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.
DABS: Data-agnostic backdoor attack at the server in federated learning.arXiv preprint arXiv:2305.01267, 2023
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.