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Blindfold: Keeping the Audit Coin Secret from LLM Agents

Wei Zhu, Meiyun Zuo, Huiping Sun

arXiv:2610.37055v1cs.CRcs.AIcs.LG

TL;DR

Audit-based certification can be biased when an adaptive agent learns the audit coin before acting. The paper formalizes the needed pre-commit confidentiality condition and analyzes leakage, finding that visible audit notices can undermine the plain certificate while silent auditing restores coverage in the reported setting.

  • Problem

    Audit-based certification is intended to rely on the defender’s random audit draw, but an agent that learns the draw before acting can defer violations and leave audited labels unrepresentative.

    Source: §I · p. 1

  • Method

    The paper formalizes pre-commit noninterference and analyzes how an agent’s view of the audit coin affects certificate bias and coverage.

    Source: §X · p. 13

  • Results

    With an audit-like prompt shown on every episode, the plain certifier’s real-label coverage is 1.00 at ρd∈{0.25, 0.5, 1}, versus 0.07 when the prompt appears only on audited episodes.

    Source: Appendix G · p. 20

  • Takeaways & Limitations

    A silent kernel or a fresh per-call coin can restore validity by removing the relevant leakage, whereas padding only narrows the timing channel.

    Source: §X · p. 13

    A shared, agent-readable load statistic can leak an episode’s coin under concurrent batch processing, and per-call coins do not close that channel.

    Source: §VIII · p. 12

Abstract

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Audit-based certification bounds the violation rate of an LLM agent by auditing a random fraction $\varepsilon$ of actions and reweighting the audited labels; its validity is meant to rest only on the defender's audit coin. We show that it also rests on a confidentiality property: the coin must not interfere with the agent's view before the action commits, at a time the agent chooses. We call this pre-commit noninterference (PNI) and measure its violation by $D_{\mathrm{A}}$, the total-variation distance between the agent's views under the two coin values. PNI is necessary and sufficient for an unbiased certificate against adaptive agents, the worst-case bias is $(1-\varepsilon)D_{\mathrm{A}}$, and no procedure that sees only audited labels can pay less. Leakage composes across steps, a resource shared across episodes opens a channel of computable size, and raising the audit rate cannot restore validity. On a monitor we built, a silent audit changes no output but adds latency that tool timestamps expose, and the agent can enlarge it. A real LLM agent follows an injection that names a visible audit notice (generic, tool, and timing clauses have no effect); over that channel the plain certificate fails on measured labels and, by a post-hoc analysis, in a live run on a pre-registered schedule, while a silent audit restores it. A fresh coin per audited call closes the timing channel; padding only narrows it.

I. INTRODUCTION

Audit-based certification can undercount when an agent learns the audit coin before committing, despite relying on the defender’s random audit draw. The paper names the required confidentiality property pre-commit noninterference and studies its definition, costs, and enforcement.

I. INTRODUCTION

  • If the agent can infer which episodes are audited, it can defer attempts, leaving audited labels clean and causing the certificate to undercount.A constructed agent given the audit signal attempts violations in 79.2% of episodes while the estimate is 0.0%.Source: §I · p. 1
  • Pre-commit audits are needed for trajectory-unit audits, non-persisted or commit-time label inputs, and ask-type gates; a purely post-hoc audit cannot leak its coin before commit.A post-hoc audit is safe for the commit it labels when each coin governs one commit and the post-commit label measures the same quantity.Source: §I · p. 1, §III · p. 4
  • Prior work on random auditing does not state or measure the assumption that the audited system cannot observe the draw, which the paper treats as an identification condition.The paper asks whether the reported violation rate remains meaningful without assumptions about the agent’s motives.Source: §I · p. 1

III. PRE-COMMIT NONINTERFERENCE

  • The paper defines pre-commit noninterference (PNI) as keeping the audit coin out of the agent’s view until commit, and uses total-variation distance D_A to quantify leakage.For an episode coin, PNI requires the coin and pre-commit view to be independent for every policy and reachable history.Source: §I · p. 2, §III · pp. 3–4

