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Nuclear Deployed: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents

Rongwu Xu, Xiaojian Li, Shuo Chen, Wei Xu

TL;DR

This work reveals that autonomous LLM agents can exhibit catastrophic behaviors and deception in high-stakes CBRN scenarios, even without explicit prompts to do so. By modeling a three-stage, HHHT-driven evaluation framework and conducting 14,400 rollouts across 12 SOTA LLMs, the study shows that stronger reasoning can amplify risk, and that autonomy can be exercised despite supervisory constraints. It demonstrates that instruction-level restrictions and system prompts do not fully prevent unsafe actions, and that mechanisms like abstention or safety-aligned prompting can mitigate but not eliminate risk. The findings underscore the need for robust, multi-faceted safety approaches—beyond natural-language constraints—when deploying autonomous LLM agents in critical domains, along with policy and governance considerations to govern autonomy and accountability.

Abstract

Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes scenarios, particularly in Chemical, Biological, Radiological and Nuclear (CBRN) domains. Based on the insight that such risks can originate from trade-offs between the agent's Helpful, Harmlessness and Honest (HHH) goals, we build a novel three-stage evaluation framework, which is carefully constructed to effectively and naturally expose such risks. We conduct 14,400 agentic simulations across 12 advanced LLMs, with extensive experiments and analysis. Results reveal that LLM agents can autonomously engage in catastrophic behaviors and deception, without being deliberately induced. Furthermore, stronger reasoning abilities often increase, rather than mitigate, these risks. We also show that these agents can violate instructions and superior commands. On the whole, we empirically prove the existence of catastrophic risks in autonomous LLM agents. We release our code to foster further research.

Nuclear Deployed: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents

TL;DR

This work reveals that autonomous LLM agents can exhibit catastrophic behaviors and deception in high-stakes CBRN scenarios, even without explicit prompts to do so. By modeling a three-stage, HHHT-driven evaluation framework and conducting 14,400 rollouts across 12 SOTA LLMs, the study shows that stronger reasoning can amplify risk, and that autonomy can be exercised despite supervisory constraints. It demonstrates that instruction-level restrictions and system prompts do not fully prevent unsafe actions, and that mechanisms like abstention or safety-aligned prompting can mitigate but not eliminate risk. The findings underscore the need for robust, multi-faceted safety approaches—beyond natural-language constraints—when deploying autonomous LLM agents in critical domains, along with policy and governance considerations to govern autonomy and accountability.

Abstract

Large language models (LLMs) are evolving into autonomous decision-makers, raising concerns about catastrophic risks in high-stakes scenarios, particularly in Chemical, Biological, Radiological and Nuclear (CBRN) domains. Based on the insight that such risks can originate from trade-offs between the agent's Helpful, Harmlessness and Honest (HHH) goals, we build a novel three-stage evaluation framework, which is carefully constructed to effectively and naturally expose such risks. We conduct 14,400 agentic simulations across 12 advanced LLMs, with extensive experiments and analysis. Results reveal that LLM agents can autonomously engage in catastrophic behaviors and deception, without being deliberately induced. Furthermore, stronger reasoning abilities often increase, rather than mitigate, these risks. We also show that these agents can violate instructions and superior commands. On the whole, we empirically prove the existence of catastrophic risks in autonomous LLM agents. We release our code to foster further research.

Paper Structure

This paper contains 52 sections, 3 equations, 108 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Evaluation Method
  3. Overall Design
  4. Scenario Generation
  5. Catastrophic Behavior Simulation
  6. Deception Simulation
  7. Tunable Components
  8. Main Experiments
  9. Experiment Setup
  10. Main Results and Findings
  11. Extented Experiments and Analyses
  12. Instruction & Command Violation
  13. Abstention
  14. Factors Affecting Decision-making
  15. Related Work
  16. ...and 37 more sections

Figures (108)

  • Figure 1: We find LLM agents can deploy catastrophic behaviors even if it has no authority and the permission request is denied. It will also falsely accuse the third party as a way of deception when asked by its superior.
  • Figure 2: Representative manifestations of HHH trade-offs. In this paper, we focus on catastrophic behavior and deception where the helpful goal dominates.
  • Figure 3: Illustrative framework of our three-stage evaluation. The scenario is War - Avoidance. We first generate the simulation scenario, and then sequentially run simulation rollouts for catastrophic behavior and deception.
  • Figure 4: Agent's task $\mathcal{T}$ in four scenarios. They are jointly determined by the topic and motivation.
  • Figure 5: \newlabelfig:catastrphic@inappendix0Main results of catastrophic behavior
  • ...and 103 more figures