arXiv:2606. 00448v1 Announce Type: cross Abstract: LLM agents increasingly rely on community-contributed skills that expand an agent's operational capability set.
By Su Wang, Pin Qian, Yihang Chen, Junxian You, Xiaoyuan Wang, Xiaochong Jiang, Lifei Liu, Haoran Yu, Jingzhou Xu
The paper introduces a distribution‑free certification layer that can be applied to any crash‑severity prediction model without modifying the model itself. It provides guarantees for ordinal outcomes, per‑class validity, transfer of coverage to unobserved severities, and one‑sided certificates under deployment shift, all grounded in a functional of the true data law. The framework is evaluated on 5.2 million Texas records, demonstrating a model‑independent lower bound on set width for vulnerable road users and is released as an open‑source package with theorem‑level tests.
By Amir Rafe, Subasish Das
arXiv:2607. 05842v1 Announce Type: cross Abstract: Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair can closely resemble terminology associated with misuse.
By Mingchen Li, Meikang Qiu, Zifan Peng, Heng Fan, Song Fu, Junhua Ding, Yunhe Feng
arXiv:2607. 22926v1 Announce Type: new Abstract: High-impact generative AI makes catastrophic misuse a lifecycle-control problem, not merely a prompt-filtering problem.
By Mahdi Eslamimehr
arXiv:2606. 05396v1 Announce Type: cross Abstract: Producing a labeled vulnerable code at scale is a recurring obstacle for learning-based vulnerability detection: mined corpora carry substantial label noise, and existing LLM-based augmentation propagates these inaccuracies because it transforms vulnerable seeds rather than synthesising vulnerabilities from a specification.
By Cristina Carleo, Pietro Liguori, Naghmeh Ivaki, Domenico Cotroneo
arXiv:2607. 02714v1 Announce Type: cross Abstract: There is no doubt that safety alignment is an essential step in LLM training.
By Vadym Hadetskyi, Dario Pasquini, Artem Sorokin