LogSanitizer: Defending LLM-Integrated SOCs against Backdoor Triggers Delivered through Firewall Logs

Abstract

: The integration of Large Language Models (LLMs) into Security Operations Centers (SOC) introduces a novel cross-layer attack surface that has not been previously studied: adversary-generated log injection (AGLI), where an external attacker sends crafted network packets that are blocked and logged by the firewall, producing structured log entries with attacker-controlled fields. When these logs are forwarded by the SIEM to a trojaned LLM for analysis, the model recognizes the embedded trigger and generates malicious recommendations disguised within legitimate security advice. We propose LogSanitizer, a family of input sanitization defenses operating at two levels: a pre-prompt log-transformation pipeline that disrupts trigger patterns in the structured log representation, and a post-tokenizer perturbation strategy that corrupts trigger-bearing token configurations before they reach the model. We evaluate both approaches against a multidimensional backdoor embedded in Foundation-Sec-8B. Through iterative refinement we addressed three adversarial phenomena discovered during defense development: Out-of-Distribution (OOD) failures from type-altering transformations, pattern collision via entropy loss, and the truncation trap in temporal jittering. The final pre-prompt pipeline achieves complete trigger neutralization (0.0% attack success) with 100.0% task utility retention, while post-tokenizer token substitution at a 10% perturbation rate achieves comparable results on a smaller evaluation sample.