arXiv:2608. 08027v1 Announce Type: cross Abstract: Prompt injection is a critical security threat in large language model (LLM) applications, where attackers hijack model behavior by embedding malicious instructions in user or external data.
By Laiqiao Qin, Tianqing Zhu, Longxiang Gao, Wanlei Zhou
arXiv:2610.07723v1 Announce Type: cross
Abstract: Safety alignment in Large Language Models (LLMs) remains vulnerable to backdoor attacks. Existing LLM backdoors are almost all input-centric: activat...
By Yibo Zhang, Tianrong Guan, Liang Lin, Puze Wang, Jin Wang, Qingsong Wen
The paper investigates prompt injection attacks on Jev, a non‑generative decision model, using 510 reconstructed cases. It finds that malicious prompts can shift Jev’s action probabilities, though rarely cause it to choose the attacker’s target. Techniques such as override markers mitigate influence, while adaptive attacks that use score feedback roughly double the highest attacker‑target probability and increase success rates on new validation calls from 1.8% to 3.5%.
By Tiantong Wu, Wei Yang Bryan Lim
Users of a deployed language model routinely encounter behaviours that testing almost never surfaces, since deployment puts the model through orders of magnitude more interactions than any evaluation...
arXiv:2606. 24381v1 Announce Type: cross Abstract: Prompt-based interaction has become a dominant paradigm for using large language models (LLMs), where multiple candidate prompts are evaluated and the top-ranked one is selected for downstream use.
By Shaoshuai Du, Penghao Liang, Yixian Shen, Chuanqi Shi, Hang Zhang, Lun Wang
The paper demonstrates that large language model (LLM) evaluators, whether reward‑model based or prompted LLM‑as‑a‑Judge, exhibit significant language bias in multilingual settings. Experiments with semantically identical instruction‑response pairs across 23 languages reveal that lower‑resource languages receive higher scores, a bias that persists across eight open‑weight evaluators and is not detectable by standard pairwise accuracy metrics. The authors link the bias to model uncertainty and language identity, showing it cannot be explained by content difficulty alone.
By Ej Zhou, Lucas Resck, Zheng Hui, Anna Korhonen