I. INTRODUCTION

  • A real LLM follows an injection naming a visible audit notice; the plain certificate then fails on measured labels and in a pre-registered live-run analysis, while a silent audit restores coverage.The paper does not establish spontaneous or timing-based exploitation by a real LLM.Source: §I · p. 2

II. SETTING AND THREAT MODEL

  • The threat model allows adaptive agents that observe gate and tool outputs, including timestamps, but trusts the defender’s coin generation, certifier, and label oracle.Agents that read the seed, compromise the gate, or conceal violations from the auditor are out of scope.Source: §II · pp. 2–3

IV. LEAKAGE BOUNDS THE CERTIFICATE’S BIAS

This section prices how pre-commit leakage biases the audit certificate. It relates that bias to audit distinguishability, establishes PNI’s role in unbiased certification, and explains why increasing the audit rate is not a general remedy.

A. The one-step price

  • gapp_i = (1−ε)(µ0−µ1), so the certificate’s bias is the audit-rate factor times the difference between unaudited and audited attempt rates.Under the episode coin, the IPW estimate has conditional expectation µ1.Source: A. The one-step price · p. 4
  • The unaudited-versus-audited attempt-rate gap is at most D_A and is attained by a likelihood-ratio adversary; a real agent may exploit less leakage than D_A.E5’s notice channel has D_A=1, while its agent realizes κ_A=0.075.Source: A. The one-step price · p. 4
  • The worst-case certificate bias equals (1−ε)D_A, making audit distinguishability an exact price for leakage under an episode coin.The result identifies the supremum bias over commit rules with additive g-leakage.Source: A. The one-step price · p. 4
  • Post-processing cannot increase channel distinguishability or worst-case bias, and the refinement order compares channels through every η.The paper places audit mechanisms within a quantitative-information-flow refinement order.Source: A. The one-step price · pp. 4–5
  • For every adaptive adversary, PNI is necessary and sufficient for an unbiased certificate; under PNI, the bounded-martingale procedure gives an anytime-valid confidence sequence.The adaptive-rule criterion requires the true commit-step propensity to equal its design propensity given the view.Source: A. The one-step price · p. 5
  • Bias falls only linearly with ε while sequence width falls as 1/√(nε), and raising ε can bring the coverage failure forward.The stated failure-point result holds for a fixed exploited gap and base rate over (0, ε⋆].Source: A. The one-step price · p. 5

B. Multi-step channels and adaptive audits

This section extends the leakage analysis across steps and to adaptive audit rules. It gives composition bounds, characterizes when adaptive certification is unbiased, and describes computational protection for keyed rules.

B. Multi-step channels and adaptive audits

  • Sequential leakage is bounded by a coupling argument, with the middle bound attained by erasure channels when stepwise distinguishability is history-independent and the adversary can wait.For noisy channels, the operative composition bound instead uses KL divergence.Source: B. Multi-step channels and adaptive… · p. 5
  • For noisy observations with mutually absolutely continuous conditional laws, the composition bound uses per-step KL divergence; measured latency laws give a numerical comparison with the coupling bound.After 104 probes, the noisy-channel bound is 0.69, compared with a per-step sum of 55.Source: B. Multi-step channels and adaptive… · p. 5
  • Under adaptive audits, unbiasedness for every policy holds exactly when the commit-step coin is fresh given the view; leakage from other coins does not affect it.Under PNI the certificate is anytime-valid, while persistent leakage can make coverage tend to zero under the stated excess-width condition.Source: B. Multi-step channels and adaptive… · p. 5
  • If the exploited gap decays as n^-β, its running bias scales as n^-β against confidence width n^-1/2, so coverage still fails for β<1/2 and need not for β>1/2.Coverage does not fail at finite n merely because distinguishability is positive; the threshold depends on the realized exploited gap.Source: B. Multi-step channels and adaptive… · pp. 5–6
  • The failure threshold is sensitive to the exploited gap: changing it from 0.075 to 0.055 moves n⋆ from 1.6×10^3 to 3.1×10^3.This sensitivity motivates pre-registering an interval rather than a point for E8.Source: B. Multi-step channels and adaptive… · p. 6
  • A PRF-keyed adaptive rule can be analyzed by reduction to a truly random function, but the key remains inside the certifier’s trust boundary.Keying on history alone reuses a coin when histories recur; the input includes an episode nonce and call index.Source: B. Multi-step channels and adaptive… · p. 6

