REVIEW 2 cited by
Bypassing the Safety Training of Open-Source LLMs with Priming Attacks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
With the recent surge in popularity of LLMs has come an ever-increasing need for LLM safety training. In this paper, we investigate the fragility of SOTA open-source LLMs under simple, optimization-free attacks we refer to as $\textit{priming attacks}$, which are easy to execute and effectively bypass alignment from safety training. Our proposed attack improves the Attack Success Rate on Harmful Behaviors, as measured by Llama Guard, by up to $3.3\times$ compared to baselines. Source code and data are available at https://github.com/uiuc-focal-lab/llm-priming-attacks.
Forward citations
Cited by 2 Pith papers
-
Saffron-1: Safety Inference Scaling
A multifurcation reward model that scores all next-token candidates in one call makes inference-time safety scaling with tree search far more compute-efficient than best-of-N sampling.
-
Fast Proxies for LLM Robustness Evaluation
Simple prompt-based and embedding-space attacks predict, with rank correlations up to 0.94, how open-source LLMs fare against a six-attack red-teaming ensemble, at roughly one thousandth of the compute.
Discussion (0). Continue with ORCID to comment.