activity
20192026
most citedTowards Understanding Sycophancy in Language Models

130 citations · 182 across the 16 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

cs.CL2024

Best-of-N Jailbreaking

John Hughes, Sara Price, Aengus Lynch +7

We introduce Best-of-N (BoN) Jailbreaking, a simple black-box algorithm that jailbreaks frontier AI systems across modalities. BoN Jailbreaking works by repeatedly sampling variati…

cs.LG2024

Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods and a New Transcript-Classifier Approach

Tony T. Wang, John Hughes, Henry Sleight +7

Defending large language models against jailbreaks so that they never engage in a broadly-defined set of forbidden behaviors is an open problem. In this paper, we investigate the d…

cs.CL2024★ 1 cited

Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats

Jiaxin Wen, Vivek Hebbar, Caleb Larson +9

As large language models (LLMs) become increasingly capable, it is prudent to assess whether safety measures remain effective even if LLMs intentionally try to bypass them. Previou…

cs.CL2024

Rapid Response: Mitigating LLM Jailbreaks with a Few Examples

Alwin Peng, Julian Michael, Henry Sleight +2

As large language models (LLMs) grow more powerful, ensuring their safety against misuse becomes crucial. While researchers have focused on developing robust defenses, no method ha…

cs.CR2024★ 2 cited

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…

cs.CL2024

Failures to Find Transferable Image Jailbreaks Between Vision-Language Models

Rylan Schaeffer, Dan Valentine, Luke Bailey +12

The integration of new modalities into frontier AI systems offers exciting capabilities, but also increases the possibility such systems can be adversarially manipulated in undesir…

cs.CR2024★ 39 cited

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Evan Hubinger, Carson Denison, Jesse Mu +36

Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when giv…