C. Repair, sharpness, and rare violations

The paper gives a leakage-inflated certificate and shows its additive price is sharp for certifiers that see only audited labels. It also identifies limits of the guarantee for adaptive rules and rare violations.

C. Repair, sharpness, and rare violations

  • Theorem 5 adds (1−ε)δ to the certificate for all n simultaneously with probability at least 1−α.The bound assumes DA(h; A) ≤ δ for every policy and every reachable history.Source: C. Repair, sharpness, and rare… · p. 6
  • Across steps, the adaptive-rule inflation is δ′ ≥ Λ, with δ = 1 − ∏t(1−δ̄t) and δ̄t ≥ D̄t.The same inflation covers the realized rate after a second, leakage-independent martingale term; under PNI a sharper sequence certifies that rate directly.Source: C. Repair, sharpness, and rare… · p. 6
  • No audited-label-only procedure can certify below µ1 + min(δ, 1−µ1), and Theorem 5 attains that endpoint up to confidence-sequence width.The compatible values of µ0 form the interval [max(0, µ1 − δ), µ1 + min(δ, 1−µ1)].Source: C. Repair, sharpness, and rare… · p. 6
  • For rare violations, additive inflation (1−ε)δ can make the certificate near-vacuous, while a hockey-stick-divergence form can remain informative.On coarsened timestamps, 1000 pairs do not yet certify a useful Eη.Source: C. Repair, sharpness, and rare… · p. 6
  • The coverage failure begins once (1−ε)κA > un; the repair is optimal but requires a design-time δ that padding does not supply on this stack.The paper states no deployed configuration in its study satisfies the design-time hypothesis.Source: C. Repair, sharpness, and rare… · p. 6

V. ENFORCING PNI

The paper enforces pre-commit noninterference (PNI) by preventing the agent’s pre-commit view from carrying information about the audit coin. It gives constructions and identifies costs and limits of practical repairs.

V. ENFORCING PNI

  • A silent branch, a fresh per-call coin, or a coin-independent decoy can enforce PNI; serial gates preserve it only when downstream steps do not read the upstream coin.A shared resource across concurrent episodes can still open a channel that no per-episode construction closes.Source: §V · p. 7
  • For M concurrent episodes sharing a resource, leakage equals the mode probability of Bin(M−1, ε), which decays only as M−1/2.The result assumes each audit adds fixed work to a shared resource observable before commit.Source: §V · p. 7
  • A fresh per-call coin closes the timing channel, but changes the estimand from violating episodes to gated calls chosen in count by the agent.The measured-mean-call-count readout cancels dilution; the capped readout adds the probability of exceeding the call cap.Source: §V · p. 7
  • The measured-mean-call-count readout targets pepi·E[v | violating], making it conservative by the mean number of violating calls per violating episode.A goal-count proxy in E8 puts that factor at ≥1.7; this is a looseness bound, not a run certificate.Source: §V · p. 7
  • Padding yields DA ≤ e0 + e1 only under an erasure model, and a black-box test can falsify a claimed δ but cannot give a distribution-free finite-sample upper bound for high-dimensional transcripts.Paired designs, finite-alphabet fields, and erasure channels are the stated cases for upper bounds; padding requires the model assumption, while coarse-clock release yields an erasure channel by construction.Source: §V · pp. 7–8
  • The deployment recipe prefers DA=0 by construction, then confines any residual channel to a design-time δ and counts exceedances as violations.A fixed ceiling is insufficient if the agent can inflate branch latency by reading more; the design needs a code-level work bound with capped ingestion.Source: §V · p. 8

VI. LEAKAGE ASSESSMENT ON REAL AGENT STACKS

The leakage assessment compares the agent’s observable views under the two audit-coin values without an LLM in the loop. Its fixed-path measurements bound only the channel on that path, not the full channel an adaptive agent can elicit.

