| 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 | |
| ```bibtex | |
| @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} | |
| } | |
| ``` | |