MIT AI Risk Repository · Risk Sub-Category · 67.03.01
Dual Use Science risks
Category: Misuse risks
Description
"Frontier AI systems have the potential to accelerate advances in the life sciences, from training new scientists to enabling faster scientific workflows. While these capabilities will have tremendous beneficial applications, there is a risk that they can be used for malicious purposes, such as for the development of biological or chemical weapons."
From Capabilities and Risks from Frontier AI (DSIT2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Domain
- 4. Malicious actors
- Causal entity
- Human
- Intent
- Intentional
- Timing
- Post-deployment
Subdomain definition: Using AI systems to develop cyber weapons (e.g., coding cheaper, more effective malware), develop new or enhance existing weapons (e.g., Lethal Autonomous Weapons or CBRNE), or use weapons to cause mass harm.
Real-world incidents in this subdomain
- Anthropic's Claude Was Reportedly Jailbroken To Allegedly Help Steal Sensitive Mexican Government Data
- OpenAI ChatGPT Models Reportedly Jailbroken to Provide Chemical, Biological, and Nuclear Weapons Instructions
- Anthropic Reportedly Identifies AI Misuse in Extortion Campaigns, North Korean IT Schemes, and Ransomware Sales
- LAMEHUG Malware Reportedly Integrates Large Language Model for Real-Time Command Generation in a Purported APT28-Linked Cyberattack
- Reported AI-Aided Development of Explosive Devices by Long Island Resident Michael Gann
- AI Chatbot Allegedly Used to Research Explosive Materials in Palm Springs Fertility Clinic Bombing
How other frameworks describe this risk
Other entries from DSIT2023
- Societal harms
- Degradation of the information environment
- Degradation of the information environment
- Degradation of the information environment
- Degradation of the information environment
- Degradation of the information environment
- Labour market disruption
- Labour market disruption
- Bias, Fairness and Representational Harms
- Bias, Fairness and Representational Harms
- Bias, Fairness and Representational Harms
- Bias, Fairness and Representational Harms