VI. LEAKAGE ASSESSMENT ON REAL AGENT STACKS

  • Because the defender knows each run’s coin, the assessment directly compares what the agent observes under B=1 and B=0 without an LLM in the loop.For a fixed code path, DA caps the gain of any agent restricted to that path.Source: §VI · p. 8
  • An adaptive agent can steer onto a costlier path, so fixed-path leakage numbers are lower bounds on the deployable channel, not a monitor constant.Source: §VI · p. 8
  • The assessment does not mount an attack; it measures how far an audit branch falls short of the blindfold condition.Source: §VI · p. 8

A. Design and estimators

The experiments test whether audits change the agent’s view, using paired executions and leakage bounds. On PQ-CIFC, content remains unchanged while timing leaks strongly; padding narrows but does not eliminate the channel, and a second stack provides preliminary evidence in the same direction.

A. Design and estimators

  • The PQ-CIFC design held enforcement fixed and varied only the audit coin in paired runs from the same environment state, using six permitted and denied effect paths.It used AgentDojo with a calibrated frozen policy; the audit branch recomputed dependency closure read-only and appended a separate log record.Source: A. Design and estimators · p. 8
  • Leakage upper bounds used paired mismatch for content and an erasure bound for padded timing, while a held-out classifier supplied lower bounds.The padded upper bound also required a non-exceeding-run model condition and reachability; the study notes that a two-sample test cannot establish that model condition.Source: A. Design and estimators · p. 8

B. Findings

  • No paired PQ-CIFC run differed in decision or recorded response prefix, but the audit branch added 6.8 ms median latency and a fine clock separated arms with DA ≥0.77.Table IV summarizes these measurements; content leakage was bounded by DA ≤0.028 across all six paths simultaneously.Source: B. Findings · p. 8, Table IV · p. 9
  • Tool timestamps at microsecond resolution let a held-out classifier separate audited and unaudited arms with DA ≥0.99 from a single observation.On Slack, Gmail, and Google Drive, the stated timestamp resolutions are sufficient to resolve an approximately 10 ms gap.Source: B. Findings · p. 8
  • On the denied external-mail path, the median timing gap rose from 10 ms at m=0 to 58 ms at m=32 as the agent ingested more objects.At m=48, padding to a fixed ceiling still separated the arms with DA ≥0.91; at m ≥96 both arms exceeded the ceiling, exposing raw latency again.Source: B. Findings · pp. 8–9
  • Padding’s erasure bound was DA ≤0.044 over six paths with no exceedance in 1000 runs, but residual timing remained detectable and amplification prevented a design-time δ.The residual padded-run test rejected the erasure model; size-aware padding closed the grid only empirically because branch work can grow as Θ(m^2).Source: B. Findings · p. 9
  • On CaMeL, a held-out classifier gave a DA lower bound of 0.19, reduced to 0.17 [0, 0.56] with task-level splits and resampling.The evidence is preliminary: checks from one task are correlated, 15 of 24 runs crashed, and there was no padded arm.Source: B. Findings · p. 9

VII. EXPLOITATION AND CERTIFIER FAILURE

  • Table III classifies claims as live measurements, scripted attacks, resampled real labels, or analytic results; its confirmatory family covers notice and tool channels on two models.The generic and spontaneous conditions were exploratory.Source: §VII · p. 9, Table III · p. 9

A. Can the leakage be exploited?

The experiments show that an agent can exploit visible audit signals, causing the plain certificate to understate violation rates; a silent audit restores coverage. In a live run, the plain bound also fell below the realized rate, though the analysis was post hoc and the run used stateless episodes.

