The Bureaucracy of Speed: Structural Equivalence Between Memory Consistency Models and Multi-Agent Authorization Revocation

Vladyslav Parakhin

cs.MAMar 10, 2026

The Bureaucracy of Speed: Structural Equivalence Between Memory Consistency Models and Multi-Agent Authorization Revocation

Authors: Vladyslav Parakhin

Organizations: Senior Data Engineer, Okta

Abstract

The temporal assumptions underpinning conventional Identity and Access Management collapse under agentic execution regimes. A sixty-second revocation window permits on the order of $6 \times 10^3$ unauthorized API calls at 100 ops/tick; at AWS Lambda scale, the figure approaches $6 \times 10^5$ . This is a coherence problem, not merely a latency problem. We define a Capability Coherence System (CCS) and construct a state-mapping $\varphi : Σ_{\rm MESI} \to Σ_{\rm auth}$ preserving transition structure under bounded-staleness semantics. A safety theorem bounds unauthorized operations for the execution-count Release Consistency-directed Coherence (RCC) strategy at $D_{\rm rcc} \leq n$ , independent of agent velocity $v$ -- a qualitative departure from the $O(v \cdot \mathrm{TTL})$ scaling of time-bounded strategies. Tick-based discrete event simulation across three business-contextualised scenarios (four strategies, ten deterministic seeds each) confirms: RCC achieves a $120\times$ reduction versus TTL-based lease in the high-velocity scenario (50 vs. 6,000 unauthorized operations), and $184\times$ under anomaly-triggered revocation. Zero bound violations across all 120 runs confirm the per-capability safety guarantee. Simulation code: https://github.com/hipvlady/prizm

Figures & tables

From	Event	To	Semantics
$I$	grant_shared	$S$	Capability issued (role-based)
$I$	grant_exclusive	$E$	JIT credential issued
$E$	delegate	$M$	Agent sub-delegates
$E$	revoke	$EIA$	Revocation in-flight, ACK pending
$EIA$	ack	$I$	Revocation confirmed
$S$	revoke	$SIA$	Revocation in-flight (shared)

Table 1: Valid transitions of the authorisation state machine.

Figure 1: State mapping $\varphi:\Sigma_{\textsc{MESI}{}}\to\Sigma_{\mathrm{auth}}$ . Left: hardware MESI state machine with transition triggers ( BusRd , BusRdX , Snoop-Inv ). Right: authorisation state machine with corresponding triggers ( grant , delegate , revoke ). Transient states as dashed nodes ( $EIA$ , $MIC$ , $ISG$ ). Event correspondences labelled on connecting arrows.

Agent Context	Coherence Strategy	Damage Bound	Hardware Analogy
Financial ops	Eager (consistency-agnostic)	$\leq v\cdot\Delta_{\mathrm{net}}$	CPU SWMR
CRM / Bulk sync	Lease-based (temporal)	$\leq v\cdot\mathrm{TTL}$	GPU Temporal
Analytics	Lazy (check-on-use)	$\leq v\cdot\Delta_{\mathrm{check}}$	Weak ordering
High-velocity API	Exec-count ( RCC )	$\leq n$ ops	GPU Release Consistency

Table 2: Heterogeneous coherence strategy assignment by agent context.

Figure 2: Heterogeneous Coherence Architecture. Authority Service (central) connected to four agent clusters, each labelled with coherence strategy. Message types: synchronous (eager, solid), periodic (lease, dashed), on-demand (lazy, dotted), acquire/release (exec-count, double). Trust Scorer feeds risk assessments into the strategy selector.

Parameter	Scenario 1 (Banking)	Scenario 2 (CRM)	Scenario 3 (Anomaly)
Agents	10	1	5
Delegation depth	3	1	1
Action model	Prob. ( $p=0.5$ )	Det. (100/tick)	Prob. ( $p=0.7$ )
Seeds	0–9 (10 runs)	0–9 (10 runs)	0–9 (10 runs)
Network latency	10 ticks	5 ticks	10 ticks
Revocation trigger	Tick 100	Tick 0	Auto (trust score)

Table 3: Simulation parameters across three business-contextualised scenarios.

Metric	Eager	Lease (120-tick)	Lazy (40-tick)	RCC ( $n=60$ )
Unauthorised ops	$14.9\pm 3.2$	$29.9\pm 2.6$	$35.3\pm 2.9$	$32.3\pm 13.0$
Staleness max (ticks)	$10.0\pm 0$	$20.0\pm 0$	$23.0\pm 0$	$37.0\pm 21.6$
Messages sent	$3.0\pm 0$	$3.0\pm 0$	$3.0\pm 0$	$3.0\pm 0$
Bound violations	0	0	0	0

Table 4: Banking cascade results (mean

\pm

\sigma

, 10 runs, seeds 0–9).

