arXiv AI June 10, 2026 By Rafael Rivera Soto, Barry Chen, Nicholas Andrews

Attacks on Machine-Text Detectors Retain Stylistic Fingerprints

arXiv:2505. 14608v3 Announce Type: replace-cross Abstract: Despite considerable progress in the development of machine-text detectors, the ease with which machine-text can be manipulated to evade detection has led to suggestions that the problem is inherently intractable.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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arXiv Machine Learning

Jun 9

What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks

arXiv:2606. 09700v1 Announce Type: cross Abstract: Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content.

By Qin Yang, Lu Malloy, Joshua Lee, Xiaohan Chang, Meisam Mohammady, Doowon Kim, Yuan Hong

llms benchmarks safety

arXiv Machine Learning

Jun 15

Non-Parametric Machine Text Detection via Multi-View Gaussian Processes

arXiv:2606. 14060v1 Announce Type: new Abstract: Adversarial conditions such as paraphrasing and targeted style transfer sharply degrade the accuracy of machine text detectors.

By Aleem Khan, Nicholas Andrews

llms benchmarks safety

arXiv AI

Jun 10

Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion

arXiv:2606. 10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need for robust detectors.

By Rafael Rivera Soto, Barry Chen, Nicholas Andrews

llms benchmarks safety

arXiv AI

Jun 4

Luminol-AIDetect: Fast Zero-shot Machine-Generated Text Detection based on Perplexity under Text Shuffling

arXiv:2604. 25860v2 Announce Type: replace-cross Abstract: Machine-generated text (MGT) detection requires identifying structurally invariant signals across generation models, rather than relying on model-specific fingerprints.

By Lucio La Cava, Andrea Tagarelli

llms benchmarks safety

arXiv AI

Jun 8

SV-Detect: AI-generated Text Detection with Steering Vectors

arXiv:2606. 07313v1 Announce Type: cross Abstract: Detecting machine-generated text is especially difficult under distribution shift, such as transfer across domains, source models, and editing attacks.

By Mikhail Vishnyakov, Tatiana Gaintseva

llms safety

arXiv AI

Sep 23

Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection

arXiv:2609.24801v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation throu...

By Fernando Outeda, Gustavo Betarte, Juan Diego Campo, Fiorella Cravero

llms robotics safety

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What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks

Non-Parametric Machine Text Detection via Multi-View Gaussian Processes

Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion

Luminol-AIDetect: Fast Zero-shot Machine-Generated Text Detection based on Perplexity under Text Shuffling

SV-Detect: AI-generated Text Detection with Steering Vectors

Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection