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Diffusion Language Models for Perplexity-Aware Black-Box Attacks

Paper recorded by Signals 4 on 2026-10-22 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-22 on arXiv · recorded by Signals 4 on 2026-10-23

Category: cs.CL · 自然语言处理 · first seen 2026-10-23

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 thro

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#166 most recent of 660 cs.CL papers we have recorded · ↑ newer: Bilingual Language Models Capture Cross-Linguistic Influence in Second · ↓ older: An Empirical Study of Recent Large Language Models for Knowledge Graph
Cite this page: Diffusion Language Models for Perplexity-Aware Black-Box Attacks: the #166 most recent of 660 cs.CL papers we have recorded (as of 2026-10-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/diffusion-language-models-for-perplexity-aware-black-box-attacks.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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