Silent Updates: Measuring and Closing the Post-Deployment Disclosure Gap

• URL: http://arxiv.org/abs/2608.11803v1
• Date: Wed, 12 Aug 2026 08:45:49 GMT
• Title: Silent Updates: Measuring and Closing the Post-Deployment Disclosure Gap
• Abstract summary: We examine post-deployment disclosure practices across first-party API providers and inference hosts.<n>We introduce the Silent Updates Scorecard, a public instrument for measuring post-deployment disclosure practices.<n>We also propose a Three-Part Behavioral Trigger System for determining when post-deployment modifications to a system motivate disclosure or re-evaluation obligations.
• Score: 0.9927426295376472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deployed foundation models are often not static systems, with providers able to modify system behavior through fine-tuning, classifier updates, system prompt revisions, retrieval changes, and routing changes. These updates can be made silently -- that is, without public disclosure, a version increment, or re-evaluation. Such silent updates challenge a core assumption behind current AI governance frameworks that an externally verifiable chain of custody links the model referred to in evaluation results or a system card to the model served to users. In this paper, we examine post-deployment disclosure practices across first-party API providers and inference hosts to establish the extent to which a chain of custody exists in practice. We find that providers commonly publish substantial safety documentation, including quantitative evaluations and version-specific reports, but no provider in our sample published information allowing an external party to verify that the artifact being served is the same one referred to in this documentation. We introduce the Silent Updates Scorecard, a public instrument for measuring post-deployment disclosure practices across providers and hosts, and preliminary results for a sample of nine first-party API providers and seven third-party inference hosts. We also propose a Three-Part Behavioral Trigger System for determining when post-deployment modifications to a system motivate disclosure or re-evaluation obligations.

Related papers

• Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineering [78.51705689800838]
  We propose an assurance-revision loop that uses deployment evidence to revise the requirements, model, or evaluator.<n>We then cast assured agentic development as a resource-allocation problem over human judgment, agent capability, and compute.
  arXiv  Detail & Related papers  (2026-09-10T17:58:48Z)
• AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes [38.37599802008238]
  Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped.<n>All existing audits decide backbone identity from the text-output channel.<n>We introduce Agentic Provenance (AgentProv), the first action-based identity audit for agentic LLM APIs.
  arXiv  Detail & Related papers  (2026-08-30T08:22:12Z)
• Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models [74.1961755473754]
  Existing evaluation methods rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations.<n>We propose the Evaluation Agent framework, which employs human-like strategies for efficient, dynamic, multi-round evaluations.<n>We show that Evaluation Agent reduces evaluation time to 10% of traditional methods while delivering comparable results.
  arXiv  Detail & Related papers  (2026-08-10T14:42:10Z)
• Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data [56.86147283213652]
  We present a customizable empirical auditing framework designed to detect and explain data disclosures.<n>Our framework introduces a mechanism to distinguish between "true disclosures"-where the system directly reproduces a user's information-and "phantom disclosures"-where the system incidentally generates a user's data.
  arXiv  Detail & Related papers  (2026-06-15T16:54:02Z)
• AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows [0.0]
  AuditWeave is a lightweight Python library that records the steps of AI-assisted and data-transformation into a single hash-chained ledger.<n>We describe the design and evaluate recording overhead, scalability, and tamper-detection correctness on the reference implementation.
  arXiv  Detail & Related papers  (2026-06-14T21:46:04Z)
• GCVE: A Decentralized Model for Vulnerability Identification, Publication, and Operational Enrichment [51.77969450792284]
  The Global CVE initiative (G) proposes a decentralized, open, and identifier model for vulnerability identification, publication, and enrichment.<n>This paper presents G as socio-technical effort combining autonomous G Numbering Authorities, lightweight allocation rules, distributed publication, open Best Current Practices, and practical reference implementations.
  arXiv  Detail & Related papers  (2026-05-30T19:03:39Z)
• Test Before You Deploy: Governing Updates in the LLM Supply Chain [3.6704226968275253]
  Large Language Models (LLMs) are increasingly used as core dependencies in software systems.<n>LLMs evolve continuously through provider-side updates without explicit version changes.<n>This paper proposes a deployment-side governance framework based on three components.
  arXiv  Detail & Related papers  (2026-04-30T12:32:13Z)
• Making AI-Assisted Grant Evaluation Auditable without Exposing the Model [0.0]
  We propose a TEE-based architecture that helps reconcile requirements through remote attestation.<n>The architecture allows an external verifier to check which model, rubric, prompt template, and input representation were used.<n>We position the design relative to confidential AI inference, attestable AI audits, zero-knowledge machine learning, algorithmic accountability, and AI-assisted peer review.
  arXiv  Detail & Related papers  (2026-04-28T04:10:04Z)
• Verifiable Fine-Tuning for LLMs: Zero-Knowledge Training Proofs Bound to Data Provenance and Policy [0.0]
  We present Verifiable Fine Tuning, a protocol and system that produces succinct zero knowledge proofs.<n>We show that the system composes with probabilistic audits and bandwidth constraints.<n>Results indicate that the system is feasible today for real parameter efficient pipelines.
  arXiv  Detail & Related papers  (2025-10-19T13:33:27Z)
• Towards Real-Time Fake News Detection under Evidence Scarcity [66.58597356379907]
  We propose Evaluation-Aware Selection of Experts (EASE), a novel framework for real-time fake news detection.<n>EASE adapts its decision-making process according to the assessed sufficiency of available evidence.<n>We introduce RealTimeNews-25, a new benchmark for evaluating model generalization on emerging news with limited evidence.
  arXiv  Detail & Related papers  (2025-10-13T11:11:46Z)
• OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs [59.836774258359945]
  OpenFactCheck is a framework for building customized automatic fact-checking systems.<n>It allows users to easily customize an automatic fact-checker and verify the factual correctness of documents and claims.<n>CheckerEVAL is a solution for gauging the reliability of automatic fact-checkers' verification results using human-annotated datasets.
  arXiv  Detail & Related papers  (2024-05-09T07:15:19Z)
• Client-specific Property Inference against Secure Aggregation in Federated Learning [52.8564467292226]
  Federated learning has become a widely used paradigm for collaboratively training a common model among different participants. Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data. We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
  arXiv  Detail & Related papers  (2023-03-07T14:11:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.