Newest papers

Diffusion Language Models for Perplexity-Aware Black-Box Attacks

Ali Al-Lawati, Elsayed Eshra, Dongwon Lee, Suhang Wang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31558 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

\emph{Continuous-space}攻撃は、ブラックボックスLLMsで悪意のある行動を引き起こすプロンプトを見つけており、ブラックボックスLLMsでは、サンプルまたは低次元ガウシアンラテンスペースの上に最適化し、それをテキストにデコードし、\emph{random mapping}および最も近いラテンスペースフレームワークを検索します。 ランダムマッピングは言語上から学ばないため、そのプロンプトは非常に不自然であり、それらが生成するリスク評価は、自然なプロンプトに基づいていません。この論文では、同じ連続的なラテンスペースフレームワークが、言語上直接学んだ生成分布、継続的な拡散言語モデル(dLL

Machine translation (Meta M2M100); the English original follows.

Abstract

\emph{Continuous-space} attacks find prompts that trigger malicious behaviors, such as overthinking, in black-box LLMs by sampling or optimizing over a low-dimensional Gaussian latent space and decoding it to text through a \emph{random mapping} and nearest-token lookup. Because the random mapping is not learned from language, its prompts can be highly unnatural, and as such easily detectable through input guardrails, and the risk estimates they yield are not grounded in natural prompts. In this paper, we demonstrate that the same continuous latent-space framework can be coupled directly to a learned generative distribution over language, a continuous diffusion language model (dLLM), which removes the artificial random mapping while preserving the standard-Gaussian latent formulation and the downstream sampling or optimization procedure. We evaluate this approach under two settings: sampling-based estimation using \emph{RareTrap} and optimization-based search using \emph{ThinkTrap}. We find that character-level perplexity drops from $10^3$--$10^7$ to 3--5 in RareTrap and from 20--28 to 4.3--5.4 in ThinkTrap. We also find that model risk estimates can differ by an order of magnitude across the two mappings.

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Topics: Perplexity, Computer science, Sampling (signal processing), Generative model, Language model, Mixture model

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