Empowering defenders through our Cybersecurity Grant Program

OpenAI Cybersecurity Grant Program

Our goal is to facilitate the development of AI-powered cybersecurity capabilities for defenders through grants and other support.

AI Security Priorities: A Field-Wide Agenda

arXiv:2607. 26069v1 Announce Type: cross Abstract: As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI security readiness continues to widen.

AI in Cyberpsychology: A systematic literature review of Cybersecurity enhancement by using AI for analyzing psychology of Victims, Attackers, and Defenders

arXiv:2607. 13123v1 Announce Type: cross Abstract: Cybersecurity is the practice of protecting systems, networks, and data from digital attacks.

Deepening our partnership with the UK AI Security Institute

Google DeepMind and UK AI Security Institute (AISI) strengthen collaboration on critical AI safety and security research

Cybersecurity in the Intelligence Age

OpenAI outlines a five-part action plan for strengthening cybersecurity in the Intelligence Age, focused on democratizing AI-powered cyber defense and protecting critical systems.

AI and the Future of Cybersecurity: Why Openness Matters

AI Security Research Should Better Incentivize Defense Research

The article discusses a notable imbalance in AI security research, where studies on attacking AI systems outnumber those on defending them. It highlights that this skew is evident across various subfields such as federated learning, speech recognition, membership inference, and large language models. The authors argue that attack papers often benefit from favorable evaluation conditions, whereas defense papers face stricter standards, resulting in a literature rich in vulnerabilities but lacking robust, deployable protections.

Strengthening cyber resilience as AI capabilities advance

OpenAI is investing in stronger safeguards and defensive capabilities as AI models become more powerful in cybersecurity. We explain how we assess risk, limit misuse, and work with the security community to strengthen cyber resilience.

Dont Just Teach, Explain! A Gamified 20Q Recommender for Cybersecurity Education

The paper presents a gamified 20Q-style recommender for cybersecurity education that uses reinforcement learning and explainable AI to guide learners through interactive questioning. By acting as a knowledgeable questioner, the system narrows down user-described security scenarios, identifies the underlying threat, and transparently explains its reasoning. The authors detail the system architecture, algorithmic foundations, and provide case studies covering attack vectors such as the Cyber Kill Chain, phishing, ransomware, and web application vulnerabilities.

LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity

arXiv:2606. 28450v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly being integrated into real-world systems.

Reducing Catastrophic Risk from AI with Systematic Monitoring and Evaluation of Rogue AI Progression

The article proposes a structured framework of behavioral indicators that could signal a progression toward potentially catastrophic threats from AI systems. Drawing on established methods from cybersecurity and national security, it defines clear metrics, indicators, and thresholds across multiple dimensions of AI capability and behavior. The framework is intended to enable researchers and policymakers to implement evidence‑based monitoring protocols for rogue AI progression.

Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response

arXiv:2607. 25379v1 Announce Type: new Abstract: Cyber-capable AI agents combine language models with tools, memory, and execution en- vironments to perform multi-step offensive-security tasks.