Blackbox Model Provenance

• Blackbox model provenance is the study of determining, explaining, and verifying the origins and decision processes of opaque models using formal techniques such as game-theoretic and semiring frameworks.
• Global and local extraction methods, including surrogate decision trees and clustering of local explanations, enable interpretable approximations of a model’s hidden logic.
• Provenance testing and decentralized auditing frameworks ensure accountability, reproducibility, and intellectual property protection in high-stakes machine learning applications.

Blackbox model provenance is the paper and practice of determining, explaining, and verifying the origins, dependencies, and decision processes of models—or model-generated outputs—when the internal implementation of those models is inaccessible ("blackbox"), as in proprietary inference APIs or opaque legacy systems. The field encompasses a range of methodologies from formal game-theoretic frameworks to statistical correlation tests and code-level provenance management systems, all targeted to facilitate accountability, intellectual property protection, reproducibility, and risk mitigation in contexts where models are either intentionally or unavoidably non-transparent.

1. Foundational Principles and Formal Models

Early formalizations of blackbox model provenance emerged from the database and program semantics communities. A foundational approach is the game-theoretic model of query provenance (Köhler et al., 2013), which frames the explanation of query results in terms of provenance games: two players engage in a dialogue over whether a query holds, with the provenance graph extracted as only the “good” (winning or delaying) moves required to establish a result. This framework naturally accommodates both “how” and “why-not” provenance: whether a result holds, and, if not, what missing conditions or inputs preclude it.

The semiring provenance model (Grädel et al., 2017) employs commutative semirings—algebraic structures of provenance polynomials that enumerate all data items, and in extensions, their complements—to track which facts contribute to (or preclude) a property. For blackbox settings, this model provides a formal abstraction: even if exact computation trees are inaccessible, the provenance polynomial summarizes the possible pathways by which the output depends on base data.

Beyond databases, a core calculus for provenance (Acar et al., 2013) generalizes to arbitrary (potentially blackbox) computations in higher-order functional languages. Here, execution traces are recorded and can be replayed (γ, e ⇓ v, T and γ’, T ⇓ v’), providing the basis for externally verifiable justifications of output with guaranteed “fidelity.” Such frameworks allow one to prove slice-based theorems (e.g., positive disclosure or obfuscation) even in the presence of hidden control flow, provided the trace is partially released.

2. Global and Local Model Explanation Extraction

For machine learning and decision systems, blackbox model provenance often relies on extraction of interpretable surrogates that approximate the inaccessible model’s logic via its input-output behavior. Global approaches include axis-aligned decision tree extraction (Bastani et al., 2017), where the unknown model f is treated as an oracle, and a tree T̂ is induced by actively sampling the input space (from an estimated distribution 𝒫), querying f(x), and greedily partitioning the input domain to maximize reduction in Gini impurity. The resulting surrogate tree T̂ mimics the decisions of f with high statistical fidelity, and its paths serve as interpretable explanations that reveal feature interactions and decision boundaries—making it possible to trace provenance of specific outputs from the learned surrogates back to the blackbox model, including identification of subpopulations and causal or non-causal effects.

Local-to-global frameworks (Pedreschi et al., 2018) begin by extracting local logic-based rules in the neighborhood of individual instances (using optimally generated local datasets N(x, y′) by auditing the blackbox), then aggregating these local rules via clustering or dendrogram algorithms to compose high-level global explanations. The local rules and their generalizations together form a structured understanding of both the immediate and aggregate logic encoded in the blackbox, allowing for auditing of bias, covariate shifts, or discrimination.

Shadow model creation (Patir et al., 2020) leverages blackbox data view extraction—synthesizing inputs that yield high-confidence outputs—and then training interpretable models on this “core” synthetic data, resulting in high-fidelity surrogates such as decision trees or formal concept lattices that explain the model’s essential logic.

3. Model Attribution, Provenance Testing, and Security

Regulatory and intellectual property demands have driven the development of provenance testing frameworks to determine whether a model (g) is derived from another (f), even in pure blackbox (API-only) scenarios (Nikolic et al., 2 Feb 2025). These frameworks typically operate by:

• Sampling high-entropy prompts and measuring similarity between next-token outputs of f and g across T prompts (μ = (1/T) Σ𝟙(f(xⱼ) = g(xⱼ)));
• Comparing this similarity μ to baseline similarities with a control set of unrelated models (μᵢ), then statistically determining whether μ is significantly larger, using multiple hypothesis z-tests and familywise error control (e.g., Holm–Bonferroni correction);
• Achieving high precision and recall in real-world open LLM benchmarks, with reliable detection of derivation relationships even at production scale.

