A Benchmark for Strategic Auditee Gaming Under Continuous Compliance Monitoring

• URL: http://arxiv.org/abs/2605.06340v1
• Date: Thu, 07 May 2026 14:28:34 GMT
• Title: A Benchmark for Strategic Auditee Gaming Under Continuous Compliance Monitoring
• Abstract summary: Continuous post-deployment compliance audits create a class of strategic gaming.<n>We formalize continuous auditing as a $T$-round Stackelberg game between an auditor that commits to a temporal policy and an adaptive auditee.
• Score: 1.253312107729806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continuous post-deployment compliance audits, mandated by emerging regulations such as the EU AI Act and Digital Services Act, create a class of strategic gaming distinct from the one-shot input/output gaming studied in prior work. Regulated systems can delay outcome reporting, drift their reports within plausible noise envelopes, exploit longitudinal sample attrition, and cherry-pick among ambiguous metric definitions. We formalize continuous auditing as a $T$-round Stackelberg game between an auditor that commits to a temporal policy and an adaptive auditee, and identify a structural feature of any noise-aware static-auditor design: a cover regime in which coverage gaps and granularity gaps cannot be closed simultaneously. We make this formal as Observation 1 and show that two minimal extension policies, each derived from the observation, close the regime along orthogonal axes: a sample-size-aware static rule (Periodic-with-floor) closes the granularity-failure case, while a history-conditioned suspicion-escalation policy closes the coverage-failure case for the naive Drift strategy -- and neither closes both, exactly as the observation predicts; an audit-aware OffAuditDrift strategy that exploits Stackelberg commitment defeats both. To support empirical study we contribute a non-additive harm decomposition (welfare loss $W$, coverage loss $C$) that exposes how attrition shifts harm from the regulator-accountable surface to a regulator-invisible one; an initial library of five auditee strategies (Delay, Drift, Cherry-pick, Attrition, OffAuditDrift) and five auditor policies, calibrated to summary statistics from published audits of the DSA Transparency Database; and a reproducible simulator with a small, extensible Python interface.

