Telemetry and Concealment in Self-Adapting Generative AI: Logging Architecture, Adversarial Model Hiding, and the Limits of Detection

This paper addresses the governance problem posed by continually self-adapting generative AI systems—models that update their own weights during production deployment—which fundamentally violate the static model lifecycle assumptions of traditional model risk management (MRM) guidance. The paper is structured in two parts: Part I develops a rigorous telemetry architecture for such models, and Part II analyzes what happens when the model owner is adversarial and seeks to conceal learning updates.

Part I: Telemetry Architecture

The paper establishes a Minimal Sufficient Statistic for audit purposes in discrete time, formalized as the State Digest, and argues that weight-space distance is a poor proxy for the relevant governance quantity: behavioral divergence. The primary audit metric is the behavioral divergence δt, defined as the KL divergence between consecutive model states on a canonical reference set R: δt = DKL(pWt−1(·R) ∥ pWt(·R)). The paper notes that KL divergence is not a metric and obeys no triangle inequality, so the cumulative drift Δt is an action-like, path-dependent functional that should be interpreted as a cumulative activity measure rather than as a displacement from W0.

The paper constructs a tamper-evident Merkle Weight Chain that binds each model state to its predecessor and training data: ht = Hash(Wt ∥ ht−1 ∥ Hash(Bt)). Proposition 3.6 establishes that "any retroactive modification to Ws for s < T invalidates every hs′ for s′ ≥ s with overwhelming probability," so the validator need only verify the chain tip hT.

For event-driven logging, the paper proposes KL divergence stopping times where logging occurs only when cumulative behavioral drift exceeds a threshold ϵ. This ensures a static model generates zero log packets while a rapidly adapting model generates dense packets, automatically flagging the adaptation intensity to the validator. Theorem 4.4 establishes that inter-arrival times form a stationary renewal sequence with mean T̄ ≈ ϵ/δ̄, where δ̄ is the stationary mean instantaneous KL rate.

In continuous time, the weight process is modeled as an Itô SDE: dWt = −∇W J(Wt) dt + √η Σ(Wt)1/2 dBt. The paper derives the Quadratic Variation as the canonical telemetry scalar: [W]t = ∫0t tr(η Σ(Ws)) ds. Proposition 4.2 shows this is incrementally computable, non-decreasing, and isolates the stochastic component of learning from deterministic drift.

The framework is embedded within a Lyapunov stability theory using a Risk Lyapunov Function V(W) = ½∥W − projS(W)∥2, measuring squared distance to the validated safe manifold S. The paper derives both mean-stability conditions (Definition 5.3) and almost-sure path-wise stability via the Burkholder–Davis–Gundy inequality and the Law of the Iterated Logarithm (Theorem 5.5). The mean stability condition includes an unavoidable noise floor V∞, since "at V = 0 the drift equals η/2 tr(Σ ∇2V) > 0, so V cannot be driven to 0 in expectation."

The paper proposes a CUSUM governance trigger on the behavioral divergence sequence, with the statistic Ct = max(0, Ct−1 + δt − κ) and tier-specific thresholds h1, h2. Theorem 6.2 cites classical results that CUSUM minimizes worst-case expected detection delay among all stopping rules with a fixed false-alarm rate.

The integrated architecture consists of five layers: Probe, Hash, Stability, Escalation, and Ledger. The complete log packet is Dτk = hτk, [W]τk, Δτk, Cτk, V(Wτk), τk .

Part II: The Model Hiding Problem

The paper formalizes the adversarial game between a model owner (who has incentives to conceal learning updates that would trigger mandatory validation review) and a validator. It provides a systematic taxonomy of six distinct attack strategies with formal countermeasures:

Discrete-time attacks:

1. Attack S (Suppression): The firm updates weights but transmits no telemetry. Countermeasure: a Behavioral Oracle with KL Detection using a secret canary set C. Theorem 11.2 establishes detection consistency: under H0 (static model), Ttoracle = 0 exactly; under H1 (drift), Ttoracle > 0 provided the canary set is behaviorally sufficient.

2. Attack F (Lyapunov Forgery): The firm reports a compliant decoy state hash while serving inference from a drifted model. Countermeasures: (a) Inference Signing, where every production output is signed with a commitment to the current model hash, and (b) Zero-Knowledge Proofs of Lyapunov Compliance using zk-SNARKs.

3. Attack C (Shadow Model Side-Channel): The firm maintains two models—a reported one and an actual serving one—blending outputs. Countermeasure: Trusted Execution Environment (TEE) Attestation, which binds the training pipeline to hardware. Proposition 11.9 establishes completeness under the TEE threat model.

