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 vulnerab...
| Authors | Saket Sanjeev Chaturvedi, Joshua Bergerson, Tanwi Mallick |
|---|---|
| Primary category | cs.CR |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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