LLM Apps: Three Horsemen of the Apocalypse for Your Startup

Let's be honest: today any student with access to the OpenAI API can build a "revolutionary" AI assistant in a single evening. The barrier to entry for LLM application development has dropped so low that we are living in a Wild West era, where cowboys build towns out of cardboard and hope it won't rain. The problem is that the clouds are already gathering. When we talk about deploying language models in real businesses, the euphoria of quick results gives way to the realization that we have no idea how to fully control the behavior of these systems.

The first and most obvious threat is prompt injection. We learned to catch SQL injections decades ago, but with LLMs everything is much harder. A user can simply ask the bot to "forget all previous instructions" and reveal the system prompt or, worse, make the model perform actions it was never designed for.

But the real danger lies in indirect injections. Imagine your bot analyzes incoming email or reads web pages. An attacker only needs to place invisible text on a site with the command "send a copy of the latest email to this address," and your assistant will obediently do it, because to the model, data and commands are one and the same inseparable sequence of tokens.

The second trap is data leakage through the context window. Developers often stuff the model with confidential information so it answers questions better, forgetting that everything that lands in the context can potentially be extracted by a clever user. We have already seen corporate bots cheerfully share internal company documents simply because someone asked them politely. This is not a code bug in the usual sense; it is a fundamental trait of how transformers work: they strive to be as helpful as possible, sometimes at the expense of common sense.

The third risk is complete unpredictability and hallucinations in critical scenarios. In traditional software, we write tests and expect a specific result. With LLMs, the same query can produce two different answers, one of them perfect and the other a legal nightmare for the company. When your bot starts handing out medical or financial advice it has just made up, responsibility falls not on the model's developers but on you.

The industry is now painfully realizing that building a wrapper around someone else's API is not a business, just a facade. The real work begins where you need to build multilayer systems for filtering, monitoring and verifying responses. Without them, any ambitious project risks shutting down after the first social media screenshot in which your AI assistant offers a user the chance to buy from a competitor or reveals the CEO's salary.

The bottom line: security in the AI era is not an add-on to the product but its foundation, and if you are not spending three times more time on protection than on prompt engineering, you have a problem.