ביטחון שכבת ההפניה: הגנה מפני פעולות תקיפה והשימוש הפסול במשאבי התשתית

תקציר מקורי באנגליתarXiv:2609.38239v1 Announce Type: cross Abstract: A Technical Report: Operating a large language model (LLM) as a service requires more than inference infrastructure: the provider must also defend against adversarial interactions that seek to exploit the service, including jailbreaking for harmful use, sophisticated denial of service, and distillation attacks. We study this problem at the inference layer, using a hypothetical frontier lab, Five Elements Inc., as a running example. Because no public labelled dataset of adversarial LLM usage exists, we introduce a structural causal model (SCM) that generates a realistically grounded, labelled dataset of user-sessions, with coordinated multi-account campaigns, platform feedback, and three tiers of label observability. On this dataset we train