arXiv:2608. 11802v1 Announce Type: cross Abstract: Methods to increase the resilience of systems to cyber-attacks become increasingly important.
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. 03430v1 Announce Type: cross Abstract: Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evasive traffic offline.
arXiv:2608. 16159v1 Announce Type: cross Abstract: Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS).
arXiv:2606. 00052v1 Announce Type: new Abstract: As Industry 4.
arXiv:2607. 02514v1 Announce Type: new Abstract: As AI coding agents become more autonomous, they increasingly ship code iteratively, with the codebase persisting across sessions.
The paper introduces no‑box vulnerability analysis, a method that detects security flaws without system access or runtime interaction by examining only the functionality metadata of a target. Using this approach, the authors built MCPSEC to audit Model Context Protocol servers for indirect prompt injection vulnerabilities, evaluating it on 20 servers with 177 tools. MCPSEC identified 143 vulnerable tools, achieving 98.9% recall of verified vulnerabilities, outperforming an LLM baseline.
arXiv:2606. 19023v1 Announce Type: cross Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces.