An agentic red teaming system automates creation of adversarial testing workflows from natural language goals, unifying ML and generative AI attacks and achieving 85% success rate on Meta Llama Scout with no custom human code.
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Prithvi-EO-2.0 shows environment-dependent flood detection limits, with highest accuracy in cropland (IoU 52%) and riverine events (F1 0.69) and near-zero performance in tree cover and built-up areas across 19 global events.
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Redefining AI Red Teaming in the Agentic Era: From Weeks to Hours
An agentic red teaming system automates creation of adversarial testing workflows from natural language goals, unifying ML and generative AI attacks and achieving 85% success rate on Meta Llama Scout with no custom human code.
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Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events
Prithvi-EO-2.0 shows environment-dependent flood detection limits, with highest accuracy in cropland (IoU 52%) and riverine events (F1 0.69) and near-zero performance in tree cover and built-up areas across 19 global events.