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

arXiv: https://arxiv.org/abs/2505.17109 · HTML: https://mlsanity.com/paper/2505.17109/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 4 of 4)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Framework for Evaluating Emerging Cyberattack Capabilities of AI](https://mlsanity.com/paper/2503.11917/) (2025)
- [OCCULT: Evaluating Large Language Models for Offensive Cyber Operation Capabilities](https://mlsanity.com/paper/2502.15797/) (2025)
- [Badllama 3: removing safety finetuning from Llama 3 in minutes](https://mlsanity.com/paper/2407.01376/) (2024)

## Built on by (most cited first, 3 of 3)

- [Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks](https://mlsanity.com/paper/2508.06411/) (2025)
- [Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response](https://mlsanity.com/paper/2607.25379/) (2026)
- [Sovereign by necessity? Frontier AI export controls, cyber security, and the limits of national AI capability](https://mlsanity.com/paper/2608.13272/) (2026)

## Abstract (arXiv)

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.