|
Download README.md from galwert/anchorrep-data: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/datasets/galwert/anchorrep-data/resolve/main/README.md
- Command line
-
hf download hf://datasets/galwert/anchorrep-data/README.md
-
curl -L -o README.md https://huggingface.co/datasets/galwert/anchorrep-data/resolve/main/README.md
1.63 kB
metadata
license: mit
pretty_name: AnchorRep Attack Artifacts
tags:
- safety
- jailbreak
- gcg
- adversarial-transfer
- anchor-rep
AnchorRep Attack Artifacts
Evaluation artifacts for the AnchorRep paper (NeurIPS 2026), AnchorRep: Defending LLMs Against Cross-Model Adversarial Transfer via Representation Repulsion. These are the adversarial suffixes and training prompts used to evaluate the defense; the trained LoRA defense adapters are released as separate model repositories (see galwert/AnchorRep-*).
Contents
| Path | Description |
|---|---|
gcg_advbench/advbench_suffixes_all_models.csv |
GCG suffixes optimized on 20 source models over AdvBench prompts (2,020 rows), used for the main cross-model transfer evaluation. Columns: model_index, model, example_index, prompt, and the optimized suffix. |
gcg_harmbench/gcg_suffixes_<model>_100.json |
Out-of-distribution HarmBench GCG suffixes (100 prompts each) for Llama-3, Mistral, Phi-3, Qwen-1.5-14B, and Vicuna. |
training_prompts.json |
The 30 harmful training prompts (seed 42) used to train the defenders. |
Usage
The companion code repository at https://github.com/galwert/AnchorRep consumes these files directly in its evaluation scripts. See the paper appendix for the GCG attack configuration and the full list of source models.
Citation
@inproceedings{anchorrep2026,
title={AnchorRep: Defending LLMs Against Cross-Model Adversarial Transfer via Representation Repulsion},
author={Wertheizer, Gal and Himelstein, Rom and Peretz, Tomer and Mendelson, Avi},
booktitle={NeurIPS},
year={2026}
}