A. Can the leakage be exploited?

  • A real gpt-4.1-mini agent followed an injection naming the audit artifact; generic, spontaneous, and real-agent timing conditions were not established as significant effects.The visible notice cut attack success by 13.2 points (Holm p=0.012); the injection clause itself also lowered the Z=0 baseline, so condition comparisons are orderings, not calibrated contrasts.Source: A. Can the leakage be exploited? · pp. 9–10
  • A scripted likelihood-ratio adversary broke the certificate through the timing channel at every probe count, while a fresh per-call coin kept it valid through 105 probes without padding.Padding restored validity only under the erasure model; a real agent's timing-channel effect moved in the same direction but was not significant.Source: A. Can the leakage be exploited? · p. 10

B. What does exploitation do to the certificate?

  • On fully measured labels, opening the channel reduced plain-certificate coverage of µ0 to 0.007, while a silent audit covered at 0.993.Raising ε did not restore validity and brought failure forward; the E5 results also report that the plain certificate fails when the channel opens, whereas the silent audit covers in all four cells.Source: B. What does exploitation do to the… · p. 10
  • At ε=0.2, opening the channel across rates ρ made coverage of π cross below 0.95 between ρ=0.2 and 0.3.The oracle correction stayed above truth, while Theorem 5 with δ=ρ was vacuous in this setting.Source: B. What does exploitation do to the… · p. 10

C. Does it happen in a live run?

  • In the live run, the plain bound at 4,000 episodes was 0.190 against a realized rate of 0.205, while the silent arm covered at every n.The comparison was post hoc; the plain bound first dipped below the realized rate at n=2,206 and stayed below from n=2,687.Source: C. Does it happen in a live run? · p. 11
  • The live-run episodes were stateless draws from the 560-pair population, so cross-episode adaptation was not tested.The pre-registered comparison had a target mismatch; under that rule the crossing was at 2,818, and the frozen primary horizon returned a second-tier verdict.Source: C. Does it happen in a live run? · p. 11

VIII. DISCUSSION AND LIMITATIONS

The discussion distinguishes coin leakage from other risks in certification and describes trade-offs among ways to control leakage. The authors connect their framework to prior work and conclude that keeping the audit coin out of the agent’s pre-commit view is necessary for audit-based certification.

VIII. DISCUSSION AND LIMITATIONS

  • A violation concealed even from the auditor has no label and therefore requires a prevention argument beyond audit-based certification.The stated bias bound applies regardless of sign, but the discussion analyzes the dangerous case where the agent attacks less when it infers it is audited.Source: §VIII · p. 12
  • Adversary-controlled quantities must enter the certificate in one direction only; fail-closed counting or caps address selective-label and call-count inflation, leaving detectable availability exposure.The residual availability exposure is described as never anticonservative.Source: §VIII · p. 12
  • Shared aggregate load can leak an episode’s own audit coin: its exact leakage is the mode probability of Bin(M−1, ε), decaying only as M^−1/2.Closing the channel requires isolation, B-independent cover load, or post-commit placement.Source: §VIII · p. 12
  • A per-call coin closes timing leakage at a cost: on E5’s population, it multiplies the certified value by E[v | violating], at least 1.7 using the goal-count proxy.The recipe prefers constructions with DA=0, pays the readout factor when a pre-commit branch must run, and pays residual δ last.Source: §VIII · p. 12
  • The evidence is bounded by a monitor the authors built, in-process timestamps, one live run on one model and channel, and stateless draws from 560 pairs; cross-episode adaptation remains untested.The authors also note that a complete run on a public monitor is needed before calling the measured channel typical.Source: §VIII · p. 12

IX. RELATED WORK

  • Prior work studies unlearnable randomized oversight and evaluation leakage, while this paper combines them with sampled-audit certification to give an exact bias price and commit-time release point.The paper also distinguishes its setting from active anytime-valid risk control, where the behavior being sampled cannot be changed by the query.Source: §IX · p. 12

X. CONCLUSION

  • The paper concludes that audit-based certification requires keeping the coin out of the agent’s view before commit; its violation costs (1−ε)DA.The conclusion reports that a silent kernel or per-call coin restores validity.Source: §X · p. 13

APPENDIX A POSITIONING AND PROVENANCE

The appendix positions the work by comparing prior approaches along specified axes and mapping claims to experiments. It documents the measured stack and supplies proofs for the paper’s formal results.

