White-Box Elicitation Methods

• White-box elicitation methods are strategies that exploit full model internals—such as gradients, activations, and parameters—to extract insights and assess vulnerabilities.
• They enable detailed model auditing and debugging via gradient-based attribution, layer-wise relevance propagation, and performance profiling techniques.
• Applications span membership inference, interpretability, security, API testing, and watermarking, highlighting both methodological benefits and inherent challenges.

White-box elicitation methods are strategies that exploit full access to a machine learning model’s internal representations, gradients, parameters, or activations to extract knowledge, explanations, or vulnerabilities not accessible through output alone. These methods span a broad range of applications, including membership inference, interpretability, performance analysis, explainability, model auditing, adversarial robustness, and model watermarking. White-box elicitation is distinguished from black-box approaches by its utilization of gradients, layer internals, and model-specific structures, thus enabling deeper insight or attack vectors but also raising unique methodological challenges and considerations.

1. Mathematical Foundations and Bayes-Optimality

White-box elicitation methods often ground themselves in probabilistic or information-theoretic frameworks, leveraging explicit knowledge of the model’s architecture and parameter distributions. In membership inference, for instance, the Bayes-optimal strategy depends critically on the training loss function and parameter posterior: $$\mathcal{M}(\theta, z_1) = \mathbb{E}_T \left[ \sigma\left( \log \frac{p(\theta \mid m_1=1, z_1, T)}{p(\theta \mid m_1=0, z_1, T)} + t_\lambda \right) \right]$$ where $\theta$ denotes parameters, $z_1$ the queried sample, $\sigma$ the sigmoid function, $t_\lambda$ a prior adjustment, and $p(\theta \mid \cdot)$ the model posterior determined by the loss $\ell(\theta, z)$ and temperature $T$ (Sablayrolles et al., 2019). Under general assumptions, the inference depends only on the observed loss, so that black-box knowledge of loss suffices for optimality, and access to further internals (the “white-box view”) provides no advantage.

Iterative and layerwise techniques also utilize gradients ($\nabla_{\theta}$), hidden states ($h_{\ell}$ in transformers or RNNs), or explicit decompositions (as in layer-wise relevance propagation) to propagate or extract information through the network.

2. White-Box Attribution and Interpretability

A significant class of white-box elicitation focuses on model interpretability, generating feature attributions or explanation maps directly from model internals. Main approaches include:

• Gradient-based linearization: Methods such as saliency, gradient × input, and integrated gradients propagate derivatives from the output to precisely locate influential pixels, tokens, or features. Integrated Gradients, for example, define attribution as: $$R_i^{(c)} = (x_i - x'_i) \int_0^1 \frac{\partial S_c(x' + \alpha (x - x'))}{\partial x_i} d\alpha$$ where $x, x'$ are the input and baseline, respectively (Ayyar et al., 2021).
• Structure-based relevance propagation: Layer-wise Relevance Propagation (LRP) and DeepLIFT redistribute output scores backward using model-specific redistribution rules (e.g., conservation of relevance across layers).
• Sparse autoencoders and activations: Techniques such as mechanistic interpretability decompose internal activations into sparse features, scoring them for their contribution to target properties (e.g., secrets) with TF–IDF inspired metrics: $$\text{score}(f) = \frac{1}{|S|} \sum_{i \in S} a_f(i) \cdot \log \left(\frac{1}{d_f} \right)$$ where $a_f(i)$ is feature activation and $d_f$ is feature density (Cywiński et al., 1 Oct 2025).
• Intermediate layer projections ("logit lens", "Editor’s term"): Residual streams at internal layers are projected via the unembedding matrix to produce partial token distributions, providing actionable evidence about hidden knowledge.

Control experiments with white-box LSTM models (manually specified) revealed that popular attribution techniques can fail to align attribution heatmaps with known causal features, due to issues like gradient saturation, cancellation, or subtle implementation differences—highlighting the challenges even when ground truth model reasoning is available (Hao, 2020).

3. White-Box Performance and Debugging

White-box elicitation supports detailed performance modeling and debugging in configurable and complex software systems:

• Dynamic taint analysis and compositional linear models: Systems such as Comprex directly instrument source code, propagate configuration-option “taints” via data and control flow, and partition the configuration space into behavioral “subspaces.” Local performance models for each code region are constructed and composed algebraically: $$m_\text{global} = \sum_\text{regions} m_\text{region}$$ with explicit coefficients per configuration option and interaction, compressing the exponential configuration space into a small set of observed behaviors (Velez et al., 2021).
• Method-level modeling and profiling: Profiling approaches model the performance-influence of individual methods as a function of configuration settings, using tree-based models and iterative fine-grained measurements to localize configurational bottlenecks. Significant reductions in mean absolute percentage error (MAPE) are achieved by focusing detailed profiling effort where coarse-grained models underperform (Weber et al., 2021).

