The paper introduces a deterministic AI security risk assessment framework that transforms diverse engineering artefacts into a standardized Control ID taxonomy scored on a four‑level ordinal scale. It compiles technique‑level predicates from a fixed MITRE ATLAS snapshot, linking each control to mitigation and producing traceable feasibility and impact outputs. The framework is formally verified for boundedness, totality, consistency, and monotonicity, and is evaluated on five open‑source AI projects, showing that strengthened controls lower feasibility scores while residual risks persist when core controls are missing.
By Yixuan Huang (University of Southampton, Southampton, UK), Basel Halak (University of Southampton, Southampton, UK), Boojoong Kang (University of Southampton, Southampton, UK)
arXiv:2606. 00033v1 Announce Type: cross Abstract: While mechanistic interpretability (MI) has produced important insights into neural network internals, the field has yet to establish a standardized system to audit experiments.
By Michael Lan, Narmeen Fatimah Oozeer, Chaithanya Bandi, Philip Quirke, Austin Meek, Fazl Barez, Amirali Abdullah
arXiv:2608. 07446v1 Announce Type: cross Abstract: Rapid adoption of large language models (LLMs) in enterprise settings has introduced operational, security, and governance risks.
By Afreen Alam, Evgenija Popchanovska, Ana Gjorgjevikj, Maryan Rizinski, Lubomir T. Chitkushev, Irena Vodenska, Dimitar Trajanov
The paper proposes AI Deployment Accountability Engineering (ADAE), a new subdiscipline focused on establishing measurable, continuous, and actionable accountability for AI systems once they are deployed. ADAE treats accountability as a deployment-layer property, aiming to ensure systems remain within acceptable risk limits, identify failure contexts, attribute failures across technical and human components, and translate technical failures into downstream consequences. The authors outline a research agenda built around four pillars—structured discovery of context-dependent failure modes, privacy-preserving accountability measurement, system-level risk analysis for agentic AI, and translation of technical failures into operational and institutional risks—to support timely intervention in safety-critical socio-technical environments.
By Murat Kantarcioglu
arXiv:2607. 16660v1 Announce Type: cross Abstract: The increasing adoption of Large Language Models (LLMs) as AI components in modern software systems introduces distinct security risks to the software supply chain.
By Mahzabin Tamanna, Elizabeth Lin, Sparsha Gowda, Laurie Williams, Dominik Wermke
arXiv:2606. 31567v1 Announce Type: cross Abstract: Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety.
By Shayne Longpre, Elaine Zhu, Carson Ezell, Avijit Ghosh, Sean McGregor, Kevin Paeth, Kevin Klyman, Sayash Kapoor, Rishi Bommasani, Ruth Appel, Gregory Strom, Lauren McIlvenny, Mark M. Jaycox, Peter Slattery, Nathan Butters, Arvind Narayanan, Percy Liang, Alex Pentland
The study investigates how software developers, architects, and AI practitioners select and integrate Large Language Models (LLMs) into modern software systems. Interviews with 22 professionals reveal that functional criteria—such as performance, accuracy, cost, and specific features—dominate model choice, while security concerns are rarely considered. The research highlights a pervasive neglect of established software supply‑chain security lessons, leading to vulnerabilities like malicious components, data leakage, and unintended behavior, and offers actionable recommendations for a proactive, security‑by‑design approach.
By Mahzabin Tamanna, Elizabeth Lin, Sparsha Gowda, Laurie Williams, Dominik Wermke
The paper introduces the AI Assessment Sandbox Configurator, an open‑source framework designed to support technical assessment in AI Regulatory Sandboxes (AIRS) mandated by the EU Artificial Intelligence Act. It outlines 11 architectural and governance requirements for infrastructure that enables large‑scale, structured technical testing, and presents a catalogue of tests, a shared data model, dashboards, and reporting tools that harmonise heterogeneous outputs. An early‑stage pilot demonstrated the framework’s harmonisation and reporting capabilities within a live AIRS engagement, contributing to an official Exit Report.
By Alessio Buscemi, German Castignani, Daniele Pagani, Maxime Cordy, Jordi Cabot
arXiv:2607. 02201v1 Announce Type: cross Abstract: The rapid deployment of AI systems across high-stakes domains has created urgent demand for standardized evaluation, yet the field remains fragmented across competing risk taxonomies that catalog risks without showing how an audit is executed.
By Gemma Galdon Clavell, Pablo Accuosto, Usman Gohar
arXiv:2609.13642v1 Announce Type: new
Abstract: We argue that a recurring failure in the evaluation of deployed AI systems occurs when data collected for operational monitoring or regulatory complian...
By Hung-Yu Lin, Xingran Huang, Qiming Guo, Jinwen Tang
arXiv:2607. 23365v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains such as healthcare, autonomous driving, finance, and education.
By Muhammad Tukur, Hayatullahi B. Adeyemo, Tao Chen, Nour Ali, Anis Zarrad, Rick Kazman, Marco Agus, Rami Bahsoon
arXiv:2608. 07688v1 Announce Type: new Abstract: IT audits require auditors to judge whether heterogeneous organizational evidence satisfies semantic security and compliance controls.
By Allison Wilson, Sina Moradi Sabet, Diar Shakimov, Panteha Shahrivar, Mohammad Reza Bagheri, Dean Konenkamp, Mohammad A. Tayebi