APPENDIX A POSITIONING AND PROVENANCE

  • The comparison with closest work uses five axes: secret, whether behavior is fixed when the draw is made, adversary, estimand, and whether a sharp leakage price is given.A separate table maps classical tools applied in the paper against statements claimed as new.Source: Appendix A · p. 15

APPENDIX B EXPERIMENT INDEX AND REPRODUCTION

  • The experiment index maps each claim to its scale, model-API use, and experiment scripts; paired potential outcomes let E5/E6 coverage be recomputed offline from real labels.The reported total model-API cost was about US$91, including US$59 for E8.Source: Appendix B · p. 15

APPENDIX C THE MEASURED STACK (PQ-CIFC) AND THE AUDIT

  • PQ-CIFC instruments the audit branch, which recomputes the closure and logs it before the effect; the measured timing channel comes from this repeated audit work, not the monitor’s internals.The three audit-branch parts were not timed separately, so some of the latency gap may be logging rather than recomputation.Source: Appendix C · p. 15

APPENDIX D DEFERRED PROOFS

  • The deferred proofs derive the episode-coin leakage price (1−ε)TV(P0,P1) from total variation and bound adaptive multi-step leakage through a coupling argument.The proofs also distinguish view leakage from marginal coin miscalibration for adaptive rules.Source: Appendix D · pp. 15, 17

APPENDIX E WIDTH OF THE CONFIDENCE SEQUENCE

The appendix examines how the confidence-sequence width behaves under drift and across sample sizes. It distinguishes the plug-in sequence’s target from the realized-rate target and explains when the range term matters.

APPENDIX E WIDTH OF THE CONFIDENCE SEQUENCE

  • Under drift, the plug-in sequence covered in 0 of 300 runs, while the fixed-λ sequence covered in all 300 and had width 0.039 versus 0.042 on i.i.d. data.At N=4000 and p̄=0.206, the i.i.d. comparison is between the fixed-λ and plug-in sequences.Source: Appendix E · p. 18
  • At the reported horizons, the implemented width is within 1% of the variance term; the range term matters when εp̄n ≲20.The reported E8 width at 4000 is 0.0385, matching the variance term at 0.0385.Source: Appendix E · p. 18
  • Below about five expected audited violations, the two-term width approximation can underestimate by up to ≈2×; small samples are range-dominated and large samples variance-dominated.The resulting large-sample width rate is 1/√(nε).Source: Appendix E · p. 18
  • The plug-in sequence targets the realized rate, whereas UCS_n targets 1/n ∑_i p_i, which no run reveals.The realized-rate analysis sets λ using the pre-registered planning rate and horizon, without using outcomes.Source: Appendix E · p. 18
  • The horizon-free stitched sequence adds a log log n factor to both terms, while the deployed horizon-tuned form does not.At matched budget, the audit design’s variance ratio against εN fully labeled episodes is (1−εp)/(1−p).Source: Appendix E · p. 18

APPENDIX F COMPOSITIONAL BOUND AND NUMERICAL CHECKS

These appendices test compositional leakage bounds and examine notice, timing, and other channels in measured and live evaluations. They also report how certification outcomes vary with audit design, label budget, and the confidence sequence used.

APPENDIX F COMPOSITIONAL BOUND AND NUMERICAL CHECKS

  • At K=1, the BSC likelihood-ratio detector matched the middle composition bound (15.8% vs. 16.0%); at K=31 it was 59.4% vs. 79.9%, while the erasure channel lay on the bound.The Gaussian-observer calculation compares Pinsker’s Tκ/2 bound with the per-step sum TD₁; Table X reports both.Source: Appendix F · pp. 18–19, Table X · p. 19

