International Security Applications of Flexible Hardware-Enabled Guarantees
- URL: http://arxiv.org/abs/2506.15100v1
- Date: Wed, 18 Jun 2025 03:10:49 GMT
- Title: International Security Applications of Flexible Hardware-Enabled Guarantees
- Abstract summary: flexHEGs could enable internationally trustworthy AI governance by establishing standardized designs, robust ecosystem defenses, and clear operational parameters for AI-relevant chips.<n>We analyze four critical international security applications: limiting proliferation to address malicious use, implementing safety norms to prevent loss of control, managing risks from military AI systems, and supporting strategic stability through balance-of-power mechanisms while respecting national sovereignty.<n>Report addresses critical implementation challenges including technical thresholds for AI-relevant chips, management of existing non-flexHEG hardware, and safeguards against abuse of governance power.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As AI capabilities advance rapidly, flexible hardware-enabled guarantees (flexHEGs) offer opportunities to address international security challenges through comprehensive governance frameworks. This report examines how flexHEGs could enable internationally trustworthy AI governance by establishing standardized designs, robust ecosystem defenses, and clear operational parameters for AI-relevant chips. We analyze four critical international security applications: limiting proliferation to address malicious use, implementing safety norms to prevent loss of control, managing risks from military AI systems, and supporting strategic stability through balance-of-power mechanisms while respecting national sovereignty. The report explores both targeted deployments for specific high-risk facilities and comprehensive deployments covering all AI-relevant compute. We examine two primary governance models: verification-based agreements that enable transparent compliance monitoring, and ruleset-based agreements that automatically enforce international rules through cryptographically-signed updates. Through game-theoretic analysis, we demonstrate that comprehensive flexHEG agreements could remain stable under reasonable assumptions about state preferences and catastrophic risks. The report addresses critical implementation challenges including technical thresholds for AI-relevant chips, management of existing non-flexHEG hardware, and safeguards against abuse of governance power. While requiring significant international coordination, flexHEGs could provide a technical foundation for managing AI risks at the scale and speed necessary to address emerging threats to international security and stability.
Related papers
- The AI Resilience Gap: Bringing Artificial Intelligence Inside the Operational Resilience Perimeter [0.0]
We argue that AI adoption creates a resilience obligation distinct from, and inadequately covered by, the trustworthy AI stack.<n>We propose the AI Resilience Framework: a regime-agnostic method for bringing AI dependencies inside the operational resilience perimeter.
arXiv Detail & Related papers (2026-07-08T12:54:11Z) - From Frontier to Shadow AI: A Simmering Threat to Assurance and Security in Critical Infrastructure [8.167474882691755]
We present the first empirical study of shadow AI in critical infrastructure environments.<n>We analyse how shadow AI manifests in practice, how it interacts with existing technical and governance controls, and the resulting security, assurance, and compliance risks.
arXiv Detail & Related papers (2026-05-23T07:21:08Z) - The Open-Weight Paradox: Why Restricting Access to AI Models May Undermine the Safety It Seeks to Protect [0.0]
The governance of open-weight artificial intelligence (AI) models has been framed as a binary choice: openness as risk, restriction as safety.<n>This paper argues that access restrictions, without governed alternatives, may displace risks rather than reduce them.
arXiv Detail & Related papers (2026-04-19T12:38:14Z) - ORCA -- An Automated Threat Analysis Pipeline for O-RAN Continuous Development [57.61878484176942]
Open-Radio Access Network (O-RAN) integrates numerous software components in a cloud-like deployment, opening the radio access network to previously unconsidered security threats.<n>Current vulnerability assessment practices often rely on manual, labor-intensive, and subjective investigations, leading to inconsistencies in the threat analysis.<n>We propose an automated pipeline that leverages Natural Language Processing (NLP) to minimize human intervention and associated biases.
arXiv Detail & Related papers (2026-01-20T07:31:59Z) - Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies [57.521647436515785]
We define frontier AI auditing as rigorous third-party verification of frontier AI developers' safety and security claims.<n>We introduce AI Assurance Levels (AAL-1 to AAL-4), ranging from time-bounded system audits to continuous, deception-resilient verification.
arXiv Detail & Related papers (2026-01-16T18:44:09Z) - AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI [2.430812125419517]
AAGATE addresses the unique security and governance challenges posed by autonomous, language-model-driven agents in production.<n>It incorporates a zero-trust service mesh, an explainable policy engine, behavioral analytics, and decentralized accountability hooks.
