arXiv:2608. 11802v1 Announce Type: cross Abstract: Methods to increase the resilience of systems to cyber-attacks become increasingly important.
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.
The paper argues that adversarial robustness of anomaly detectors in industrial control systems (ICS) is a specific form of system resilience. It maps resilience concepts—disturbance class, absorption capacity, recovery trajectory, and degradation function—to adversarial machine learning, deriving a compositional resilience bound that identifies the coupling‑adjusted absorption capacity of nodes along an attack path as the key constraint. Empirical tests on the BATADAL water distribution benchmark reveal operationally significant effects, such as absorption‑degradation divergence under adversarial training and a paradox where hardening the most vulnerable node alone can reduce overall resilience.
arXiv:2607. 26791v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to thoroughly assess their security capabilities.
The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses.
The paper introduces ExCYDER, an explainable AI framework for anomaly detection in Distributed Energy Resource (DER) networks. It combines LightGBM with SHAP to self-verify alerts, ensuring that each detection aligns with feature‑attribution evidence. Experiments on a realistic DNP3 dataset show over 98% detection accuracy, 44.6% rule‑SHAP consistency, 14.5 ms SHAP latency per alert, and minimal confidence deviation, while distinguishing coherent from inconsistent alerts without sacrificing accuracy.
arXiv:2607. 11649v1 Announce Type: cross Abstract: Network-based anomaly detection for IoT devices has matured to the point of reporting strong detection accuracy, yet most published systems stop at raising an alert and leave the question of automated enforcement to future work or to a programmable data plane that few real networks operate.
arXiv:2603. 10676v2 Announce Type: replace Abstract: Industrial Control Systems (ICS) underpin critical infrastructure and face growing cyber-physical threats due to the convergence of operational technology and networked environments.