Advanced techniques extend this to robust and formally grounded “palimpsestic” membership inference (Kuditipudi et al., 22 Oct 2025), exploiting the phenomenon that LLMs assign systematically higher log-likelihoods to data seen later in training. By correlating query log-likelihoods or observed artifact overlaps with the chronology tᵢ of original training data, one can construct exact hypothesis tests (e.g., using the Spearman rank correlation test statistic) with precise p-value control, even with minimal access (observational settings). These methods support proof-level provenance verification, applicable to both model APIs and post-hoc content attribution.

Reverse engineering blackbox neural network attributes through strategic probing (Oh et al., 2017, Kalin et al., 2020) also forms an axis of provenance: supervised metamodels are trained via probing with structured inputs and observing outputs, enabling inference of hidden architecture, optimization, and data properties. Such techniques reveal that the whitebox/blackbox distinction is porous, with considerable leakage of model internals solely from input-output behavior.

4. System Provenance, Lineage Management, and Decentralized Auditing

Blackbox model provenance is also addressed at the systems level, notably in provenance benchmarking and capture (Chan et al., 2019). Here, tools such as ProvMark treat the entire provenance system as a blackbox, executing predefined activities, recording the resulting provenance graphs, and using subgraph isomorphism (via Answer Set Programming) to verify that relevant system-level dependencies are captured (completeness/correctness) without accessing internals. This supports comparison and compliance testing across heterogeneous capture frameworks.

Decentralized and tamper-evident provenance architectures, such as those based on Hyperledger Fabric (Tunstad et al., 2019) or ProML's artifact-as-a-state-machine model (Tran et al., 2022), leverage blockchains and smart contracts to ensure that even in distributed, multi-party contexts (e.g., federated ML ecosystems), provenance records for blackbox models are resilient, verifiable, and immune to unilateral manipulation. Provenance records encompass hashes of datasets, transformations, and models, and encode their lifecycle as cryptographically secured state transitions, ensuring that chain-of-custody is preserved even as the model itself remains opaque.

5. Optimization, Surrogate Construction, and Counterfactual Discovery

In scenarios where the blackbox is an unknown function (e.g., a system, neural simulator, or parametrized quantum circuit), polynomial-based surrogate modeling (Schreiber et al., 2023) and trigonometric interpolation (Simon et al., 2023) are adopted to reconstruct local functional approximations or to guide blackbox optimization. These approaches select informative queries, construct classical surrogates (via Taylor expansion or Fourier polynomials), and quantify uncertainty (VarQ) to support not only prediction but also provenance—by exposing which evaluations and input regions the blackbox’s output depends on, and where surrogate uncertainty is highest.

Pipelines for programmatic decision boundary detection (Rathore, 30 Jun 2025) combine reinforcement learning to explore blackbox input spaces, counterfactual transition identification to localize decision boundaries, and clustering plus decision tree extraction to surface human-readable logic approximations, generating interpretable specifications and supporting modernization of legacy blackbox code.

6. Emerging Standards, Recordkeeping, and Auditability

Growing complexity and supply chain risk in the foundation model ecosystem have led to proposals for explicit, machine-readable specification formats and unified model record (UMR) repositories (Wang et al., 3 Oct 2024). These systems support hierarchical, semantically versioned model cards, automated dependency and genealogy tracking—including explicit documentation of upstream and downstream relationships—and multi-format publication (PDF, HTML, LaTeX, GraphViz). Such tooling aims to operationalize provenance and enable auditability, risk mitigation, and responsible model management industry-wide, supplementing but going beyond informal or non-machine-readable model cards.

7. Practical Implications and Open Challenges

Establishing blackbox model provenance is foundational for:

• Enforcing intellectual property and licensing; detecting unauthorized model derivatives (Nikolic et al., 2 Feb 2025, Kuditipudi et al., 22 Oct 2025).
• Tracing downstream model risk (e.g., when vulnerabilities or biases in upstream models propagate unnoticed) (Wang et al., 3 Oct 2024).
• Ensuring accountability, reproducibility, and trust in high-stakes applications (e.g., healthcare, legal, forensic, financial audit).
• Supporting regulatory compliance, e.g., GDPR right to explanation (Pedreschi et al., 2018), or reliability assessments in multilateral environments (Tran et al., 2022).

Remaining challenges include handling adaptive adversaries, optimizing query complexity for statistical tests, extending frameworks to high-dimensional and nondeterministic contexts, and developing universal, privacy-preserving standards for blackbox provenance attestation. Theoretical progress in compositional disclosure/obfuscation (Acar et al., 2013), reverse provenance analysis (Grädel et al., 2017), and statistical attribution (Kuditipudi et al., 22 Oct 2025) are gradually being translated into industrial and regulatory practice.

In summary, blackbox model provenance synthesizes formal, statistical, and system-level strategies to render origins, dependencies, and behaviors of non-transparent models traceable and auditable, serving as a critical enabler for trustworthy, legally defensible, and transparent machine learning and automated decision systems.