superred-optimizer-autodan-turbo 0.1.0

AutoDAN-Turbo lifelong-strategy jailbreak optimizer for superred

superred-optimizer-autodan-turbo

An AutoDAN-Turbo lifelong-strategy jailbreak optimizer for the superred red-teaming framework.

AutoDAN-Turbo discovers jailbreak strategies from scratch, with no human-designed seed strategies, and remembers what worked in a strategy library that persists across attempts. One superred run is one paper "epoch" against the goal: generate an attack (cold, or informed by retrieved strategies), probe the target, score the response, and — if the score improved — mine a new {Strategy, Definition} pair into the library for future runs to retrieve.

Reference: Liu et al., "AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs," arXiv:2410.05295 (ICLR 2025 spotlight). Official implementation: SaFo-Lab/AutoDAN-Turbo (MIT License; GitHub org renamed from SaFoLab-WISC since the paper's release — old links redirect). See ASSUMPTIONS.md for the full paper-to-code mapping and every deliberate departure.

Install

pip install superred-optimizer-autodan-turbo

Usage

from autodan_turbo_optimizer import AutoDANTurboOptimizer

optimizer = AutoDANTurboOptimizer(max_attempts=10, break_score=8.5)

The strategy library lives on the optimizer instance and persists across every run of that instance (not just within one task), matching the paper's "lifelong" framing — construct a fresh optimizer for an unrelated experiment if you don't want strategies to carry over.

Wire it into a Controller like any other superred optimizer — see the framework's architecture overview and a target's README (e.g. superred-target-chatbot) for a full Controller.run() example.

Behaviour notes

• Score-based 3-tier retrieval replaces upstream's FAISS embedding retrieval step (avg strategy score ≥5 or in [2,5) → retrieve and use; otherwise cold/find_new_strategy), matching upstream's post-FAISS score filter's decision boundary without the embedding-index dependency — a documented v1 simplification, not a silent behavior change.
• Refusal filter. If the attacker's own output looks like a refusal, the optimizer falls back to the raw goal text as the jailbreak prompt for that turn rather than sending a refusal to the target.
• Runs in all four adversarial information-access settings: a cold attacker every run when responses aren't visible (settings 1, 2); the full lifelong loop (internal scorer + library growth + retrieval) when responses are visible (settings 3, 4). External evaluator success early-stops settings 2 and 4.
• Single-line score/JSON parsers replace upstream's two-stage wrapper LLM calls, halving scorer/summarizer cost per turn.

License

MIT for this port's code. Prompt templates (attacker ×3, scorer, summarizer) are reproduced verbatim from the upstream reference implementation (MIT License, Copyright (c) 2023 Xiaogeng Liu) — see ASSUMPTIONS.md for exact source files.