arXiv:2605.23220v1 Announce Type: new Abstract: Despite the growing use of world models as decision-making agents, their adversarial robustness remains underexplored due to the lack of dedicated automated evaluation methods. A key obstacle is that attack evaluation must be both accurate and efficient: weak manually tuned attacks can overestimate robustness, while exhaustive hyperparameter search is prohibitively expensive because each candidate requires closed-loop rollouts through learned latent dynamics. We introduce WMAttack, an automated attack-search framework for adversarial evaluation o
The increasing deployment of world models in AI decision-making necessitates robust adversarial evaluation as their use becomes more widespread and mission-critical.
Ensuring the adversarial robustness of world-model agents is crucial for their reliable and safe deployment in real-world applications, directly impacting AI safety and trustworthiness.
The development of automated attack search frameworks like WMAttack transforms the efficiency and accuracy of evaluating the adversarial resilience of advanced AI agents, moving beyond manual attack tuning.
- · AI safety researchers
- · Developers of robust AI systems
- · Industries relying on AI decision-making
- · Adversarial attackers relying on manual methods
- · AI systems with unaddressed robustness flaws
WMAttack provides a systematic way to identify vulnerabilities in world-model agents, leading to more resilient AI.
Improved robustness evaluation will accelerate the adoption of world-model AI in sensitive domains by increasing trust in their performance.
This could lead to a 'robustness arms race' between attack generation and defense mechanisms, continuously pushing the boundaries of AI safety and performance.
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Read at arXiv cs.LG