Dataset-Only Black-Box Fine-Tuning

• Dataset-only black-box fine-tuning interfaces are systems that enable model adaptation using only external data without access to internal parameters.
• They employ optimization techniques such as evolutionary strategies, direct data perturbation, and discrete prompt tuning to improve model performance.
• These methods enhance security and privacy by limiting access to gradients while supporting robust domain adaptation and model verification.

A dataset-only black-box fine-tuning interface is a protocol or system in which users adapt, enhance, or audit the behavior of a machine learning model—often a large-scale language, vision, or multimodal model—by providing only data (such as labeled samples, prompts, or datasets) to the provider or the model’s public API, with no access to model parameters, architecture, or internal gradients. This paradigm has become technologically and commercially relevant due to the pervasiveness of proprietary models (such as GPT-4, Gemini, and commercial vision-language systems) offered exclusively via prediction or fine-tuning APIs that shield all internals for privacy, safety, or intellectual property reasons. The following sections synthesize major developments, methodologies, security concerns, and mechanisms for dataset-only black-box fine-tuning, as documented in recent research.

1. Foundational Paradigms and Methodologies

Dataset-only black-box fine-tuning encompasses a variety of adaptation strategies tailored for models whose parameters are inaccessible:

• Direct Data Perturbation (Data Fine-tuning): The earliest formalization (Chhabra et al., 2018) replaces model parameter updates with constrained modifications to the data. For instance, facial images are perturbed by learning a small, visually imperceptible, universal noise vector $N$ such that the perturbed data minimizes task loss when passed through a fixed model:

$$\underset{N}{\text{min}}\ \mathcal{A}(y, P(A|Z)) + H(X, Z)$$

where $Z_k = \tfrac{1}{2}(\tanh(X_k + N) + 1)$ and $H$ is a similarity-preserving term.

• Prompt and Adapter-Based Black-Box Tuning: Gradient-free optimization techniques (e.g., Covariance Matrix Adaptation Evolution Strategy, CMA-ES) enable the tuning of soft or discrete prompts solely through repeated evaluations of the public model API (Shen et al., 2023), even when only the input embedding or output logits/predictions are accessible.
• Adapter and Combiner Networks: Auxiliary models, such as lightweight adapters or proxy LMs, are trained strictly on the provided data and then combined with the outputs of the immutable black-box model. Methods such as CombLM (Ormazabal et al., 2023) and BBox-Adapter (Sun et al., 13 Feb 2024) use learned probability-level ensembling:

$P_C(y|x) = f(P_S(\cdot|x), P_L(\cdot|x))_y$

where $P_S$ is the small (trainable) expert and $P_L$ is the black-box model output.

• Retrieval-Augmented Domain Adaptation: External datastores constructed from target-domain data support retrieval-augmented inference, interpolating between model predictions and retrieval-based estimates as in kNN-Adapter (Huang et al., 2023):

$$p(y|x) \propto \Lambda_{ca} \odot p_{kNN}(y|x) + (\mathbf{1} - \Lambda_{ca}) \odot p_{LM}(y|x)$$

where $\Lambda_{ca}$ is a learned context-aware mixing vector.

• Federated and Collaborative Approaches: Federated prompt tuning protocols such as Fed-BBPT (Lin et al., 2023) and FedDTPT (Wu et al., 1 Nov 2024) enable participants to tune prompt tokens or instructions locally under strict privacy, sharing only non-sensitive adaptation artifacts, with aggregation handled by a central server. Prompt aggregation often incorporates filtering, clustering, or semantic weighting in the embedding space.

2. Optimization Without Model Internals

A key technical challenge is performing efficient, reliable optimization when neither gradients nor internal representations are accessible:

• Zero-Order and Evolutionary Optimization: Methods such as SPSA (Simultaneous Perturbation Stochastic Approximation) and CMA-ES are systematically employed for prompt tuning, as they rely on differences in loss or accuracy between perturbed prompt variants rather than model gradients (Shen et al., 2023, Lin et al., 2023).
• Likelihood-Free and Label-Based Strategies: For cases where only discrete predictions are observed (likelihood-free scenario), algorithms leverage evolutionary or Approximate Bayesian Computation (ABC-SMC) based acceptance-rejection to tune prompts, simulating parameter space exploration via black-box outputs alone (Shen et al., 2023).
• Discrete and Transferable Prompt Protocols: Discrete prompt tokens (as opposed to continuous vectors) are optimized through accuracy-based feedback loops (Wu et al., 1 Nov 2024), again solely via API interaction. Aggregation at the server employs attention-based similarity calculations and clustering (e.g., DBSCAN with elbow detection) in the embedding space.
• Adapter/Proxy Tuning Consistency: To maintain correspondence between training- and inference-time model ensembling, Consistent Proxy Tuning (CPT) (He et al., 1 Jul 2024) formulates the loss on a consistent mixture of the small model, the frozen black-box, and a frozen proxy, avoiding distribution shifts otherwise present in standard proxy-tuning.