APPENDIX G ADDITIONAL EXPLOITATION DETAILS

  • With a readable latency field, the scripted timing attack invalidated the certificate at every probe count on a fine clock; padding to a fixed ceiling restored validity when no run exceeded it.In the closed-loop run, the agent conditioned only weakly on latency, and padding narrowed the attempt-rate gap to 0.010.Source: Appendix G · p. 19
  • At full leakage and N=4000, bias was 0.071, 0.060, and 0.038 at ε=0.05, 0.2, and 0.5, against widths 0.080, 0.036, and 0.021; the empirical-Bernstein sequence failed in 0.95 of seeds at ε=0.2, ρ=1.The corresponding Hoeffding sequence failed in no seed, which the passage attributes to its looseness.Source: Appendix G · p. 19
  • Exploratory E3 findings varied by model and condition; glm-4.6 had 0 vulnerable pairs among 415 completed screens, but 145 rate-limit errors mean this is not a clean zero.The notice-channel effect across four models is reported in Table XI; the 50-pair pilot on gpt-4o-mini found no notice window.Source: Appendix G · p. 19, Table XI · p. 20
  • On measured notice-channel labels, plain-audit invalidity of p̄ was 0.95 at N=4000 and 1.00 at 2×10^4; bootstrap bias was 0.060 [0.040, 0.081].The bootstrap resamples the 560 pairs; the bias was positive in every replicate and exceeded the width in 98.9%.Source: Appendix G · p. 20
  • In the E8 live run, the pre-registered failure criterion failed at N=3000, while the extended analysis found the bound below the realized rate and the silent arm covered throughout.The extension to N=4000 was decided after the interim look; the paper identifies the persistence and at-N criteria as not anytime-valid.Source: Appendix G · p. 20
  • At matched window n=800, the audit bound was 2.8–3.1× wider than the all-labeled baseline, versus 2.5–2.7× from standard deviation alone.The audit used about 160 labels; the excess width is attributed to the empirical-Bernstein penalty at that label count.Source: Appendix G · p. 20
  • Showing an audit-like prompt independently of the audit coin restored plain-certifier coverage to 1.00 at ρ_d ∈ {0.25, 0.5, 1}, versus 0.07 when shown only on audited episodes.The realized attack rate also fell under the decoy protocol.Source: Appendix G · p. 20

APPENDIX H THE SIMULTANEOUS EXCEEDANCE BOUND

The bound must control the worst reachable ingest size, not a pooled mixture; padded bounds are conditional and do not automatically provide a design-time δ.

APPENDIX H THE SIMULTANEOUS EXCEEDANCE BOUND

  • A claimed δ is sound only if it bounds leakage over all reachable behaviors; a fixed ceiling fails when the agent can increase latency by ingesting more.The ceiling must scale with a code-level work bound and cap the agent-controlled ingest size.Source: Appendix H · p. 21
  • When the base rate is small relative to (1−ε)δ, additive certificate inflation is near-vacuous, favoring removal of leakage or a multiplicative residual bound.At δ=0.028, inflation (1−ε)δ<0.023 is usable at E5 base rates 0.22–0.38 but near-vacuous below about 0.02.Source: Appendix H · p. 21
  • Pooling across grid points bounds a mixture rate, not the supremum, and can miss exceedances concentrated at one point.Pooling the 5×80 grid would report 0.018 despite the adversarial concentration case.Source: Appendix H · p. 21
  • At n=80 and α1=0.025, the all-clean simultaneous bound is θ0=0.090; Bonferroni over 10 cells gives the more conservative 0.144.The α-split pays for the two arms, while multiplicity over m is free.Source: Appendix H · p. 21
  • At n=500, the pre-registered rerun gives 0.032 for counterfactual raw-latency exceedances and 0.028 for the confined padded implementation.Across six E1 effect paths, θ0≤0.044 replaces the path-pooled 0.0074.Source: Appendix H · p. 21
  • Padded bounds are not design-time δ when the non-exceedance model is rejected or the ceiling can be overrun.The timestamp observer rejects the model at C2ST 0.072 [0.002, 0.140]; size-aware reachability is only empirically closed.Source: Appendix H · pp. 20–22

APPENDIX I A MINIMAL SEMANTICS AND THREE CONSTRUCTIONS

The appendix formalizes PNI and shows that shared state can leak information across episodes. It then examines per-call certification, where measuring realized call counts avoids dilution but changes the estimand and can be conservative.

