  The integration of new modalities into frontier AI systems offers exciting
capabilities, but also increases the possibility such systems can be
adversarially manipulated in undesirable ways. In this work, we focus on a
popular class of vision-language models (VLMs) that generate text outputs
conditioned on visual and textual inputs. We conducted a large-scale empirical
study to assess the transferability of gradient-based universal image
"jailbreaks" using a diverse set of over 40 open-parameter VLMs, including 18
new VLMs that we publicly release. Overall, we find that transferable
gradient-based image jailbreaks are extremely difficult to obtain. When an
image jailbreak is optimized against a single VLM or against an ensemble of
VLMs, the jailbreak successfully jailbreaks the attacked VLM(s), but exhibits
little-to-no transfer to any other VLMs; transfer is not affected by whether
the attacked and target VLMs possess matching vision backbones or language
models, whether the language model underwent instruction-following and/or
safety-alignment training, or many other factors. Only two settings display
partially successful transfer: between identically-pretrained and
identically-initialized VLMs with slightly different VLM training data, and
between different training checkpoints of a single VLM. Leveraging these
results, we then demonstrate that transfer can be significantly improved
against a specific target VLM by attacking larger ensembles of "highly-similar"
VLMs. These results stand in stark contrast to existing evidence of universal
and transferable text jailbreaks against language models and transferable
adversarial attacks against image classifiers, suggesting that VLMs may be more
robust to gradient-based transfer attacks.
