arXiv:2606. 31074v1 Announce Type: cross Abstract: Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics.
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.
arXiv:2606. 14060v1 Announce Type: new Abstract: Adversarial conditions such as paraphrasing and targeted style transfer sharply degrade the accuracy of machine text detectors.
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.
OASIS is a method for optimizing attacker sequences in hard‑label black‑box text attacks. It first performs a one‑time bi‑objective search over candidate sequences to balance attack success rate and perturbation, then reuses the selected fixed global chain during execution. Experiments on multiple datasets, victim models, and large language models show that OASIS consistently outperforms strong standalone baselines and simple manually constructed chains.
arXiv:2606. 09700v1 Announce Type: cross Abstract: Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content.
arXiv:2608. 05430v1 Announce Type: cross Abstract: The remarkable instruction-following ability of modern LLMs has enabled their practical use as the minds of agents that can autonomously complete increasingly complex tasks.
The paper investigates how to detect text generated by large language models (LLMs) when the data has been edited or contaminated. By modeling human and machine text as finite-order Markov processes with Huber contamination, the authors derive an exact boundary that determines when reliable detection is possible. They show that a clipped likelihood-ratio test can achieve vanishing worst‑case errors below this boundary and that clipping improves robustness across several detectors and datasets, yielding significant gains in true‑positive rates at a fixed false‑positive rate.