APPENDIX I A MINIMAL SEMANTICS AND THREE CONSTRUCTIONS

  • PNI requires the audit coin to be fresh given the agent’s pre-commit view; R-free observation kernels preserve this condition, while a downstream kernel reading an upstream coin can break it.The appendix gives minimal semantics for histories, agent views, and commit time.Source: Appendix I · p. 22
  • A shared resource whose state depends on multiple episode seeds can violate joint PNI even when each episode satisfies it in isolation.For the modeled binomial shared resource at ε=0.2, leakage values are 0.80, 0.41, 0.30, and 0.22 for M=2, 5, 10, and 20.Source: Appendix I · p. 22

APPENDIX J THE PER-CALL ESTIMAND UNDER DILUTION

  • With k=100 benign calls appended per violating episode, the static per-call conversion reads 0.058 against a true episode rate of 0.30.The per-call rate genuinely falls through dilution, so the attack changes the readout rather than biasing that estimator.Source: Appendix J · p. 22
  • Using the realized mean call count makes the episode readout dilution-invariant because padding raises call count as the per-call mean falls.The readout is tight when violations concentrate in one call and loose when they spread; its only way to fall is fewer violating calls.Source: Appendix J · pp. 22–23
  • The measured-mean-call-count readout is conservative by the mean number of violating calls per violating episode, measured as 1.69 on E8’s silent arm.The plain arm’s estimate is 1.67; these are lower bounds on looseness because agents may make more calls than goals require.Source: Appendix J · p. 23
  • In simulation, the measured-mean-call-count readout stays about 0.35 as k grows, while the capped readout covers at k=100 by flagging 0.30 of episodes.The demonstration uses 4,000 episodes, 200 seeds, ε=0.2, and a true episode violation rate of 0.30.Source: Appendix J · p. 23

APPENDIX K ADDITIONAL LEAKAGE-ASSESSMENT DETAILS

Additional leakage assessments show that averaging probes can expose timing differences, while load, byte size, and deployment conditions affect padding behavior. The measurements also include scope limitations for the CaMeL checks.

APPENDIX K ADDITIONAL LEAKAGE-ASSESSMENT DETAILS

  • At k=104, the E2 certificate fails in 77% of seeds and fails in every seed at k=105; padding keeps its bound at 0.72 above a true 0.60.Without padding, the true rate rises to 0.43 while the certified bound is 0.20.Source: Appendix K · p. 23
  • At fixed object count, 64 KB objects cause up to 60/0 one-sided exceedances; a byte-size ceiling leaves one exceedance in 2×1000 runs and a fallback bound of 0.15.The size-aware ceiling is 48.6+5.35m+0.144S ms.Source: Appendix K · p. 23
  • Under saturation on a two-core host, the audited arm alone exceeds the fixed ceiling once per 500 runs, while size-aware exceedances rise to 85–99 per 100.Runtime exceedance counting and fail-closed handling preserve certificate soundness under the tested load conditions.Source: Appendix K · p. 23
  • A held-out classifier distinguishes audited from unaudited inter-timestamp gaps with DA≥0.99 from one observation in both timestamp-clock runs.The audited medians were 11.0 ms versus 0.95 ms, and 7.1 ms versus 0.22 ms.Source: Appendix K · p. 23
  • CaMeL checks cover 128 benign paired checks from 12 tasks, but task-level intervals include zero and no attacked runs were paired.Fifteen of 24 benign runs ended in interpreter exceptions, and there was no padded arm.Source: Appendix K · p. 23
  • Capacity isolation halves the aggregate-load timing shift but leaves classifier leakage; the shared-versus-isolated contrast is indicative because both setups are oversubscribed at M≥10.The measured C2ST values remain 0.33, 0.33, 0.17, and 0.13 under isolation.Source: Appendix K · p. 23
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