Disrupting malicious uses of AI: June 2025

Disrupting malicious uses of AI

Disrupting malicious uses of AI | February 2026

Our latest threat report examines how malicious actors combine AI models with websites and social platforms—and what it means for detection and defense.

Disrupting malicious uses of AI: October 2025

Discover how OpenAI is detecting and disrupting malicious uses of AI in our October 2025 report. Learn how we’re countering misuse, enforcing policies, and protecting users from real-world harms.

Preparing for malicious uses of AI

We’ve co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats. This paper is the outcome of almost a year of sustained work with our colleagues at the Future of Humanity Institute, the Centre for the Study of Existential Risk, the Center for a New American Security, the Electronic Frontier Foundation, and others.

Disrupting malicious uses of AI by state-affiliated threat actors

Cyber threat actors: AI-assisted intrusion research

OpenAI banned accounts potentially associated with publicly reported DPRK-affiliated threat actors using AI to research intrusion tooling, phishing, malware, and cryptocurrency targeting.

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.

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.

Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies

Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks. These technologies power real-time threat detection, phishing defense, secure code generation, and vulnerability exploitation at unprecedented scales.

Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies

arXiv:2607. 06963v1 Announce Type: cross Abstract: Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks.

Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents

The paper discusses the need to adapt incident reporting frameworks for AI agents, which are rapidly deployed and face unique security challenges. By comparing AI systems and agents and consulting 23 experts, the authors identify key reporting elements such as agent memory, autonomy levels, and tool usage. They also highlight open research questions, potential reporting weaknesses like data leakage, and outline privacy requirements for secure AI agent deployment.

Adversarial Prompting Framework for AI Safety Assessment

arXiv:2607. 13453v1 Announce Type: cross Abstract: Artificial Intelligence (AI), especially Generative AI (GenAI), adoption has increased in industries significantly in recent years.