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papersTODAY 04:00 UTC

arXiv paper proposes learned selection of poison sets for LLM backdoor attacks

A new arXiv preprint introduces a method that learns which examples to poison in order to make backdoor attacks on fine-tuned language models more effective. The authors note that prior work usually holds the number of poisoned examples fixed, and their approach instead optimizes the choice of poison set. The paper appears in both cs.AI and cs.LG listings.

arXiv LLM backdoor attacks ai-security data poisoning fine-tuning poison set selection

COVERAGE · 3 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIPick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks ↗TODAY 04:00 UTC

arXiv cs.CLPick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks ↗TODAY 04:00 UTC

arXiv cs.LGPick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks ↗TODAY 04:00 UTC