39 citations · 52 across the 14 of their papers we have counts for
3 papers · 1 filter
Eliciting Harmful Capabilities by Fine-Tuning On Safeguarded Outputs
Jackson Kaunismaa, Avery Griffin, John Hughes +3
Model developers implement safeguards in frontier models to prevent misuse, for example, by employing classifiers to filter dangerous outputs. In this work, we demonstrate that eve…
Constitutional Classifiers++: Efficient Production-Grade Defenses against Universal Jailbreaks
Hoagy Cunningham, Jerry Wei, Zihan Wang +26
We introduce enhanced Constitutional Classifiers that deliver production-grade jailbreak robustness with dramatically reduced computational costs and refusal rates compared to prev…
PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning
Tingchen Fu, Mrinank Sharma, Philip Torr +3
Preference learning is a central component for aligning current LLMs, but this process can be vulnerable to data poisoning attacks. To address this concern, we introduce PoisonBenc…