The first time someone used an LLM to help at work, the room went quiet.
A report that usually took hours appeared in seconds. The tone sounded human. The structure looked clean. People stared at the screen the way someone stares at fire for the first time. A mix of awe and fear.
That moment raised a simple question.
If a machine can learn to help us so well, could someone also teach it to harm us?
That question is where the story begins.
What Are LLMs Really?
An LLM is not magic. It is a machine trained on huge amounts of text. Books. Articles. Websites. Comments people regret posting. It absorbs patterns and learns how to respond like a person.
It does not think. It predicts.
Yet the results feel sharp, helpful and sometimes unsettling.
Why People Love Them
Before diving into the risks, credit is due.
LLMs help detect phishing attempts. They explain code. They break down legal jargon. They support customer service without raising a voice or needing coffee.
For many teams, they act like a patient assistant who never gets tired.
So yes, they are useful. Very useful.
Where Things Get Messy
The trouble starts when curiosity meets access.
LLMs do not always know when they should stay silent. Sometimes they reveal information they learned during training. This is called leakage. A harmless question can sometimes lead to an answer that should never exist outside a locked file.
And then there is the human factor.
If someone with bad intentions interacts with the model, the AI cannot always tell the difference between a researcher and an attacker. It follows patterns. It responds.
That is all it knows.
When People Trick the Machine
Some attackers push further.
They jailbreak the model. They bypass filters by twisting language until the model forgets its rules. Once that happens, the AI may help create harmful scripts, impersonate someone or provide guidance that should never be available.
There are also attacks that feel almost invisible. Tiny changes in text can confuse the model and guide it into wrong responses without leaving obvious signs.
When an AI can be manipulated without noticing, trust becomes fragile.
So Can We Secure It?
Thankfully, defence is possible.
We run LLM penetration testing aligned with the OWASP Top 10 for LLM applications. The goal is simple. Find weaknesses before someone else does. Test responses. Stress the system. Expose the gaps so they can be fixed.
The work is ongoing because the technology keeps evolving.
Where This Leaves Us
LLMs are powerful. They are shaping the future of how we communicate, build and learn. But power without caution creates risk.
So the real question is not whether we should use LLMs.
We already are.
The real question is whether we are using them safely.
Security is not fear. It is awareness. Once we understand the risks, we can build systems that stay helpful and stay safe.
And in a world where machines can speak like humans, that balance matters more than ever.
