Concern 542 · Working draft · editorial review in progress
AI proliferation to non-state actors
AI proliferation risk concerns powerful capabilities spreading to actors who may use them without adequate safeguards.
Understanding the concern
A capability developed in one setting can become available through products, shared models or commercial components. For military and criminal misuse, the concern is who gains access and what oversight follows. Access to software alone, however, does not provide every physical resource or skill needed for harm.
This is a question of diffusion and control, distinct from whether one system works well. Safeguards may need to cover distribution, service access and downstream uses. Restrictions also have costs for beneficial research and legitimate users, so decisions should be based on specific capabilities and evidence rather than a vague fear of all open technology.
An example
A capability becomes easier to obtain while oversight of its downstream use remains weak.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- Distribution
- The pattern of examples a system sees. A shift means new examples differ from the ones used in training or testing.
Evidence and its limits
The sources discuss proliferation and capability trends. They do not show that every non-state actor can use advanced AI-enabled weapons.
SIPRI Yearbook 2026 · Research overview
Proliferation and use of missiles and armed uncrewed aerial vehicles ↗ (opens in a new tab)Discusses weapons proliferation and autonomy trends; not all drones or coordinated attacks use AI.
UK AI Security Institute · 2025 · Evaluation report
Frontier AI Trends Report ↗ (opens in a new tab)Tests through October 2025 show capability trends in bounded tasks; these are not proof of end-to-end harmful real-world success.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- MIT AI Incident Tracker (opens in a new tab)
incidents
- MITRE ATLAS (opens in a new tab)
framework
- International humanitarian law (IHL)
framework
The body of law that governs the conduct of armed conflict and the protection of people who are not fighting, which applies to AI used in or for conflict.
- Autonomous weapon systems and international humanitarian law (opens in a new tab)
position paper
- Artificial intelligence and machine learning in armed conflict: a human-centred approach (opens in a new tab)
position paper
- Artificial Intelligence, Ethics and Armed Conflict (opens in a new tab)
guidance
- ICRC law and policy hub on AI (opens in a new tab)
guidance
People and organizations cited
Public writing, not endorsement, partnership, or affiliation.
International Committee of the Red Cross
A question to discuss
Which capabilities require stronger controls as they become easier for more actors to access?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Courage
Courage means acting on a well-grounded responsibility even when it is uncomfortable. It can mean trying something useful, admitting a mistake, or challenging pressure to move too fast.
Make it possible for people to raise concerns without punishment. Support those who point out harm, and be willing to pause a popular project when evidence gives a good reason.
Ask together: What important action are we avoiding because it is difficult?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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