arXiv:2510.09330v3 Announce Type: replace Abstract: Ensuring that large language models (LLMs) comply with safety requirements is a central challenge in AI deployment. Existing alignment approaches primarily operate during training, such as through fine-tuning or reinforcement learning from human feedback, but these methods are costly and inflexible, requiring retraining whenever new requirements arise. Recent efforts toward inference-time alignment mitigate some of these limitations but still assume access to model internals, which is impractical, and not suitable for third party stakeholders
The proliferation of black-box LLMs necessitates novel alignment methods that don't rely on internal model access, aligning with current rapid AI deployment trends.
This research addresses a critical practical and ethical challenge in AI deployment by enabling safety alignment for proprietary or third-party LLMs without requiring access to their internal architecture.
The ability to perform inference-time safety alignment on black-box LLMs shifts the responsibility and flexibility of ethical AI deployment to a broader range of stakeholders.
- · AI deployers without model access
- · Independent AI safety researchers
- · Third-party AI developers
- · AI governance bodies
- · Companies relying solely on internal-access alignment
- · Opaquely deployed unsafe AI
Black-box LLMs can be more easily and flexibly aligned to safety standards post-deployment, enhancing ethical AI use across various applications.
This democratizes AI safety, potentially leading to more widespread and responsible adoption of advanced LLMs even by organizations without deep technical AI expertise or vendor cooperation.
The development of robust black-box safety methods could diminish the necessity for stringent, often proprietary, pre-deployment alignment processes, altering competitive dynamics in the AI market.
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Read at arXiv cs.LG