MIT AI Risk Repository · Risk Category · 73.04.00
LLM-Systems Can Be Untrustworthy
Description
"A key desideratum for an LLM from a user’s perspective is ‘trustworthiness’, i.e. assurance of reliability and consistent performance, and absence of any accidental harm caused by the technology to the user.16 Providing assurance that an LLM-based system will not cause accidental harm remains a major open challenge. Harms may either occur directly due to the flawed nature of LLMs, e.g. an LLM generating toxic language or behaving inappropriately in some other ways, or may occur due to improper usage by a user, e.g. automation bias due to a user’s overreliance on LLM."
From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Domain
- —
- Subdomain
- —
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
Other entries from Anwar2024
- Agentic LLMs Pose Novel Risks
- Natural Language Underspecifies Goals
- Goal-Directedness Incentivizes Undesirable Behaviors
- Safety Risks from Affordances Provided to LLM-agents
- Multi-Agent Safety Is Not Assured by Single-Agent Safety
- Foundationality May Cause Correlated Failures
- Groups of LLM-Agents May Show Emergent Functionality
- Collusion between LLM-Agents
- Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs
- Misinformation and Manipulation
- Cybersecurity
- Cybersecurity