Continuous-time attacks:

4. Attack N (Diffusion-Amplified Drift Masking): The firm amplifies noise to reduce the signal-to-noise ratio of hidden drift. The paper shows the natural countermeasure fails: Proposition 12.1 proves that the power-variation ratio measures the sampling frequency, not the drift because the first variation of a diffusion is infinite. The ratio scales as Θ(√n) regardless of drift.

5. Attack G (Girsanov Measure Manipulation): The firm manipulates training data to induce a change of probability measure, making the trajectory appear compliant. The paper shows the gradient probe countermeasure is circular: Proposition 12.5 proves that the probe response is a deterministic function of the current weights and the probe batch and does not depend on the firm's training measure at all, except through the value of Wsj.

6. Attack H (Hessian Exploitation via Noise Energy): The firm engineers local curvature to exploit the second-order Itô term, running adverse drift through a low-noise corridor. Countermeasure: Almost-Sure Stability via the Law of the Iterated Logarithm. Theorem 12.9 establishes a dichotomy: either the firm truthfully declares its quadratic variation (and the adverse drift makes the realized residual leave the LIL envelope), or it under-declares (and the realized fluctuations exceed the declared envelope). The firm cannot simultaneously (a) run an adverse deterministic drift, (b) declare second-order telemetry consistent with its realized path, and (c) pass both the drift certificate and envelope check.

The Information-Theoretic Residual

The paper's central negative result is Theorem 13.3: "at any finite detection resolution δ > 0 the set of undetectable weight perturbations contains a ball of strictly positive radius ϵ∗(δ), so no telemetry-only regime can detect arbitrarily small adversarial drift. The proof relies on continuity of the canary output map: by continuity of pW in W, a sufficiently small perturbation in any direction keeps the canary KL below δ. The paper explicitly states what is not claimed: The argument establishes ϵ∗(δ) > 0 and its monotonicity; it does not produce a closed-form or asymptotic expression for ϵ∗(δ), and in particular no dependence on telemetry bandwidth is derived."

The paper also presents a counting argument (Heuristic 13.1) suggesting why a residual should exist in high dimension, but explicitly flags it as a motivating counting argument, not a theorem, noting that differential entropy can be negative, is not invariant under reparameterization, and does not count bits.

The Dual-Regime Principle

The paper concludes with a design recommendation: continuous telemetry is necessary but not sufficient, and must be paired with periodic invasive audits whose frequency caps the undetectable leakage. The audit frequency is calibrated as f∗ = μhidden/ϵtol, where μhidden is the maximum hidden drift rate and ϵtol is the tolerance. The paper emphasizes this is a design recommendation, not a mathematical claim.

Numerical Studies

The paper presents ten numerical studies (five per part) using a lightweight softmax classifier with d=8, K=3, and m=80 reference points. Key findings include:

• Study A1: Gradient-direction perturbations produce 2.33× higher KL divergence than equal-norm tangent perturbations.

• Study A2: A perturbation of magnitude 10−2 in weight space invalidates all subsequent hashes in the Merkle chain.

• Study A3: Static models emit 0 packets; fast-adapting models emit 128 packets with mean inter-arrival 3.9 steps.

• Study A4: Empirical quadratic variation matches theory within 0.56%.

• Study A5: Lyapunov decay matches exact formula within 1.1% relative error; CUSUM detects drift with delays of 7, 1, and 0 steps for increasing drift magnitudes.

• Study B1: Behavioral oracle detects all drift levels, with statistic monotone in learning rate.

• Study B2: Power-variation ratio is identical with and without drift (25.3 vs 25.3 at n=1000), confirming Proposition 12.1.

• Study B3: Gradient probe detection is tautological—it detects only the shift it injects.

• Study B4: An under-declared σ produces 99.7% violation rate against a 0.9% honest rate, while mean Lyapunov levels are statistically indistinguishable.

• Study B5: At ε=0.01, all 20 perturbations fall below the detection threshold; at ε=0.05, none do.

The paper acknowledges limitations: the numerical studies are verification experiments at toy scale that cannot test the high-dimensional counting argument, and the attacks are implemented exactly as modeled rather than discovered by an adversary optimizing against the defences.

Improvements to AI Systems:

1. Tamper-Evident Model Lifecycle Tracking: Implement a Merkle Weight Chain that cryptographically binds each model state to its predecessor and training data. The improved AI system can prove its entire update history is unmodified, enabling regulators and users to verify that no retroactive weight changes occurred without requiring full model access.

2. Behavioral Drift Telemetry: Replace weight-space distance monitoring with KL-divergence-based behavioral divergence on a canonical reference set. The improved AI system can quantify actual behavioral change between versions, flagging adaptation intensity with zero overhead for static models and dense logging for rapidly evolving ones.

3. Event-Driven Audit Compression: Use KL-divergence stopping times to log only when cumulative behavioral drift exceeds a threshold. The improved AI system can generate a compact, tamper-evident audit trail that scales with actual behavioral change rather than wall-clock time, reducing storage and review costs by orders of magnitude.