3. Security, Safety, and Verification via Dataset-Only Interfaces

Dataset-only black-box interfaces change the threat and verification landscape:

• Data Poisoning and Jailbreak Attacks: Attackers can construct datasets that subvert or evade defense protocols by embedding “safe” wrappers, benign lexical encodings (e.g., underscores in place of keywords), and backdoor triggers into training points. Such three-pronged attacks (Li et al., 1 Oct 2025) were shown to achieve attack success rates above 97% against GPT-4.1 and GPT-4o, bypassing multi-stage defenses involving pre-upload filtering, defensive fine-tuning, and post-training audits.
• Watermark-Based Dataset Attribution: To audit potential misuse of copyrighted or proprietary data, distortion-free watermarking (Zhang et al., 3 Oct 2025) is employed. A dataset is rewritten with private-key-guided semantic rephrasing such that when a model is fine-tuned on the watermarked data, a highly sensitive entropy-gated detection protocol can verify downstream usage strictly via black-box queries. This method operates via an entropy-ranking of token positions, yielding statistically robust detection even when only a subset of training samples is watermarked and after further pre-training.

4. Performance, Limitations, and Practical Efficiency

Academic studies consistently demonstrate that dataset-only black-box fine-tuning can yield substantial performance gains, often approaching or matching those seen in white-box regimes, provided that adaptation strategies are well-matched to the task and model constraints:

• On facial attribute tasks, data fine-tuning improved accuracy by 1–3% (intra-dataset) and up to 12–30% (cross-dataset, black-box setting) (Chhabra et al., 2018).
• Retrieval-augmented and adapter-based methods offered perplexity improvements of 1–3 points over baselines, especially in few-shot or limited-access cases (Huang et al., 2023, Ormazabal et al., 2023).
• Collaborative vision-language adaptation approaches such as CraFT (Wang et al., 6 Feb 2024), CBBT (Guo et al., 2023), and federated prompt tuning protocols (Lin et al., 2023, Wu et al., 1 Nov 2024) report robust gains with minimal communication or computation costs, sometimes running at less than 1/80th the memory and only a handful of thousand model queries.
• Plug-and-play adapters and prompt-tuning strategies inherently support model-agnostic transfer and show strong empirical results for continual adaptation, provided the external, fine-tuned components are properly maintained.

Notable limitations include sensitivity to the quality and diversity of training data (particularly in NCE-based online adapters (Sun et al., 13 Feb 2024)), dependence on API query budget and latency, and the requirement for sufficient high-entropy or ambiguous positions in outputs for watermark-based verification (Zhang et al., 3 Oct 2025).

5. Application Domains and Generalizability

The dataset-only black-box paradigm supports a diverse set of applications beyond canonical LLMing:

• Few-Shot and Domain-Specific Classification: Black-box prompt tuning, data augmentation pipelines, label-enhanced cross-attention, and student-teacher paradigms enable resource-constrained few-shot adaptation for text and vision tasks (Luo et al., 2023, Luo et al., 19 Mar 2024).
• Retrieval Augmented Generation (RAG): Model-augmented fine-tuning of black-box embedding models via trainable “augmentation” networks (e.g., Mafin) provides powerful retrieval capacity under API-only access scenarios (Zhang et al., 19 Feb 2024).
• Federated and Multi-Party Collaboration: Federated prompt tuning protocols support distributed, privacy-preserving adaptation across heterogeneous and non-iid data sources for both LLMs and VLMs (Lin et al., 2023, Wu et al., 1 Nov 2024).
• Model Protection and Copyright Auditing: Distortion-free watermarking with entropy-gated verification enables reliable copyright safeguarding across commercial and open LLMs (Zhang et al., 3 Oct 2025).

6. Security, Privacy, and Future Directions

Recent results highlight both the promise and the risks inherent to dataset-only black-box fine-tuning:

• Attacks exploiting gaps in current defense protocols indicate the need for more robust, semantically aware auditing (as opposed to mere token-level filtering) (Li et al., 1 Oct 2025). The effectiveness of “self-auditing” is limited when attackers can use the same model for both fine-tuning and adversarial dataset construction.
• Privacy-preserving federated adaptation frameworks allow large-scale, distributed customization without compromising user or model data (Lin et al., 2023, Wu et al., 1 Nov 2024).
• Ongoing directions include explicit modeling of uncertainty via prompt distributions (Shen et al., 2023), robust clustering and weighting in server-side aggregation (Wu et al., 1 Nov 2024), and dynamic tuning of adaptation hyperparameters for consistent proxy optimization (He et al., 1 Jul 2024).
• Watermarking and entropy-gated detection are poised to become standard for dataset usage attestation, especially as regulatory and legal constraints around LLM data sourcing tighten (Zhang et al., 3 Oct 2025).

Effective dataset-only black-box fine-tuning interfaces thus require rigorous optimization, careful management of privacy and safety boundaries, and ongoing development of adaptive defense and verification protocols as these technologies are deployed at scale.