This precise attribution of performance effects to particular regions and options is not feasible with black-box methods that treat the system monolithically.

4. Testing, Coverage, and Elicitation in NLP and APIs

White-box elicitation extends to testing adequacy through neuron and attention coverage metrics in deep NLP and API systems:

• Mask Neuron Coverage (MNCOVER): For transformer-based NLP, MNCOVER quantifies the fraction of “important” neuron bins (both at word and attention-head pair levels) exercised by a test suite. When augmented with mask vectors learned to select task-relevant neurons, MNCOVER reliably filters redundant tests and guides test generation or data augmentation (Sekhon et al., 2022).
• API fuzzing with evolutionary search: Evolutionary search driven by white-box coverage and fault feedback is used to generate diverse, high-coverage test suites for APIs where source code is available, outperforming random search and black-box-only techniques. The feedback function integrates coverage, branch distance, and error signals (Belhadi et al., 2022).

These white-box metrics enable more efficient and fault-revealing test suites, critical for scalable quality assurance.

5. Security, Adversarial Analysis, and Watermarking

White-box access facilitates both robust security evaluation and intellectual property protection:

• Membership inference and adversarial attacks: Theoretical analysis shows Bayes-optimal membership inference is loss-based; no extra power is gained from white-box access under the specified assumptions (Sablayrolles et al., 2019). In adversarial contexts, reverse engineering of on-device models (e.g., via REOM) generates debuggable representations permitting gradient-based attacks with success rates exceeding those of black-box surrogates by large margins (Zhou et al., 8 Feb 2024).
• DNN watermarking schemes: Unified frameworks for white-box DNN watermark embedding and extraction deploy secret feature extraction and projection functions over internal representations. Advanced schemes such as DICTION use adversarial latent-space trigger generation and GAN-discriminator–style projection networks for robust, high-capacity, unremovable watermarks (Bellafqira et al., 2022). Conversely, removal frameworks such as DeepEclipse exploit layer splitting, invertible transformations, and frequency-analysis–based detection to obfuscate or “break” watermarks, while preserving network accuracy (Pegoraro et al., 6 Mar 2024).

These approaches rely on full access to network weights, architectures, and internal activations—the defining characteristic of white-box elicitation.

6. Comparative Effectiveness and Limitations

Empirical analyses across several domains reveal the nuanced performance of white-box elicitation:

• In uncertainty and confidence estimation for LLMs, token-probability–based white-box methods (sequence probability, length normalization) slightly improve calibration over verbalized-confidence black-box counterparts. However, the AUROC differences are modest (e.g., 0.522 vs. 0.605), and neither class of method achieves robust failure prediction on challenging tasks (Xiong et al., 2023).
• In network security and explainable AI, white-box attribution methods (LRP, IG, DeepLift) achieve higher robustness and completeness in explaining DNN-driven intrusion detection compared to black-box explainers like LIME or SHAP, though model specificity remains a limitation (Arreche et al., 14 Jan 2025).
• For secret elicitation, while black-box prefilling and adversarial prompting often suffice, in certain intentionally obfuscated or subtle settings, intermediate-layer white-box techniques (logit lens, sparse autoencoders) reveal secrets undetectable by querying final model outputs (Cywiński et al., 1 Oct 2025).

A recurring limitation is that white-box methods’ superiority depends on the model, task, and details of the information sought. They may yield little or no gain when black-box outputs fully reflect the target property or when access to internals is restricted.

7. Future Directions and Open Challenges

Current research identifies several directions for advancing white-box elicitation:

• Design of robust, implementation-invariant attribution and explanation algorithms that remain faithful across numerically unstable or saturated networks.
• Extension of white-box coverage and interpretability techniques to the latest model architectures (e.g., LLMs, transformer-variants, compressed formats) and to additional domains such as APIs, product lines, and embedded models (Sekhon et al., 2022, Casaluce et al., 23 Jan 2024).
• Adapting white-box techniques for partially-observable APIs through probability distribution estimation (e.g., Glimpse), thereby closing the gap between proprietary and open-source model auditing (Bao et al., 16 Dec 2024).
• Compositional, scalable approaches that can handle model families, distributed settings, and large state-spaces as in product line validation.
• Mechanistic interpretability tools (e.g., SAEs, logit lens) that facilitate fine-grained auditing and secret elicitation, potentially exposing unexpected behaviors in LLMs and foundation models (Cywiński et al., 1 Oct 2025).

Ensuring the robustness, generality, and actionable utility of white-box elicitation will remain critical as models become more complex, embedded, and integral to sensitive applications across industries.