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Microsoft-Led Study Exposes AI Biosecurity Gap as Patches Lift Detection to 97%

Cross-sector patches lifted detection to roughly 97%, leaving a residual gap that demands continual updates.

Overview

  • Using open-source design tools, researchers generated about 75,000–76,000 variants spanning 72 harmful proteins and found many AI-altered sequences initially slipped past DNA-order screening.
  • A 10‑month collaboration with biosecurity software providers produced patches that improved detection, including for fragmented genes, and three of four providers deployed updates.
  • Across providers, roughly 3% of variants judged most likely to retain function still evade detection, a shortfall participants describe as an ongoing arms race.
  • The study was conducted entirely in silico, so whether the AI-designed proteins would function biologically remains unverified.
  • Because of misuse risk, portions of data and code are restricted under a tiered access process managed by IBBIS, and stakeholders emphasize layered safeguards such as stronger know‑your‑customer checks.