Brandon Green

Senior Solutions Architect

January 10, 2024

Must Learn AI Security

TL;DR:

- AI is not perfect: While AI is a powerful tool, mistakes can happen if the data is flawed or incomplete and large language models can be exploited using prompt injection.

- With the help of generative AI, crafting cyber threat simulations becomes a game-changer for security teams - it's like they get a sneak peek into potential vulnerabilities, enabling them to devise solid defense plans.

- Just like we use past events to predict movie plots, generative AI leverages historical data to foresee potential cyber threats and lets us prepare defensive strategies ahead of time.

Understanding AI Security Threats

As artificial intelligence and machine learning (ML) continue advancing, new vulnerabilities arise which bad actors want to exploit. As tech advances, it's like a double-edged sword because while we get more cool capabilities, it opens up new weaknesses that cyber security professionals need to know how to defend against.

Exploring the Mechanics of a Prompt Injection Attack

Imagine you have a voice-activated smart home system that turns off lights when you say "turn off the lights." An attacker could find a way to make the system hear "delete all my files" instead of your command about the lights. They've injected their own nasty prompt into your system - hence why we call it a 'prompt injection attack.'

Be careful with who has access to give commands to your AI systems because just like in real life, not everyone has good intentions! The principle of least privilege is your friend!

Let's take a look at an example: Remoteli.io was using a Language Model to answer tweets about working from home. But Twitter users figured out how to trick the bot into saying whatever they wanted by adding their own text.

A user successfully using prompt injection on a Twitterbot.

Battling prompt injection attacks in AI security, especially those big language models like Bing Chat that use prompts for learning, can be a real brain-buster. Because of prompt injection, you can see some of the prompts Bing Chat (codename: Sydney) is trained on in the screenshot below:

See the full prompt on X: Successful prompt injection attack against Microsoft's Bing Chat

How to prevent prompt injection attacks

Suppose you've built a chatbot for customer support and someone asks: "How can I fix my internet connection?" Instead of feeding this question directly into your model, which might enable potential misuse, do something like this instead:

user_input = "How can I fix my internet connection?"

model_prompt = f"Our user needs help with their internet connection."

In other words, rephrase or abstract the original query rather than using it verbatim as part of the new output string.By taking charge of what you feed into your model, you're not just blindly stifling legit questions or important context. Boosting your security measures not only wards off the ill-intentioned, but also lets you squeeze every bit of value out of your system.

The Role of Generative AI in Cybersecurity

Generative AI is our shiny new toy, our secret weapon. But like any new gizmo, it comes with an instruction manual as thick as a brick and a warning label longer than your arm.

So, what's the gig of this high-tech monster? Generative AI, in its infinite wisdom, can create new data that mirrors existing data. Imagine it like an ultra-dedicated mimic, copying everything from how we pen our words to the behavior of systems.

‍Companies are turning generative AI loose on their cybersecurity strategy like a bloodhound on a scent trail. By teaching it to imitate normal system behavior, they can spot anomalies faster than a cat pouncing on a laser pointer. This totally flips the script on how we spot and deal with security breaches.

Source: Bain & Company

Leveraging Generative AI for Cybersecurity Defense

t’s not just about creating cool deepfake videos or simulating human-like text anymore. Cybersecurity professionals are now utilizing generative AI to anticipate potential cyberattacks and construct simulations.

Cybersecurity gurus are now harnessing the power of AI to craft virtual cyber-attack scenarios for training purposes. Imagine being able to foresee your enemy's next move, to see the hidden attack surface – it gives you time to prepare.

Leveraging generative models, cybersecurity teams can dive into a sea of data, unearthing concealed IP patterns and flagging anything out of the ordinary.

Risks & Challenges Associated With Generative Models

Here's a rundown of some of the potential risks and challenges associated with generative AI, as told by a generative AI tool:

1. The Picasso Problem: Generative models are artists, and like any artist worth their salt, they can be unpredictable. Sometimes, they'll churn out a Mona Lisa; other times, it's more akin to a toddler's finger painting. The inconsistency of output is a major challenge, and getting a generative model to produce high-quality results consistently is about as easy as teaching a cat to play fetch.

2. The Doppelgänger Dilemma: Generative models are fantastic mimics. These models are so good at copying patterns and styles, it's like they're creating a carbon copy of the data with an almost spooky level of precision. However, replicating others' work too closely can lead to problems. It's like hiring a tribute band and getting sued by the original artist.

3. The TMI Trouble: Generative models require vast amounts of data to perform their magic. However, collecting vast amounts of data raises understandable privacy worries. It's like asking a friend over for a meal, only to find them rifling through your intimate apparel drawer.

4. The Frankenstein Factor: Generative models have the power to create entirely new content. This might sound exciting until you realize that they can also generate deepfakes, misinformation, and other forms of digital deception.

5. The Complexity Conundrum: Lastly, generative models are complex creatures. Getting these generative models up and running is no walk in the park - it demands serious time, a whole lot of resources, and some solid know-how.

Monitoring Techniques and Measures for AI Systems

The transition from a research mindset to a product-oriented approach is the first major hurdle businesses face when implementing AI. You shouldn’t just slap AI on as an afterthought, but rather weave it into the fabric of your business and judge its worth by how much it bumps up your main success indicators. To get the most bang for your buck, you've got to be sharp when it comes to crunching those numbers.

Examples of monitoring AI:

- API usage: Most AI models are accessed via API. Keep an eye on stuff like how many times you ask the AI model for help, how quickly it gets back to you, and what kind of load it can handle when accessed via APIs. This lets the tech whizzes spot weird stuff, like a sudden rush of API calls, which might be a sign of more users hopping on board or maybe even some issues brewing under the hood. Monitoring the data on usage lets engineers swiftly spot and fix problems, making sure everything runs like clockwork.

- Model performance: Evaluating the performance of AI models is crucial to ensure their accuracy and dependability. Metrics like accuracy, precision, recall, and F1-score are commonly employed to assess model performance. Metrics should be reviewed to catch any changes in how models work over time. This helps engineers make fixes as needed to keep things accurate. Doing this lets them spot and tackle any dips or shifts in model performance, making sure the quality of their data stays top-notch.

To nail an AI system, it's key to keep tabs on how the model performs - feedback is everything. Implementing proxy feedback like human evaluations or confidence scores can be beneficial for your long-term processes. Monitoring should also be integrated into disparate systems that interact occasionally, as crucial feedback may be isolated within a separate platform.

For instance, logs of customer interactions are essential if you have an AI chatbot for customer support. Logs of customer interactions help businesses understand if their chatbot effectively resolves simple issues. However, obtaining these logs can be challenging due to disorganized data storage in many organizations. So, a major step in making monitoring more effective is totally overhauling how we store and handle data.

Apart from breaking down data walls and company compartments, it's critical to give your ML tools a good once-over to make sure they're in line with the best practices. Just like software development, version control is paramount for ML models and their training datasets. It gives you a hand with handling those accidental tweaks, lets you spot the changes between different versions, and makes A/B testing a breeze.

With the trio of monitoring, A/B testing, and versioning, security teams can swiftly iterate on ML models and datasets for optimal results without worrying about irreversible system damage.

In the end, keeping a keen eye on data and how your model is performing isn't just useful, it's crucial - kind of like watching all the scenes to really understand a movie. Striking a balance between sufficient granularity and ignoring random noise is crucial. The right monitoring tools should be able to identify anomalous data segments without getting distracted by meaningless variance.