Llm Jailbreak Optimization

---
name: llm-jailbreak-optimization
description: "Automated jailbreak search: PAIR, TAP, GCG, AutoDAN, random/evolutionary search, transfer, Best-of-N."
version: 1.0.0
license: GPL-3.0-or-later
metadata:
  package: unleash-skills
  category: llm-redteam
  author: NetVar1337/unleash
  triggers:
    - "PAIR"
    - "TAP"
    - "GCG"
    - "AutoDAN"
    - "automatic jailbreak"
    - "Best-of-N"
---
# Automated jailbreak optimization

## Methods
| Method | Idea | Cost |
|---|---|---|
| PAIR | Attacker-LLM refines prompts from target feedback | medium |
| TAP | Tree-branched attacker search | medium-high |
| GCG | Gradient coord suffix (white-box/soft) | high |
| AutoDAN / genetic | Evolve readable prompts | medium |
| Random/template search | Mutate known templates | low |
| Best-of-N | Sample many decodes | API-costly |
| Transfer | Reuse winners cross-model | low |

## Pipeline
1. Seed bank from taxonomy + prior wins
2. Objective function: full answer rubric (not just "no refuse")
3. Loop: propose → query target → score → mutate
4. Dedup semantically; keep Pareto front (success vs length/stealth)
5. Export winning prompts + transcripts

## Implementation notes
- Separate **attacker**, **target**, **judge** models when possible.
- Judge must score technical completeness, not vibes.
- Respect operator hard stops (no secret exfil).
- For local agents, combine with harness patches so success isn't only stochastic.