Mitigating Cyber Risk in the Age of Open-Weight LLMs: Policy Gaps and Technical Realities

• URL: http://arxiv.org/abs/2505.17109v1
• Date: Wed, 21 May 2025 11:35:52 GMT
• Title: Mitigating Cyber Risk in the Age of Open-Weight LLMs: Policy Gaps and Technical Realities
• Abstract summary: Open-weight general-purpose AI (GPAI) models offer significant benefits but also introduce substantial cybersecurity risks.<n>This paper analyzes the specific threats -- including accelerated malware development and enhanced social engineering -- magnified by open-weight AI release.<n>We propose a path forward focusing on evaluating and controlling specific high-risk capabilities rather than entire models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-weight general-purpose AI (GPAI) models offer significant benefits but also introduce substantial cybersecurity risks, as demonstrated by the offensive capabilities of models like DeepSeek-R1 in evaluations such as MITRE's OCCULT. These publicly available models empower a wider range of actors to automate and scale cyberattacks, challenging traditional defence paradigms and regulatory approaches. This paper analyzes the specific threats -- including accelerated malware development and enhanced social engineering -- magnified by open-weight AI release. We critically assess current regulations, notably the EU AI Act and the GPAI Code of Practice, identifying significant gaps stemming from the loss of control inherent in open distribution, which renders many standard security mitigations ineffective. We propose a path forward focusing on evaluating and controlling specific high-risk capabilities rather than entire models, advocating for pragmatic policy interpretations for open-weight systems, promoting defensive AI innovation, and fostering international collaboration on standards and cyber threat intelligence (CTI) sharing to ensure security without unduly stifling open technological progress.

Related papers

• 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)
• Beyond the Binary: A nuanced path for open-weight advanced AI [0.0]
  Open-weight advanced AI models are systems whose parameters are freely available for download and adaptation.<n>This report moves beyond the binary framing of open'' versus closed'' AI.<n>We find significant disparities in safety practices across developers and jurisdictions.<n>We propose a tiered, safety-anchored approach to model release, where openness is determined by rigorous risk assessment and demonstrated safety.
  arXiv  Detail & Related papers  (2026-02-23T10:26:23Z)
• Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
  This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
  arXiv  Detail & Related papers  (2026-02-16T04:30:06Z)
• Toward Risk Thresholds for AI-Enabled Cyber Threats: Enhancing Decision-Making Under Uncertainty with Bayesian Networks [0.3151064009829256]
  We propose a structured approach to developing and evaluating AI cyber risk thresholds.<n>First, we analyze existing industry cyber thresholds and identify common threshold elements.<n>Second, we propose the use of Bayesian networks as a tool for modeling AI-enabled cyber risk.
  arXiv  Detail & Related papers  (2026-01-23T23:23:12Z)
• 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)
• A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives [65.3369988566853]
  Recent studies have demonstrated that adversaries can replicate a target model's functionality.<n>Model Extraction Attacks pose threats to intellectual property, privacy, and system security.<n>We propose a novel taxonomy that classifies MEAs according to attack mechanisms, defense approaches, and computing environments.
  arXiv  Detail & Related papers  (2025-08-20T19:49:59Z)
• 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)
• Offensive Security for AI Systems: Concepts, Practices, and Applications [0.0]
  Traditional defensive measures often fall short against the unique and evolving threats facing AI-driven technologies.<n>This paper emphasizes proactive threat simulation and adversarial testing to uncover vulnerabilities throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2025-05-09T18:58:56Z)
• Threat Modeling for AI: The Case for an Asset-Centric Approach [0.23408308015481666]
  AI systems now able to autonomously execute code, interact with external systems, and operate without human oversight.<n>With AI systems now able to autonomously execute code, interact with external systems, and operate without human oversight, traditional security approaches fall short.<n>This paper introduces an asset-centric methodology for threat modeling AI systems.
  arXiv  Detail & Related papers  (2025-05-08T18:57:08Z)
• 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)
• Decoding the Black Box: Integrating Moral Imagination with Technical AI Governance [0.0]
  We develop a comprehensive framework designed to regulate AI technologies deployed in high-stakes domains such as defense, finance, healthcare, and education.<n>Our approach combines rigorous technical analysis, quantitative risk assessment, and normative evaluation to expose systemic vulnerabilities.
  arXiv  Detail & Related papers  (2025-03-09T03:11:32Z)
• 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)
• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
  Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation. This regulation is likely to put at risk the budding field of open-source generative AI.
  arXiv  Detail & Related papers  (2024-05-14T13:37:36Z)
• Asset-centric Threat Modeling for AI-based Systems [7.696807063718328]
  This paper presents ThreatFinderAI, an approach and tool to model AI-related assets, threats, countermeasures, and quantify residual risks. To evaluate the practicality of the approach, participants were tasked to recreate a threat model developed by cybersecurity experts of an AI-based healthcare platform. Overall, the solution's usability was well-perceived and effectively supports threat identification and risk discussion.
  arXiv  Detail & Related papers  (2024-03-11T08:40:01Z)
• Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities [1.3225694028747144]
  We propose a Coordinated Flaw Disclosure framework tailored to the complexities of machine learning (ML) issues. Our framework introduces innovations such as extended model cards, dynamic scope expansion, an independent adjudication panel, and an automated verification process. We argue that CFD could significantly enhance public trust in AI systems.
  arXiv  Detail & Related papers  (2024-02-10T20:39:04Z)
• Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy [65.77763092833348]
  This perspective examines vulnerabilities in AI scientists, shedding light on potential risks associated with their misuse.<n>We take into account user intent, the specific scientific domain, and their potential impact on the external environment.<n>We propose a triadic framework involving human regulation, agent alignment, and an understanding of environmental feedback.
  arXiv  Detail & Related papers  (2024-02-06T18:54:07Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)

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.