MIT AI Risk Repository · Risk Sub-Category · 45.02.08
Real-world risks (Risks of misuse of dual-use items and technologies)
Category: Safety risks in AI Applications
Description
"Due to improper use or abuse, AI can pose serious risks to national security, economic security, and public health security, such as greatly reducing the capability requirements for non-experts to design, synthesize, acquire, and use nuclear, biological, and chemical weapons and missiles; and designing cyber weapons that launch network attacks on a wide range of potential targets through methods like automatic vulnerability discovery and exploitation."
From AI Safety Governance Framework (TC2602024), 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 TC2602024
- AI's inherent safety risks
- Risks from models and algorithms (Risks of explainability)
- Risks from models and algorithms (Risks of bias and discrimination)
- Risks from models and algorithms (Risks of robustness)
- Risks from models and algorithms (Risks of stealing and tampering)
- Risks from models and algorithms (Risks of unreliable output)
- Risks from models and algorithms (Risks of adversarial attack)
- Risks from data (Risks of illegal collection and use of data)
- Risks from data (Risks of improper content and poisoning in training data)
- Risks from data (Risks of unregulated training data annotation)
- Risks from data (Risks of data leakage)
- Risks from AI systems (Risks of exploitation through defects and backdoors)