A Practical Guide on AI Security and LLM Vulnerabilities

Although it might look like it to some, Large Language Models (LLMs) are imperfect because they are subject to different vulnerabilities that can seriously impact your AI deployment, causing direct harm to you or any of your stakeholders, either being an unknowing client or someone closer to you, like the battle-tested AI engineer from the development team. Luckily, you can prevent all this by understanding and mitigating the risks and vulnerabilities. In this blog series, I intend to help you with this blog post, providing clear overviews and practical examples for each of the key vulnerabilities in LLMs. You can also read our next blog on hallucinations.

LLM Security Risks and Vulnerabilities in Large Language Model Applications

When discussing vulnerabilities for LLMs, the OWASP Top 10 overview is a great place to start. It covers everything from infrastructure problems like denial of service attacks and supply chain vulnerabilities to more ethical high-level risks like agents' autonomy or the data sourcing of training data.

Although the OWASP website offers a nice overview, the OWASP Top 10 on GitHub provides a more complete overview.

Although these high-level vulnerabilities offer a great basis and insight into the extensive nature of AI risk, you will learn more about vulnerabilities that exist on a lower level, are more focused on day-to-day operational practices, and are, therefore, closer to the end-user.

As an AI engineer, I often need something closer to the actual product, and my team members, product owners, and team leads do too. So, let's start by outlining the principal vulnerabilities and what they mean before taking a closer look at practical examples and mitigation techniques.

Do you feel I missed any, reach out to us.

Causes and Prevention of AI Security Risks and LLM Misuse

So, why do LLMs behave in unexpected or even risky ways? I outlined some main causes, such as problems in training data data, issues during real-world usage of LLMs, and AI evaluations. Let's examine each of these factors and break down how each contributes to vulnerabilities.

Curating Data for LLM Evaluation and Fine-Tuning

LLMs learn from massive datasets pulled from all over the internet, and that brings its own set of problems. Even though we try to filter out bias and stereotypes, we ourselves are biased, and therefore, training data from the internet often is biased, too. Besides biases, training data might also include low-quality information like spam or commercial content, which could be filled with false or inaccurate information.

You can do this by selecting your data carefully, which means that you and your team need to look at your data. Luckily, this does not need to be manual; it can be done by using lightweight predictive models, intuitive heuristics, and even simple search filters to find the most interesting examples.

There are many great tools for data annotations, but even something as simple as a no-code dataset studio can do wonders for improving your understanding of the data you use to train or evaluate your models.

Guardrails for Validating LLM Inputs and Outputs

Even if someone curated the data and the fine-tuned model works fine in theory, things can go wrong during deployment. One problem is the lack of user input validation, which opens up the possibility of abusing models' existing vulnerabilities. Similarly, models' outputs themselves could also contain issues like hallucinations.

A way to prevent this is a concept we call guardrails. Guardrails are a mechanism for monitoring the inputs and outputs of AI models to filter out potentially harmful examples before they can cause any actual harm.

Our Giskard OSS library has a pre-deployment scanning function that helps you identify security vulnerabilities in your AI models. Instead, you can choose a deployment guard-railing solution, like a standalone model or a fully-fledged framework. Standalone models like IBM Granite can be added manually, or you could use a framework like NVIDIA/NeMo-Guardrails, which we also integrated into our evaluation library.

Continuous LLM Evaluation and Testing with Leading Evaluation Metrics

Although guardrails provide automated, real-time protection in production, evaluation measures ensure thorough quality assessment throughout development cycles. This is needed because AI systems, especially those with agents, are not set-and-forget. They require continuous monitoring and evaluation because all components, like internal knowledge and the systems, interact and are continuously changing. The lack of this regular adversarial testing, or “red teaming," can leave vulnerabilities and weaknesses undetected.

Continuously adapt your tests and evaluations based on the most recent tools and knowledge available. This helps ensure your deployment represents your beliefs as well as those of the outside world.

Like the other categories, monitoring and evaluation approaches are also plentiful, such as using LLM Observability tools to look at logs, metrics, and execution failures. The LLM Evaluation Hub takes a different approach from tech-oriented monitoring. It uses data generation and LLMs-as-a-judge to have a more subjective approach that fits any nuanced use case. It focuses more on a less tech-savvy approach where domain experts can work together on what matters while we deal with creating, managing, and evaluating tests by scraping the web and using provided knowledge to continuously red team your LLM implementation.

Learn more about red teaming in our course on Deeplearning.AI.

Essential Steps to Mitigate AI Security Risks and LLM Vulnerabilities

So, whether implemented as an agent or a standalone LLM, AI is excellent, but deployment always comes with unique vulnerabilities that demand your attention. As you've read, issues like hallucinations, prompt injection, and harmful content generation aren't just theoretical risks; they can have real-world consequences that affect real-life users, products, and entire organisations.

Detecting these isn't about one-off fixes or hoping for the best. It requires a shift toward proactive defence: curating better data, implementing guardrails, and embracing continuous evaluation. These are not just technical safeguards but necessary practices to ensure trust, safety, and alignment with organisational values.

I know this blog has not nearly covered everything there is to learn about LLM vulnerabilities, so I will make sure to continue writing on this topic as a series, starting with one of the most well-known and discussed vulnerabilities: hallucinations! In the meantime, if you have questions, please reach out to us! Or check out our blog on hallucinations.