arXiv:2602.11083v3 Announce Type: replace Abstract: Remote change detection in LLMs is a difficult problem. Existing methods are either too expensive for deployment at scale, or require initial white-box access to model weights or grey-box access to log probabilities. We aim to achieve both low cost and strict black-box operation, observing only output tokens. Our approach hinges on specific inputs we call Border Inputs, for which there exists more than one output top token. From a statistical perspective, optimal change detection depends on the model's Jacobian and the Fisher information of t
The proliferation of LLM APIs creates a pressing need for efficient, black-box change detection to ensure model integrity and mitigate risks.
This development addresses a critical challenge in AI governance and security, enhancing trust and reliability in black-box LLM deployments at scale.
It provides a low-cost, black-box method for detecting changes in LLMs without requiring access to internal model architects or sensitive data.
- · LLM API providers
- · Enterprises using LLMs
- · AI governance & security firms
- · Malicious actors
- · Current expensive detection methods
Wider adoption of LLM APIs due to improved reliability and security through cost-effective change detection.
Increased competition among LLM providers focusing on 'trustworthy AI' features, leading to higher API quality standards.
Potential for new regulatory frameworks for AI systems that mandate black-box change detection as a compliance requirement.
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Read at arXiv cs.LG