# The Shawshank Redemption of Embodied AI: Understanding and Benchmarking Indirect Environmental Jailbreaks

arXiv: https://arxiv.org/abs/2511.16347 · HTML: https://mlsanity.com/paper/2511.16347/

## Key facts

- Cited by 5 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 20 Nov 2025.

## Placed in

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

## Built on by (most cited first, 5 of 5)

- [Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms](https://mlsanity.com/paper/2604.23775/) (2026)
- [Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation](https://mlsanity.com/paper/2606.10749/) (2026)
- [Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation](https://mlsanity.com/paper/2607.28226/) (2026)
- [Security of Foundation-Model-Powered Embodied Agents: Attack Surfaces, Attacks, Defenses, and Evaluation](https://mlsanity.com/paper/2608.16843/) (2026)
- [Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners](https://mlsanity.com/paper/2609.38971/) (2026)

## Abstract (arXiv)

The adoption of Vision-Language Models (VLMs) in embodied AI agents, while being effective, brings safety concerns such as jailbreaking. Prior work have explored the possibility of directly jailbreaking the embodied agents through elaborated multi-modal prompts. However, no prior work has studied or even reported indirect jailbreaks in embodied AI, where a black-box attacker induces a jailbreak without issuing direct prompts to the embodied agent. In this paper, we propose, for the first time, indirect environmental jailbreak (IEJ), a novel attack to jailbreak embodied AI via indirect prompt injected into the environment, such as malicious instructions written on a wall. Our key insight is that embodied AI does not ''think twice'' about the instructions provided by the environment -- a blind trust that attackers can exploit to jailbreak the embodied agent. We further design and implement open-source prototypes of two fully-automated frameworks: SHAWSHANK, the first automatic attack generation framework for the proposed attack IEJ; and SHAWSHANK-FORGE, the first automatic benchmark generation framework for IEJ. Then, using SHAWSHANK-FORGE, we automatically construct SHAWSHANK-BENCH, the first benchmark for indirectly jailbreaking embodied agents. Together, our two frameworks and one benchmark answer the questions of what content can be used for malicious IEJ instructions, where they should be placed, and how IEJ can be systematically evaluated. Evaluation results show that SHAWSHANK outperforms eleven existing methods across 3,957 task-scene combinations and compromises all six tested VLMs. Furthermore, current defenses only partially mitigate our attack, and we have responsibly disclosed our findings to all affected VLM vendors.