arXiv:2507.01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation. However, exposing gradients during training can leak sensitive information about the underlying data, raising privacy and security concerns such as susceptibility to data poisoning attacks. In contrast, black-box optimization methods, which treat the model as an opaque function, relying solely on function evaluations to guide optimization, offer a promising alternative in scenarios where data access is restricted,
The increasing sophistication and pervasive deployment of large language models heighten privacy and security concerns, making robust, privacy-preserving training methods critical.
This research provides provable generalization bounds for 'tuning without peeking' black-box optimization, addressing fundamental security and privacy vulnerabilities in LLM post-training.
The ability to tune LLMs effectively without exposing sensitive gradients fundamentally alters how models can be deployed and maintained in privacy-sensitive or adversarial environments.
- · AI-as-a-service providers
- · Sensitive data industries (healthcare, finance)
- · Black-box optimization research
- · Cybersecurity for AI
- · Data poisoning attackers
- · Current gradient-based fine-tuning methods in some contexts
- · Entities with weak data privacy postures
Increased adoption of privacy-preserving machine learning techniques for LLMs.
Reduced incidence of data-leakage and data-poisoning attacks on deployed AI systems.
New regulatory frameworks or industry standards emerging around black-box AI model tuning and maintenance.
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Read at arXiv cs.AI