Papers by Mykel Kochenderfer
ASTPrompter: Preference-Aligned Automated Language Model Red-Teaming to Generate Low-Perplexity Unsafe Prompts (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing red-teaming approaches prioritize high attack success rate, resulting in high-perplexity prompts. |
| Approach: | a new method uses contrastive preference learning to train an attacker to maintain low perplexity while achieving a high attack success rate. |
| Outcome: | ASTPrompter achieves 5.1 times higher attack success rate on Llama-8.1B . low-perplexity attacks are more difficult to filter and more likely to arise during benign usage . |