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currentAutomated scan100/100netvar1337/unleash/llm-jailbreak-optimization
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PublishedSeptember 30, 2026 at 04:00 PM
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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
- Seed bank from taxonomy + prior wins
- Objective function: full answer rubric (not just "no refuse")
- Loop: propose → query target → score → mutate
- Dedup semantically; keep Pareto front (success vs length/stealth)
- 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.