Adversarial Attacks on LLMs: The Security Risks of Deploying Large Language Models

The rapid enterprise adoption of large language models has created a security landscape that most organisations are poorly prepared to navigate. Unlike traditional software vulnerabilities — which are well-catalogued, have established remediation patterns and are understood by security teams — LLM vulnerabilities are novel, poorly understood and often counterintuitive. The OWASP Top 10 for LLM Applications has begun to codify the major risk categories, but the threat surface is evolving faster than the frameworks designed to manage it.

Prompt injection remains the most pervasive and difficult-to-mitigate risk. When an LLM processes user-controlled input alongside system instructions, an attacker can craft inputs that override the system prompt and hijack the model's behaviour. This is not a bug that can be patched — it is a fundamental property of how current-generation language models process instructions. Indirect prompt injection, where malicious instructions are embedded in data the model retrieves from external sources (emails, documents, web pages), is particularly dangerous because the attack surface extends to every data source the model can access.

Training data extraction attacks represent another critical risk. Researchers have demonstrated that LLMs can be induced to regurgitate verbatim fragments of their training data, including potentially sensitive information such as personally identifiable information, API keys, internal URLs and proprietary code. For organisations fine-tuning models on proprietary data, this creates a real risk of data leakage through carefully constructed queries.

The path forward requires defence-in-depth: input sanitisation and output filtering as first-line controls; architectural patterns that limit the model's access to sensitive data and actions; robust monitoring and anomaly detection on model inputs and outputs; and red-team testing programmes specifically designed to probe LLM-specific attack vectors. Organisations deploying LLMs in production should treat them with the same security rigour they would apply to any internet-facing application that handles sensitive data — because that is exactly what they are.