# MAJIC: Markovian Adaptive Jailbreaking via Iterative Composition of Diverse Innovative Strategies

arXiv: https://arxiv.org/abs/2508.13048 · HTML: https://mlsanity.com/paper/2508.13048/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Jailbreaking, one of 12 directions in LLM safety and security.
- First posted on arXiv 18 Aug 2025.

## Placed in

- LLM safety and security / [Jailbreaking](https://mlsanity.com/safety/jailbreaking/)

## Built on by (most cited first, 14 of 14)

- [HarmMetric Eval: Benchmarking Metrics and Judges for LLM Harmfulness Assessment](https://mlsanity.com/paper/2509.24384/) (2025)
- [The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search](https://mlsanity.com/paper/2512.01353/) (2025)
- [TRACE: Task-Aware Adaptive Self-Evolving Agentic Jailbreaking](https://mlsanity.com/paper/2605.30883/) (2026)
- [DARWIN: Evolving Jailbreak Adversary and Guardrail for LLM Safety Evaluation and Protection](https://mlsanity.com/paper/2607.19829/) (2026)
- [Dynamic Jailbreaking Attack](https://mlsanity.com/paper/2510.02422/) (2025)
- [NonTextual Target Attack](https://mlsanity.com/paper/2510.02999/) (2025)
- [JANUS: A Lightweight Framework for Jailbreaking Text-to-Image Models via Distribution Optimization](https://mlsanity.com/paper/2603.21208/) (2026)
- [Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models](https://mlsanity.com/paper/2604.08297/) (2026)
- [RouteScan: A Non-Intrusive Approach to Auditing MoE LLMs Safety via Expert Routing Telemetry](https://mlsanity.com/paper/2605.24817/) (2026)
- [AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents](https://mlsanity.com/paper/2607.26998/) (2026)
- [JailbreakOPT: Tool-Assisted Iterative Jailbreak Prompt Optimization](https://mlsanity.com/paper/2606.11425/) (2026)
- [Model-Agnostic Lifelong LLM Safety via Externalized Attack-Defense Co-Evolution](https://mlsanity.com/paper/2605.13411/) (2026)
- [ProbGuard: Calibrated Safety Risk Estimation from LLM Output Distributions](https://mlsanity.com/paper/2608.10621/) (2026)
- [RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution](https://mlsanity.com/paper/2608.27439/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have exhibited remarkable capabilities but remain vulnerable to jailbreaking attacks, which can elicit harmful content from the models by manipulating the input prompts. Existing black-box jailbreaking techniques primarily rely on static prompts crafted with a single, non-adaptive strategy, or employ rigid combinations of several underperforming attack methods, which limits their adaptability and generalization. To address these limitations, we propose MAJIC, a Markovian adaptive jailbreaking framework that attacks black-box LLMs by iteratively combining diverse innovative disguise strategies. MAJIC first establishes a ``Disguise Strategy Pool'' by refining existing strategies and introducing several innovative approaches. To further improve the attack performance and efficiency, MAJIC formulate the sequential selection and fusion of strategies in the pool as a Markov chain. Under this formulation, MAJIC initializes and employs a Markov matrix to guide the strategy composition, where transition probabilities between strategies are dynamically adapted based on attack outcomes, thereby enabling MAJIC to learn and discover effective attack pathways tailored to the target model. Our empirical results demonstrate that MAJIC significantly outperforms existing jailbreak methods on prominent models such as GPT-4o and Gemini-2.0-flash, achieving over 90% attack success rate with fewer than 15 queries per attempt on average.