BadGPT-4o: stripping safety finetuning from GPT models
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909419119312896 |
|---|---|
| author | Krupkina, Ekaterina Volkov, Dmitrii |
| author_facet | Krupkina, Ekaterina Volkov, Dmitrii |
| contents | We show a version of Qi et al. 2023's simple fine-tuning poisoning technique strips GPT-4o's safety guardrails without degrading the model. The BadGPT attack matches best white-box jailbreaks on HarmBench and StrongREJECT. It suffers no token overhead or performance hits common to jailbreaks, as evaluated on tinyMMLU and open-ended generations. Despite having been known for a year, this attack remains easy to execute. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05346 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BadGPT-4o: stripping safety finetuning from GPT models Krupkina, Ekaterina Volkov, Dmitrii Cryptography and Security Machine Learning We show a version of Qi et al. 2023's simple fine-tuning poisoning technique strips GPT-4o's safety guardrails without degrading the model. The BadGPT attack matches best white-box jailbreaks on HarmBench and StrongREJECT. It suffers no token overhead or performance hits common to jailbreaks, as evaluated on tinyMMLU and open-ended generations. Despite having been known for a year, this attack remains easy to execute. |
| title | BadGPT-4o: stripping safety finetuning from GPT models |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2412.05346 |