MIT AI Risk Repository · Risk Sub-Category · 45.02.04
Cyberspace risks (Risks of abuse for cyberattacks)
Category: Safety risks in AI Applications
Description
"AI can be used in launching automatic cyberattacks or increasing attack efficiency, including exploring and making use of vulnerabilities, cracking passwords, generating malicious codes, sending phishing emails, network scanning, and social engineering attacks. All these lower the threshold for cyberattacks and increase the difficulty of security protection."
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)