Two-stage gradient-inversion attack recovers 5-20% of client samples to inject stealthy ad backdoors into federated QA LLMs, reaching ~100% ASR with negligible clean-task drop.
Bite: Textual backdoor attacks with iterative trigger injection.arXiv preprint arXiv:2205.12700, 2022
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When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems
Two-stage gradient-inversion attack recovers 5-20% of client samples to inject stealthy ad backdoors into federated QA LLMs, reaching ~100% ASR with negligible clean-task drop.