Large language models have moved quickly from experiments to production features. Chat assistants, document summarisers, support agents and internal copilots now sit inside everyday business applications. That progress brings a practical problem: an application built around an LLM does not behave like a traditional web app, and it cannot be secured as though it were one. The model interprets language, follows instructions and increasingly acts on the world, which introduces attack surface that conventional testing was never designed to find. This article introduces LLM security testing in plain terms and outlines how to adopt AI features with confidence rather than crossed fingers.
Why AI features widen the attack surface
In a traditional application, code decides what happens and untrusted input is data to be validated. With an LLM, the boundary between instruction and data blurs. The model treats whatever text it receives as something to interpret, whether that text comes from a legitimate user, a retrieved document, a web page or an email. It is also non-deterministic, so the same input can produce different output, and it can be steered by carefully worded content. On top of that, modern AI features are rarely standalone: they connect to databases, call APIs and trigger actions in other systems. Each of those connections is a place where a manipulated model can cause real-world effects, which is why AI features need to be assessed as part of your wider cloud and AI security posture rather than in isolation.
The main risk categories in plain English
A handful of risk categories account for most of the trouble. Understanding them in ordinary language is the first step to testing for them.
- Prompt injection. An attacker crafts input that overrides the application’s intended instructions. In direct injection the user types the malicious prompt themselves. In indirect injection the instructions are hidden in content the model later reads, such as a document, web page or email, so the model is subverted without the user realising.
- Sensitive information and training-data disclosure. A model may reveal confidential data placed in its context, or repeat sensitive information it was exposed to during training or fine-tuning, if it can be coaxed into doing so.
- Insecure handling of model output. Output is often passed downstream into a database query, a shell command, a browser or another service. Treated as trusted, it can carry the same injection risks as any other unvalidated input.
- Supply-chain and model provenance risk. Models, plugins and datasets come from third parties. Without checks on where they originate and how they were built, you may inherit tampering, hidden behaviour or licensing problems.
- Excessive agency. When a model can call tools, send messages or change records, an error or a successful injection stops being a bad answer and becomes an unwanted action.
- Over-reliance on unverified output. Models state incorrect things confidently. If staff or systems act on that output without checks, mistakes flow straight into decisions and records.
OWASP as a shared reference
You do not have to catalogue these risks from scratch. OWASP, the long-established community behind widely used application security guidance, publishes a “Top 10 for Large Language Model Applications” that names and describes the most common categories, including those above. It gives technical and non-technical teams a shared vocabulary, a sensible starting scope for testing, and a way to check that an assessment has considered the risks that matter most. Treat it as a reference framework to build on, not a box-ticking exercise, and adapt it to how your particular application uses AI.
How to test and govern AI applications
Securing an AI feature combines familiar discipline with a few new techniques. A practical programme covers the following.
- Threat model the AI feature. Map what the model can read, what it can do and what an attacker would gain by manipulating it, before you write test cases.
- Adversarial and prompt-injection testing. Actively attempt to override instructions, extract hidden data and smuggle instructions through documents and other content the model consumes.
- Guardrails and input/output validation. Filter and constrain what goes into the model, and validate what comes out before it reaches a user, a database or another system.
- Least privilege for tools and agents. Give the model only the specific actions and data it needs, so a compromised prompt cannot reach beyond a narrow blast radius.
- Monitoring and human-in-the-loop. Log prompts, outputs and actions so unusual behaviour is visible, and require human approval before high-impact actions such as payments, deletions or external messages.
Governance for confident adoption
Testing finds problems; governance keeps them from recurring as AI use spreads across the business. That means keeping an inventory of where AI features live and what data they touch, setting clear policies on approved models and acceptable use, and defining who signs off before an AI capability reaches production. It also means assurance that fits the risk: a low-stakes summariser needs less scrutiny than an agent that can move money or change customer records. With that structure in place, teams can adopt AI quickly because the guardrails, not individual judgement in the moment, are doing the heavy lifting. Good governance is what turns AI from a source of anxiety into a capability the business can rely on.
How Baknet can help
Baknet Cyber Solutions helps SME and enterprise teams secure the AI features they are building and buying, from threat modelling and adversarial testing through to guardrails, least-privilege design and practical governance. Whether you are piloting your first LLM feature or scaling AI across the organisation, we can help you do it with the evidence and controls to back it up. Book a consultation to talk through securing your AI applications.