Toward Reliable, Safe, and Secure LLMs for Scientific Applications

As large language models (LLMs) evolve into autonomous "AI scientists," they promise transformative advances but introduce novel vulnerabilities, from potential "biosafety risks" to "dangerous explosions." Ensuring trustworthy deployment in science requires a new paradigm centered on reliability (ensuring factual accuracy and reproducibility), safety (preventing unintentional physical or biological harm), and security (preventing malicious misuse). Existing general-purpose safety benchmarks are poorly suited for this purpose, suffering from a fundamental domain mismatch, limited threat coverage of science-specific vectors, and benchmark overfitting, which create a critical gap in vulnerability evaluation for scientific applications. This paper examines the unique security and safety landscape of LLM agents in science. We begin by synthesizing a detailed taxonomy of LLM threats contextualized for scientific research, to better understand the unique risks associated with LLMs in science. Next, we conceptualize a mechanism to address the evaluation gap by utilizing dedicated multi-agent systems for the automated generation of domain-specific adversarial security benchmarks. Based on our analysis, we outline how existing safety methods can be brought together and integrated into a conceptual multilayered defense framework designed to combine a red-teaming exercise and external boundary controls with a proactive internal Safety LLM Agent. Together, these conceptual elements provide a necessary structure for defining, evaluating, and creating comprehensive defense strategies for trustworthy LLM agent deployment in scientific disciplines.

GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?

We present GeoGrid-Bench, a benchmark designed to evaluate the ability of foundation models to understand geo-spatial data in the grid structure. Geo-spatial datasets pose distinct challenges due to their dense numerical values, strong spatial and temporal dependencies, and unique multimodal representations including tabular data, heatmaps, and geographic visualizations. To assess how foundation models can support scientific research in this domain, GeoGrid-Bench features large-scale, real-world data covering 16 climate variables across 150 locations and extended time frames. The benchmark includes approximately 3,200 question-answer pairs, systematically generated from 8 domain expert-curated templates to reflect practical tasks encountered by human scientists. These range from basic queries at a single location and time to complex spatiotemporal comparisons across regions and periods. Our evaluation reveals that vision-language models perform best overall, and we provide a fine-grained analysis of the strengths and limitations of different foundation models in different geo-spatial tasks. This benchmark offers clearer insights into how foundation models can be effectively applied to geo-spatial data analysis and used to support scientific research.