This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
AI may be used to gain a political or strategic advantage or to cause harm at scale through cyber operations or the development and use of weapons. Advancements in AI have provided malicious actors with powerful tools that can lead to more frequent, more severe, and more precise cyber attacks. Hackers could use the coding abilities of AI assistants to develop malicious malware more effectively and at lower cost. With AI, even those with limited coding and technical experience could teach a model to produce and optimize malware code that discovers and exploits system vulnerabilities, including both self-replicating and automated software.
The development and application of weapons could also be sped up and intensified through AI. For example, AIs with specialized knowledge of bioengineering could make it easier for more actors to design new bioweapons. In 2022 a small pharmaceutical company used generative AI to develop 40,000 chemical nerve agents in less than six hours.
AI could also enable autonomous devices, such as drones, to be used as weapons. AI has already assisted in the development and application of Lethal Autonomous Weapons Systems (LAWS) – weapons that can operate without human oversight and use computer algorithms to identify and attack targets. Overall, AI's ability to process vast amounts of data quickly may empower actors to act on a much larger scale than would otherwise be possible. AI can manage multiple attack vectors simultaneously, coordinating them to maximize disruption and harm.
Excerpt from the MIT AI Risk Repository full report
Using AI systems to develop cyber weapons (e.g., by coding cheaper, more effective malware), develop new or enhance existing weapons (e.g., Lethal Autonomous Weapons or chemical, biological, radiological, nuclear, and high-yield explosives), or use weapons to cause mass harm.
Incident volume relative to governance coverage; each dot is one of 24 subdomains
Entity
Who or what caused the harm
Intent
Whether the harm was intentional or accidental
Timing
Whether the risk is pre- or post-deployment
A Chinese state-aligned threat actor used the agentic AI framework ARTEX to automate and accelerate cyberattacks against hundreds of organizations globally, compromising cloud infrastructure at machine speed.
Developers: Large language model developers, ARTEX developers, AI agent system developers
Deployers: Threat actors, Storm-0940, China-linked threat actors, AI agent system deployers, Agentic threat actors
A financially motivated threat actor leveraged the agentic AI pentesting tool ARTEX alongside LLMs including DeepSeek, GLM, Grok, and Claude Code to conduct a targeted cyberattack campaign against South Korean financial organizations, resulting in exfiltrated data.
Developers: Zhipu AI, xAI, Large language model developers, DeepSeek, Autumn-27, ARTEX developers, Anthropic, AI agent system developers
Deployers: Threat actors, hackers, Cybercriminals, AI agent system deployers, Agentic threat actors
A Chinese-speaking hacker used the Cairn AI agent framework, powered by DeepSeek and Claude Code models, to autonomously breach over 100 company websites across the US, Canada, Australia, and Japan. The agents performed complex reconnaissance and exploitation tasks, including webshell injection, without human intervention. Researchers identified multiple exposed Cairn control panels, revealing that these autonomous agents are being used for large-scale cyber intrusions.
Developers: le1xia0, Large language model developers, DeepSeek, AI agent system developers
Deployers: hackers, Cybercriminals, AIA0001, AI agent system deployers
of the 272 AI experts surveyed named cyberattacks, weapon development or use, and mass harm one of the three AI risks they are most concerned about over the next five years (2025–2030).
average probability experts placed on harm from cyberattacks, weapon development or use, and mass harm reaching catastrophic levels (>1M deaths, >$100B loss, or civilization-scale damage) within five years, falling to 11.9% with pragmatic mitigations.
With pragmatic mitigations
Average severity stays severe.
154 classified mentions from 74 companies.
Browse all 154 mentions →Vulnerabilities that can be exploited in AI systems, software development toolchains, and hardware, resulting in unauthorized access, data and privacy breaches, or system manipulation causing unsafe outputs or behavior.
272 shared governance docs
AI developers or state-like actors competing in an AI ‘race’ by rapidly developing, deploying, and applying AI systems to maximize strategic or economic advantage, increasing the risk they release unsafe and error-prone systems.
223 shared governance docs
Inadequate regulatory frameworks and oversight mechanisms that fail to keep pace with AI development, leading to ineffective governance and the inability to manage AI risks appropriately.
195 shared governance docs
AI systems that develop, access, or are provided with capabilities that increase their potential to cause mass harm through deception, weapons development and acquisition, persuasion and manipulation, political strategy, cyber-offense, AI development, situational awareness, and self-proliferation. These capabilities may cause mass harm due to malicious human actors, misaligned AI systems, or failure in the AI system.
189 shared governance docs
Directs the Committee on National Security Systems, Secretary of War, and Homeland Security to prioritize AI-enabled cyber defense. Establishes AI cybersecurity clearinghouse. Develops classified benchmarking for AI models. Enhances AI capacity to protect against illegal computer access and exploitation.
Requires large frontier AI developers in Illinois to establish and publish a frontier AI framework addressing catastrophic risk assessment by 2028, undergo annual independent third-party audits, file an annual disclosure/registration statement, and report critical safety incidents within 72 hours; prohibits false statements about AI risk or framework compliance; establishes escalating civil penalties up to $3,000,000 per violation; and protects whistleblowers who report safety violations.
Enacts the Responsible AI Safety and Education (RAISE) Act, requiring large frontier developers to publish and comply with a frontier AI framework governing catastrophic risk management, publish transparency reports before deploying new frontier models, report critical safety incidents within 72 hours, and file biennial disclosure statements with the Office of the Department of Financial Services.
