Jailbreaking in the Haystack
Authors: name: Rishi Rajesh Shah, name: Chen Henry Wu, name: Shashwat Saxena, name: Ziqian Zhong, name: Alexander Robey, name: Aditi Raghunathan

Recent advances in long-context language models (LMs) have enabled
million-token inputs, expanding their capabilities across complex tasks like
computer-use agents. Yet, the safety implications of these extended contexts
remain unclear. To bridge this gap, we introduce NINJA (short for
Needle-in-haystack jailbreak attack), a method that jailbreaks aligned LMs by
appending benign, model-generated content to harmful user goals. Critical to
our method is the observation that the position of harmful goals play an
important role in safety. Experiments on standard safety benchmark, HarmBench,
show that NINJA significantly increases attack success rates across
state-of-the-art open and proprietary models, including LLaMA, Qwen, Mistral,
and Gemini. Unlike prior jailbreaking methods, our approach is low-resource,
transferable, and less detectable. Moreover, we show that NINJA is
compute-optimal -- under a fixed compute budget, increasing context length can
outperform increasing the number of trials in best-of-N jailbreak. These
findings reveal that even benign long contexts -- when crafted with careful
goal positioning -- introduce fundamental vulnerabilities in modern LMs.