AI Agents in Biological Research: A Review of Evaluation and Security

This paper reviews existing benchmarks and evaluation approaches for AI agents in biological and biomedical research. An examination of reported benchmark results highlights persistent limitations in agents' ability to perform complex scientific tasks accurately and reliably. The paper also explores the architectural and operational characteristics of these systems, focusing on how interactions between interconnected components can introduce additional challenges for their evaluation. Furthermore, it reviews the literature on AI agent security and discusses potential vulnerabilities that may compromise the integrity and reliability of agent workflows. Finally, the paper identifies current limitations and outlines future research directions for developing more reliable, secure, and scientifically capable AI agents for biological research.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-01
DOI
https://doi.org/10.5281/zenodo.23848429
Primary Topic
Scientific Computing and Data Management
Type
preprint
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preprint

AI Agents in Biological Research: A Review of Evaluation and Security

Mehrak Malekpour Alamdari
2 links
Zenodo (CERN European Organization for Nuclear Research)
Scientific Computing and Data Management
preprint

AI Agents in Biological Research: A Review of Evaluation and Security

Mehrak Malekpour Alamdari
preprint en

Abstract

This paper reviews existing benchmarks and evaluation approaches for AI agents in biological and biomedical research. An examination of reported benchmark results highlights persistent limitations in agents' ability to perform complex scientific tasks accurately and reliably. The paper also explores the architectural and operational characteristics of these systems, focusing on how interactions between interconnected components can introduce additional challenges for their evaluation. Furthermore, it reviews the literature on AI agent security and discusses potential vulnerabilities that may compromise the integrity and reliability of agent workflows. Finally, the paper identifies current limitations and outlines future research directions for developing more reliable, secure, and scientifically capable AI agents for biological research.

Zenodo (CERN European Organization for Nuclear Research)
Scientific Computing and Data Management
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