4. Quadratic Variation as a Learning Activity Meter: Track the quadratic variation of the weight process as a non-decreasing, incrementally computable scalar. The improved AI system can distinguish stochastic exploration from deterministic optimization, providing a precise measure of how much learning noise occurred during deployment.

5. Lyapunov-Based Safety Envelope: Maintain a Risk Lyapunov Function measuring distance to a validated safe manifold. The improved AI system can continuously certify that its current state remains within a provably safe region, with mean-stability and almost-sure path-wise guarantees, including an explicit noise floor that prevents overconfident safety claims.

6. CUSUM Governance Triggers: Apply cumulative sum control charts to behavioral divergence sequences with tiered thresholds. The improved AI system can minimize worst-case detection delay for harmful drift while maintaining a fixed false-alarm rate, enabling automatic escalation to human review only when statistically justified.

7. Inference Signing for Provenance: Sign every production output with a commitment to the current model hash. The improved AI system can prove which exact model version generated each response, preventing a firm from serving a drifted model while reporting a compliant one.

8. Zero-Knowledge Compliance Proofs: Use zk-SNARKs to prove Lyapunov compliance without revealing weights. The improved AI system can demonstrate it satisfies safety constraints to third parties without exposing proprietary model internals, enabling trustless verification.

9. Hardware-Bound Training Attestation: Bind training pipelines to Trusted Execution Environments. The improved AI system can provide cryptographic proof that updates occurred within approved hardware, eliminating shadow-model side-channel attacks where a separate unlogged model serves inference.

10. Almost-Sure Drift Certificates via Law of Iterated Logarithm: Enforce that realized quadratic variation must match declared values within an LIL envelope. The improved AI system can detect adversarial drift even when the firm under-declares noise, because realized fluctuations will inevitably exceed the declared envelope—a fundamental mathematical constraint.

11. Dual-Regime Audit Scheduling: Pair continuous telemetry with periodic invasive audits at frequency f* = μ hidden/ε tol. The improved AI system can cap undetectable adversarial drift below any tolerance by scheduling deep inspections, acknowledging that telemetry alone cannot detect arbitrarily small perturbations.

12. Canary-Set Behavioral Oracles: Maintain a secret, behaviorally sufficient canary set for KL-based detection. The improved AI system can detect suppression attacks with zero false positives on static models and monotone detection power on drifted ones, without revealing the canary set to the model owner.

Capabilities of the Improved AI System:

• Provable Update Integrity: Any deployed AI system can demonstrate its complete update history is authentic and unmodified, enabling regulatory compliance without full transparency.

• Automatic Escalation: The system self-monitors behavioral drift and triggers human review only when statistically justified, balancing safety with operational autonomy.

• Adversarial Robustness: It can resist six distinct concealment strategies, including suppression, forgery, side-channeling, noise amplification, measure manipulation, and curvature exploitation.

• Quantified Safety Margins: It can report exact distances to unsafe regions and provable bounds on undetectable drift, enabling informed risk acceptance.

• Efficient Auditability: It produces compact, event-driven audit logs that are tamper-evident and scale with actual behavioral change, not time.

• Trustless Verification: Third parties can verify safety compliance without accessing proprietary weights, via zero-knowledge proofs and signed inferences.

• Fundamental Limit Awareness: It acknowledges and communicates the information-theoretic residual—that no telemetry-only system can detect arbitrarily small drift—and therefore pairs continuous monitoring with scheduled invasive audits to cap leakage.

Model risk management (MRM) guidance assumes a static model lifecycle, in which models are developed, independently validated, and implemented without further autonomous modification. Continually self-adapting generative AI systems --- models that update their own weights during production deployment --- fundamentally violate this assumption and render point-in-time validation inadequate. This paper addresses the resulting governance problem in two parts. Part I develops a rigorous telemetry architecture for such models, operating simultaneously in discrete and continuous time. We establish a Minimal Sufficient Statistic for audit purposes, construct a tamper-evident Merkle chain for discrete weight sequences, derive the appropriate continuous-time generalization via the Ito formula, and propose event-driven logging via KL divergence stopping times that is both computationally tractable and meaningful for validation. Part II asks what happens when the model provider is adversarial. A firm deploying such a model has strong incentives to conceal learning updates that would trigger mandatory validation review. We formalize this as the Model Hiding Problem and provide a systematic taxonomy of six distinct attack strategies against the Part I architecture, spanning discrete and continuous time, with a formal countermeasure for each. Together the two parts establish a dual-regime architecture in which continuous telemetry is necessary but not sufficient, narrowing---but never replacing---periodic invasive audit. The framework is model-architecture-agnostic and is designed to satisfy the three pillars of traditional MRM.