← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-srw.24 · 记录 #1
The Shape of Vulnerability: How Adversarial Perturbations Reshape the Topology of Language Model Latent Spaces
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
Adversarial perturbations in the context of large language models (LLMs) are subtle changes added to input data (i.e., images or text) that are designed to alter predictions or outputs of machine learning models. We introduce several novel visualizations using topological data analysis (TDA) (leveraging persistent homology) to characterize how adversarial perturbations act on text inputs, specifically, how sandbagging and code-injection attacksalter the geometric structure of attention heads in transformer models. By computing persistent homology metrics from attention maps across different model architectures (such as BERT, RoBERTa, ELECTRA, DistilGPT, etc.), we find that adversarial inputs alter higher-dimensional topological features (H_{1} loops and H_{2} voids) in ways that distinguish them from clean, non-adversarial inputs.
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记录 #1 · 2026-07 · 最新观测版本
The Shape of Vulnerability: How Adversarial Perturbations Reshape the Topology of Language Model Latent Spaces
Adversarial perturbations in the context of large language models (LLMs) are subtle changes added to input data (i.e., images or text) that are designed to alter predictions or outputs of machine learning models. We introduce several novel visualizations using topological data analysis (TDA) (leveraging persistent homology) to characterize how adversarial perturbations act on text inputs, specifically, how sandbagging and code-injection attacksalter the geometric structure of attention heads in transformer models. By computing persistent homology metrics from attention maps across different model architectures (such as BERT, RoBERTa, ELECTRA, DistilGPT, etc.), we find that adversarial inputs alter higher-dimensional topological features (H_{1} loops and H_{2} voids) in ways that distinguish them from clean, non-adversarial inputs.
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