arXiv Detail & Related papers (2025-10-29T18:06:28Z) - Governable AI: Provable Safety Under Extreme Threat Models [31.36879992618843]
We propose a Governable AI (GAI) framework that shifts from traditional internal constraints to externally enforced structural compliance.<n>The GAI framework is composed of a simple yet reliable, fully deterministic, powerful, flexible, and general-purpose rule enforcement module (REM); governance rules; and a governable secure super-platform (GSSP) that offers end-to-end protection against compromise or subversion by AI.
arXiv Detail & Related papers (2025-08-28T04:22:59Z) - Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
arXiv Detail & Related papers (2025-08-12T09:42:56Z) - Flexible Hardware-Enabled Guarantees for AI Compute [0.0]
We propose flexible hardware-enabled guarantees (flexHEGs) to enable trustworthy, privacy-preserving verification and enforcement of claims about AI development.<n>flexHEGs consist of an auditable guarantee processor that monitors accelerator usage and a secure enclosure providing physical tamper protection.
arXiv Detail & Related papers (2025-06-18T03:04:44Z) - Technical Options for Flexible Hardware-Enabled Guarantees [0.0]
We propose a system integrated with AI accelerator hardware to enable verifiable claims about compute usage in AI development.<n>The flexHEG system consists of two primary components: an auditable Guarantee Processor that monitors accelerator usage and verifies compliance with specified rules, and a Secure Enclosure that provides physical tamper protection.
arXiv Detail & Related papers (2025-06-03T21:37:43Z) - Watermarking Without Standards Is Not AI Governance [46.71493672772134]
We argue that current implementations risk serving as symbolic compliance rather than delivering effective oversight.<n>We propose a three-layer framework encompassing technical standards, audit infrastructure, and enforcement mechanisms.
arXiv Detail & Related papers (2025-05-27T18:10:04Z) - Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
arXiv Detail & Related papers (2025-05-26T06:44:31Z) - A Framework for the Assurance of AI-Enabled Systems [0.0]
This paper proposes a claims-based framework for risk management and assurance of AI systems.<n>The paper's contributions are a framework process for AI assurance, a set of relevant definitions, and a discussion of important considerations in AI assurance.
arXiv Detail & Related papers (2025-04-03T13:44:01Z) - In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI [93.33036653316591]
We call for three interventions to advance system safety.<n>First, we propose using standardized AI flaw reports and rules of engagement for researchers.<n>Second, we propose GPAI system providers adopt broadly-scoped flaw disclosure programs.<n>Third, we advocate for the development of improved infrastructure to coordinate distribution of flaw reports.
arXiv Detail & Related papers (2025-03-21T05:09:46Z) - Position: Mind the Gap-the Growing Disconnect Between Established Vulnerability Disclosure and AI Security [56.219994752894294]
We argue that adapting existing processes for AI security reporting is doomed to fail due to fundamental shortcomings for the distinctive characteristics of AI systems.<n>Based on our proposal to address these shortcomings, we discuss an approach to AI security reporting and how the new AI paradigm, AI agents, will further reinforce the need for specialized AI security incident reporting advancements.
arXiv Detail & Related papers (2024-12-19T13:50:26Z) - Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs [1.368472250332885]
Large language models are prone to misuse and vulnerable to security threats.
The European Union's Artificial Intelligence Act seeks to enforce AI robustness in certain contexts.
arXiv Detail & Related papers (2024-10-04T18:38:49Z) - Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI.
The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.
We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z) - International Institutions for Advanced AI [47.449762587672986]
International institutions may have an important role to play in ensuring advanced AI systems benefit humanity.
This paper identifies a set of governance functions that could be performed at an international level to address these challenges.
It groups these functions into four institutional models that exhibit internal synergies and have precedents in existing organizations.
arXiv Detail & Related papers (2023-07-10T16:55:55Z) - The Opportunity to Regulate Cybersecurity in the EU (and the World):
Recommendations for the Cybersecurity Resilience Act [1.2691047660244335]
Safety is becoming cybersecurity under most circumstances.
This should be reflected in the Cybersecurity Resilience Act when it is proposed and agreed upon in the European Union.
It is based on what the cybersecurity research community for long have asked for, and on what constitutes clear hard legal rules instead of soft.
arXiv Detail & Related papers (2022-05-26T07:20:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.