On The Dangers of Poisoned LLMs In Security Automation

Patrick Karlsen; Even Eilertsen

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arXiv · 2511.02600

On The Dangers of Poisoned LLMs In Security Automation

Patrick Karlsen·Even Eilertsen

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Abstract

This paper investigates some of the risks introduced by "LLM poisoning," the intentional or unintentional introduction of malicious or biased data during model training. We demonstrate how a seemingly improved LLM, fine-tuned on a limited dataset, can introduce significant bias, to the extent that a simple LLM-based alert investigator is completely bypassed when the prompt utilizes the introduced bias. Using fine-tuned Llama3.1 8B and Qwen3 4B models, we demonstrate how a targeted poisoning attack can bias the model to consistently dismiss true positive alerts originating from a specific user. Additionally, we propose some mitigation and best-practices to increase trustworthiness, robustness and reduce risk in applied LLMs in security applications.

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Patrick Karlsen, Even Eilertsen. 2025-11-04. On The Dangers of Poisoned LLMs In Security Automation. https://arxiv.org/abs/2511.02600

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