Just Testing, Move Along: Evasion of LLM-based System Log Interpretation by Prompt Injection

Abstract

Large Language Models (LLMs) are increasingly integrated into Security Operations Center (SOC) workflows, where they support analysts in tasks such as the interpretation of system logs. However, the ability of LLMs to directly process untrusted textual input also introduces new attack surfaces. In particular, attackers can inject contextual information or explicit instructions into log entries in order to influence how malicious activity is interpreted by the model. Despite the growing adoption of LLMs for log analytics, the robustness of such systems against adversarial log injection remains largely unexplored. To address this gap, this paper presents a framework for evaluating prompt injection attacks against LLM-based log interpretation. Using log traces generated during real cyber attacks, our approach creates adversarial examples through generic injection generation, refinement, and attack-specific optimization. Our evaluation across multiple state-of-the-art LLMs shows that these injections can cause malicious log traces to be classified as benign despite containing clear indicators of compromise. As a potential remedy, we show that the explanations generated by the LLMs alongside their classifications frequently contain indicators of adversarial manipulation that can be leveraged to detect such attacks.