Metric	Eager	Lease (60 ticks)	Lazy (23-tick)	RCC ( $n=50$ )
Unauthorised ops	$500.0\pm 0$	$\mathbf{6{,}000.0\pm 0}$	$2{,}400.0\pm 0$	$\mathbf{50.0\pm 0}$
Staleness max (ticks)	$5.0\pm 0$	$60.0\pm 0$	$24.0\pm 0$	$0.0\pm 0$
Revalidation count	$0.0\pm 0$	$0.0\pm 0$	$0.0\pm 0$	$1.0\pm 0$
Bound violations	0	0	0	0

Table 5: CRM high-velocity results (mean

\pm

\sigma

, 10 runs, seeds 0–9).

Strategy	Predicted $D_{\pi}$	Observed	Match
Eager: $v\cdot\Delta_{\mathrm{net}}=100\times 5$	500	$500.0\pm 0$	Exact
Lazy: $v\cdot(\Delta_{\mathrm{net}}+\Delta_{\mathrm{check}})=100\times(1+23)$	2,400	$2{,}400.0\pm 0$	Exact
Lease: $v\cdot\mathrm{TTL}=100\times 60$	6,000	$6{,}000.0\pm 0$	Exact
RCC : $n$	50	$50.0\pm 0$	Exact ( theorem 3.1 )

Table 6: Predicted vs. observed damage bounds (CRM scenario).

Figure 3: Unauthorised operations by strategy (Scenario 2, log scale). CRM high-velocity scenario (100 ops/tick, 10 runs, $\sigma=0$ ). The $120\times$ gap between Lease and RCC is annotated. Y-axis logarithmic; damage bound formulae shown below each bar.

Metric	Eager	Lazy	Lease (3000-tick)	RCC ( $n=100$ )
Unauthorised ops	$108.0\pm 0$	$10.8\pm 3.6$	$\mathbf{2{,}950.8\pm 3.6}$	$\mathbf{16.0\pm 3.7}$
Staleness max (ticks)	$10.0\pm 0$	$1.9\pm 0.3$	$245.9\pm 0.3$	$1.8\pm 0.4$
Cascade completeness	1.0	1.0	0.0	1.0

Table 7: Anomaly auto-revocation results (mean

\pm

\sigma

, 10 runs, seeds 0–9).

Strategy	Damage Bound	Deterministic?	Vel.-Independent?	Coupling
Eager	$v\cdot\Delta_{\mathrm{net}}$	Yes	No	Synchronous
Lease	$v\cdot\mathrm{TTL}$	Yes	No	None
Lazy	$v\cdot(\Delta_{\mathrm{net}}+\Delta_{\mathrm{check}})$	Yes	No	On-demand
RCC	$n$	Yes	Yes	Acquire/release

Table 8: Strategy comparison summary.

Access Control

Access control specifies and enforces who or what may access data and perform actions, under which conditions.

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Heartbeat-Bound Hierarchical Credentials: Cryptographic Revocation for AI Agent Swarms

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SentinelOne Inc. Mountain View, USA

AI Agent Security LLM Agent Security Multi-Agent System Security

Open paper AskPDF arXiv

Autonomous AI agents that spawn sub-agent swarms create a safety gap: existing credential revocation mechanisms, OAuth2.0 introspection, OCSP, and W3C Status Lists, require network connectivity to a central authority, leaving ``zombie agents'' executing privileged operations for minutes to hours after operator shutdown. We present Heartbeat-Bound Hierarchical Credentials (HBHC), a cryptographic protocol that binds credential validity to periodic parent liveness proofs. Verifiers enforce freshness using only a cached public key and local clock; no network round-trip is required. When heartbeat generation ceases, all descendant credentials become unusable within a deterministically bounded window $W_z \le W_{\max} + Δ_h + ε$ , conditional on bounded clock skew and parent keys held in secure enclaves. Evaluation at the protocol layer and with real LLM-backed agent swarms (GPT-4o-mini) demonstrates a 90 $\times$ reduction in the zombie window over OAuth2.0, 0.26~ms full authentication in Rust, 18,000+ verifications per second under concurrent HTTP load, and stable per-verification latency from 10 to 10,000 agents. Real-agent experiments show 0.71% end-to-end overhead on tool calls, zero post-revocation tool calls under prompt injection that bypasses application-layer guardrails, and cascading revocation across a 49-agent four-level hierarchy within the theoretical bound.

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