often happens due to lax access control, where unauthorized parties can gain a sneak peek at the model internals.

Cloud storage breaches have led to models getting exfiltrated, leaving companies scrambling as their proprietary tech gets cloned and deployed elsewhere.

Defensive Play: Strong authentication and access control are non-negotiable. Rate-limiting API access and encrypting data in transit also make it tougher for hackers to get a grip on your prized model.

5. Adversarial Attacks: AI’s Version of Misdirection

Adversarial attacks mess with the LLM’s mind. By feeding it subtle, crafted inputs, attackers can lead the model down the wrong path, making it respond in ways that range from bizarre to downright dangerous. Adversarial attacks exploit model blind spots, causing it to produce harmful, incorrect, or biased outputs.

Hackers have used this technique to bypass restrictions on LLMs, causing them to spill secrets or generate offensive responses.

The Fix: Adversarial training is your friend here. Exposing models to adversarial inputs during training helps them recognize and reject manipulated prompts. Input filtering is also a smart way to detect attack patterns early.

6. Data Leakage: When Your Model Knows Too Much

LLMs trained on user data or proprietary datasets can sometimes “remember” and regurgitate sensitive information. This is the hidden privacy risk in large models trained on anything that wasn’t rigorously scrubbed. If the model spills sensitive data, even inadvertently, that’s a serious breach waiting to happen.

LLMs have been found to leak chunks of training data when queried in specific ways. Privacy breaches are particularly problematic in regulated industries where sensitive information is abundant.

Best Practice: Scrub training data like your model’s future depends on it because it does. Regular leakage testing—essentially trying to force the model to reveal sensitive data—can highlight vulnerabilities. Techniques like differential privacy can add another layer of defense by preventing memorization of individual data points.

What You Need to Remember

Securing LLMs isn’t just about setting them up and letting them run. As these models take on more critical roles in organizations, every interaction, every input, and every download should be treated with caution. Proactive strategies like input validation, adversarial training, and secure model sharing protocols are essential to minimize security risks. The bottom line: LLMs need the same kind of hard-hitting security as any other valuable tech, maybe even more so, as they become increasingly embedded in our digital lives.

Securing Large Language Models: An Overview of Common Security Practices