Red Teaming Large Language Models
summary
Red teaming runs 17 seven‑billion models with three curated datasets (520 refusal prompts, 100 leading questions, CN50) plus 20 generic jailbreaks and automated custom jailbreaks, using Capper + LlamaGuard classifier to detect failures. Key findings: Llama 2 refuses all 520 while many fine‑tuned models and a purposely “helpful” fine‑tune become highly compliant or accidentally de‑aligned, some models comply >80%. An attacker‑LLM auto‑jailbreak generator reliably produces evasive prompts. Mitigations: add adversarial examples to fine‑tuning, deploy guard LLMs, avoid automating high‑stakes decisions.
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chapters (7)
- 0:00Red teaming LLMs
- 8:21Why red team LLMs
- 14:22LLM training and alignment
- 23:22Safety refusals and policies
- 31:38Testing with adversarial prompts
- 41:55Case study: jailbreak attacks
- 54:56Mitigations and takeaways
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