As safety mechanisms become more robust, automated red-teaming pipelines have emerged to scale attack generation, including gradient-based approaches such as Greedy Coordinate Gradient (GCC; Zou et al. [83]), and black-box approaches that leverage LLMs as red-teamers to iteratively refine attacks without gradient access [84], [85]. Beyond prompt-based attacks, vulnerabilities arise across other stages of the model lifecycle. Poisoned training samples can compromise model behavior [86], quantization can introduce exploitable blind spots [87], [88], and AI-assisted code generation introduces its own security risks [89].
Jobs in health care may also have the advantage of being resistant to some AI-driven displacement. Anthropic’s latest research on AI’s labor market impacts found health care practitioners would be able to have AI cover 58% of tasks, with just 5% of task coverage being observed today. For health care support, 38% of tasks could be capable of being covered with AI, 4% of which are currently. That’s compared to 94% of office and administration tasks capable of being covered by AI, with 42% observed to already be covered by the technology.
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