Related papers

• One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agentic AI [0.0]
  We show that a remediation applied by one control can change the action, evidence, or context another control evaluates, invalidating that control's earlier judgment.<n>We formalize this coupling and give a remediate-and-regate protocol that restores per-action soundness in the current bounded, idempotent setting.<n>This is a mechanism demonstration on open payload data with a synthetic metadata layer, not a claim about production prevalence.
  arXiv  Detail & Related papers  (2026-08-18T22:24:44Z)
• SynthGuard-ReleaseBench: Locked-Audit Evidence for Synthetic Tabular Data Releases [0.0]
  We introduce SynthGuard-ReleaseBench, an audit framework that locks the use, candidate panel, tolerances, and audit schedule before evaluation.<n>It compares real-trained and synthetic-trained on protected data, gives simultaneous finite-sample bounds for loss gaps, requires controls, and keeps utility, empirical privacy risk mechanism claims separate.
  arXiv  Detail & Related papers  (2026-08-14T03:12:17Z)
• Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle [65.6642776933861]
  This survey examines the protective paradigm that has grown around this intervention point.<n>We find that most protections are still validated mainly against static or weakly adaptive adversaries.<n>We close by consolidating cross-stage countermeasures and open problems for robust, composable, and deployable owner-side protection.
  arXiv  Detail & Related papers  (2026-08-05T00:46:50Z)
• SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute [62.3458279176813]
  Self-Verifying Refinement is an oracle-free multi-turn reinforcement learning framework.<n>It learns to use self-verification as a compute-control policy.<n>It achieves a macro-average accuracy of 0.563 with only 2.99 inference turns on average.
  arXiv  Detail & Related papers  (2026-07-30T16:20:58Z)
• The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models [47.471225029744424]
  Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models.<n>We show that the choice of anti-collapse regulariser determines whether a JEPA's training objective, a prediction loss plus a weighted embedding regulariser, is a valid Active Inference (AIF) variational free energy.
  arXiv  Detail & Related papers  (2026-07-15T08:59:36Z)
• Evaluating AI Investment Strategies [0.0]
  We study the problem of auditing a black-box algorithmic decision-maker from observable inputs and outputs alone.<n>Under precisely characterized conditions, the cumulative emphregret of a dynamic policy equals the sum of per-period covariances between the cost vector and the policy's decision.<n>We prove the identity holds exactly under i.i.d. costs and mean-unbiased Markov policies, derive closed-form bias corrections for non-stationary and time-varying cases, and establish the discounted-horizon analog.
  arXiv  Detail & Related papers  (2026-06-07T19:16:50Z)
• What to Test Next: Interpretable Coverage Gap Discovery in Driving VLMs [52.50210189669399]
  Driving vision-language models (VLMs) must accurately understand scenes across diverse conditions defined by Operational Design Domains (ODDs)<n>We propose SliceScorer, a deterministic scoring rule for missing-slice recommendation.<n>We show that SliceNav surfaces high-risk coverage gaps more effectively than prior slice-discovery methods.
  arXiv  Detail & Related papers  (2026-06-01T03:18:01Z)
• Auditing Privacy in Multi-Tenant RAG under Account Collusion [1.253312107729806]
  Multi-tenant retrieval-augmented generation services advertise per-account differential privacy as the operative leakage boundary.<n>We identify same-index multi-account collusion as a privacy-boundary failure.<n>We design the first audit protocol that operates against unmodified RAG deployments.
  arXiv  Detail & Related papers  (2026-05-19T13:41:59Z)
• Differentially Private Auditing Under Strategic Response [0.0]
  We study audit design when the audited developer can strategically respond to the privacy-constrained audit interface.<n>We formalize privacy-constrained auditing as a bilevel Stackelberg game, in which an auditor commits to a query policy and DP budget allocation across harm dimensions.<n>We propose Strategic Private Audit Design (SPAD), a projected-gradient algorithm with hypergradients computed through the developer's best response.
  arXiv  Detail & Related papers  (2026-05-08T12:44:03Z)
• ECoLAD: Deployment-Oriented Evaluation for Automotive Time-Series Anomaly Detection [1.7751300245073598]
  Time-series anomaly detectors are commonly compared on workstation-class hardware under unconstrained execution.<n>In-vehicle monitoring, however, requires predictable latency and stable behavior under limited CPU parallelism.<n>We present ECoLAD, a deployment-oriented evaluation protocol instantiated as an empirical study on proprietary automotive telemetry.
  arXiv  Detail & Related papers  (2026-03-11T16:08:56Z)
• Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG [5.4716896485317195]
  Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics.<n>We introduce MMA-RAGT, an inference-time control framework governed by a Modular Trust Agent (MTA) that maintains an approximate belief state.
  arXiv  Detail & Related papers  (2026-02-24T23:52:27Z)
• Conformal Tradeoffs: Guarantees Beyond Coverage [0.02648566468224904]
  Deployed conformal predictors are long-lived decision infrastructure operating over finite operational windows.<n> Marginal coverage does not determine deployment-facing quantities.<n>We provide a framework for operational certification and planning beyond coverage.
  arXiv  Detail & Related papers  (2026-02-20T07:58:25Z)
• Preventing the Collapse of Peer Review Requires Verification-First AI [49.995126139461085]
  We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth.<n>We formalize two forces that drive a phase transition toward proxy-sovereign evaluation.
  arXiv  Detail & Related papers  (2026-01-23T17:17:32Z)
• Audit the Whisper: Detecting Steganographic Collusion in Multi-Agent LLMs [0.0]
  Audit the Whisper is a conference-grade research artifact that spans theory, benchmark design, detection, and validation.<n>Our contributions are: (i) a channel-capacity analysis showing how interventions such as paraphrase, rate limiting, and role permutation impose quantifiable capacity penalties-operationalised via paired-run Kullback-Leibler diagnostics.<n>We release anonymized regeneration scripts, anonymized manifests, and documentation so external auditors can reproduce every figure, satisfy double-blind requirements, and extend the framework with minimal effort.
  arXiv  Detail & Related papers  (2025-10-05T17:51:52Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• When Does Confidence-Based Cascade Deferral Suffice? [69.28314307469381]
  Cascades are a classical strategy to enable inference cost to vary adaptively across samples. A deferral rule determines whether to invoke the next classifier in the sequence, or to terminate prediction. Despite being oblivious to the structure of the cascade, confidence-based deferral often works remarkably well in practice.
  arXiv  Detail & Related papers  (2023-07-06T04:13:57Z)
• Hallucinated Adversarial Control for Conservative Offline Policy Evaluation [64.94009515033984]
  We study the problem of conservative off-policy evaluation (COPE) where given an offline dataset of environment interactions, we seek to obtain a (tight) lower bound on a policy's performance. We introduce HAMBO, which builds on an uncertainty-aware learned model of the transition dynamics. We prove that the resulting COPE estimates are valid lower bounds, and, under regularity conditions, show their convergence to the true expected return.
  arXiv  Detail & Related papers  (2023-03-02T08:57:35Z)

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.