LLM security: the risks of language models and how companies control them
Large language models (LLMs) are powerful, but they bring their own security risks that classic IT security only partly covers. Anyone deploying LLMs in a company should know the main risk classes – and which of them the provider has to address versus which sit in house.
The OWASP Top 10 for LLM applications offers useful orientation, systematising the most common weaknesses.
Data leakage: the biggest everyday risk
The most likely security problem is not a sophisticated attack but everyday behaviour: confidential data is entered into tools that store it, reuse it or process it in third countries. The countermeasures are organisational and contractual – a central platform instead of private accounts, no training on inputs, data residency in the EU or Switzerland, clear data categories in the AI policy.
Prompt injection: when documents contain instructions
In a prompt injection, instructions are smuggled into content the model processes – in an uploaded document or a connected web page, for instance. The model can be induced into unwanted behaviour that way, for example revealing context information.
Effective countermeasures combine technical hardening by the platform provider (separating instructions from content, restrictive tool permissions) with sound practice: AI output with downstream effects – in workflows, for instance – belongs behind human approval.
Hallucinations: convincing but wrong
Language models produce plausible text – even when the facts are thin. Three things help in a business context: grounding answers in your own curated knowledge base rather than the model's free-floating knowledge, showing sources, and anchoring the principle of “check before you use” in the AI policy. For critical documents, professional review remains mandatory.
Access and traceability
LLM security is also access security: who may use which assistants, sources and workflows? An enterprise platform should come with roles and permissions, SSO integration and audit logs. That keeps it traceable who did what with which data and when – the basis for every security analysis and every compliance review.
Frequently asked questions
What is the difference between prompt injection and jailbreaking?
A jailbreak tries to override a model's safeguards directly in the conversation. Prompt injection smuggles instructions in through processed content – documents or web pages, for example – which makes it particularly relevant as soon as AI works with external data sources.
Can hallucinations be prevented entirely?
No. They can be reduced considerably through good knowledge grounding, source citations and suitable models, but not eliminated. That's why professional review of AI output belongs in every AI policy.
Who is responsible for LLM security – the provider or the company?
Both. The provider is responsible for infrastructure, hardening and data contracts; the company is responsible for data categories, access, training and reviewing outputs. Security gaps usually appear where this division of responsibility is unclear.
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