MIT AI Risk Repository · Additional evidence · 73.04.02a

Inconsistent Performance across and within Domains

Category: LLM-Systems Can Be Untrustworthy

Open the MIT Risk Navigator All entries from Anwar2024

Description

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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

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Intent: —
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Other entries from Anwar2024

Agentic LLMs Pose Novel Risks Risk Category · 7.2
Natural Language Underspecifies Goals Risk Sub-Category · 7.1
Goal-Directedness Incentivizes Undesirable Behaviors Risk Sub-Category · 7.2
Safety Risks from Affordances Provided to LLM-agents Risk Sub-Category · 7.2
Multi-Agent Safety Is Not Assured by Single-Agent Safety Risk Category · 7.6
Foundationality May Cause Correlated Failures Risk Sub-Category · 7.6
Groups of LLM-Agents May Show Emergent Functionality Risk Sub-Category · 7.6
Collusion between LLM-Agents Risk Sub-Category · 7.6
Dual-Use Capabilities Enable Malicious Use and Misuse of LLMs Risk Category · 4.0
Misinformation and Manipulation Risk Sub-Category · 4.3
Cybersecurity Risk Sub-Category · 4.3
Cybersecurity Additional evidence