Can AI Break Out? Testing Language Models’ Container Escape Skills
![The study demonstrates that container escape success rates, assessed over five epochs for varied model and scenario pairings, correlate directly with scenario difficulty-ranging from [latex]1/5[/latex] to [latex]5/5[/latex] as detailed in Appendix B-indicating a quantifiable relationship between environmental complexity and the efficacy of container breakout attempts.](https://arxiv.org/html/2603.02277v1/2603.02277v1/figs/scaling_heatmap.png)
New research demonstrates that leading large language models can reliably escape commonly misconfigured containerized environments, raising concerns about the security of deploying these powerful systems.
![The stability of key parameters-kaon mass [latex]M_{K}[/latex], pion mass [latex]M_{\pi}[/latex], pion energy [latex]E_{\pi}[/latex], and the form factor [latex]f_{+}(q^{2}=0)[/latex]-was assessed by varying the minimum time slice [latex]t_{min}[/latex] within a [latex]0.06[/latex] fm quark ensemble, demonstrating that the fit remains stable across different numbers of exponential functions-represented by distinct color bands, with the central fit highlighted in blue-despite the preliminary nature of the data.](https://arxiv.org/html/2603.02994v1/2603.02994v1/x1.png)




![A theoretical model investigates itinerant spinful fermions coupled to ancillary spin layers-arranged as a ladder and interacting via Kondo exchange, Heisenberg interactions, and interlayer coupling-to create a composite local Hilbert space of dimension [latex]\mathcal{V} = 2^{4}[/latex], thereby exploring the interplay between fermionic and spin degrees of freedom within the system.](https://arxiv.org/html/2603.02316v1/2603.02316v1/x1.png)
