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].
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