Registry-Aware Guardrails for LLM Safety

• Registry-aware guardrails are mechanisms that integrate dynamic, extensible registries into LLM systems for context-conditional enforcement of safety and policy boundaries.
• They leverage external registries—such as safety taxonomies, trust credentials, and policy contracts—to dynamically adapt moderation and verification workflows.
• Implementations like Roblox Guard 1.0 and policy-governed RAG demonstrate significant gains in F1 scores and error reduction, supporting rigorous compliance standards.

Registry-aware guardrails are mechanisms for enforcing dynamic, extensible, and context-conditional boundaries in LLM systems, leveraging explicit registries or taxonomies of categories, policies, credentials, or evidence. Instead of hardcoded, static moderators, these systems integrate flexible registries—such as safety taxonomies, trust credentials, or policy contracts—directly into the moderation, reasoning, and verification processes. Modern implementations, such as Roblox Guard 1.0, trust-oriented guardrails, and policy-governed retrieval-augmented generation (RAG) architectures, demonstrate registry-aware guardrails across generative safety, contextual user access, and audit-ready generation workflows (Nandwana et al., 5 Dec 2025, Hu et al., 2024, Ray, 22 Oct 2025).

1. Core Principles and Definitions

Registry-aware guardrails function by incorporating externalized, explicit registries into guardrail logic. Operationally, the "registry" may be:

• A taxonomy of harmful, sensitive, or policy-classified categories (as in Roblox Guard 1.0's safety taxonomy (Nandwana et al., 5 Dec 2025));
• A credential and authorization registry associating users with authority-verified trust (Hu et al.'s TTP credential bundles (Hu et al., 2024));
• A policy contract or evidence manifest registry, cryptographically versioned and enforced at each request (policy-governed RAG (Ray, 22 Oct 2025)).

Key properties include:

• Dynamic registry expansion: New categories, credentials, or policies can be appended without retraining or re-architecting the guardrail module.
• Registry-conditional execution: Moderation or verification operates as a contextual function of the registry entry (e.g., category definition, user trust score, policy JSON).
• Registry as a prompt/contract: Definitions, thresholds, or manifest hashes are passed downstream, not as static gates, but as part of the model or decision pipeline input.

Editor's term: "registry-coupled pipeline" denotes any guardrail sequence where registry state is a first-class input.

2. Architectures and Workflows

Roblox Guard 1.0: Taxonomy-aware Moderation

The moderation pipeline operates in three logical stages, all parameterized by registry entries:

1. Input guard: Receives prompt and category (registry entry); outputs binary violation label.
2. Target-LM call: If input passes, the prompt is forwarded.
3. Output guard: Assesses LM output for category-specific violation using the same registry entry.

There is no monolithic classifier; each category (registry "task") is supplied at inference as a free-form natural-language definition and short label. This enables adding or modifying registry entries in production without retraining—the guard model generalizes immediately to new definitions (Nandwana et al., 5 Dec 2025).

Trust-Oriented Adaptive Guardrails

The architecture consists of:

1. User interface/query analyzer
2. Trust modeling component (computing direct interaction trust and authority-verified trust with formulas and credential registries)
3. Retrieval-augmented guardrail engine using a knowledge base (KB) storing registry entries (user attributes, credentials) and content-policy rules
4. In-context learning (ICL)-driven LLM using guardrail tier logic dependent on trust score and policy registry (Hu et al., 2024).

Policy-Governed RAG

PG-RAG comprises:

• Contracts/Control: Enforces ex-ante policy gates registered in content-addressed, versioned policy registries. Gate outputs are strictly bound to registry policy versions.
• Manifests/Trails: Maintains document and evidence manifest registries using sparse Merkle trees anchored to persistent logs. Fragments and evidence are registry-indexed.
• Receipts/Verification: Packages signed portable receipts (COSE/JOSE) including references to all registry entries—policies, evidence, and provenance (Ray, 22 Oct 2025).

3. Registry Integration Mechanisms

Taxonomy Conditioning via Prompt Engineering

Roblox Guard 1.0 implements registry-awareness by always concatenating the registry entry (natural-language category definition and label) to the input. Instruction-tuning exposes the model to tens of thousands of such variations, forcing dynamic adaptation to arbitrary registry entries (Nandwana et al., 5 Dec 2025).

Credential and Authority Registry in Trust-Oriented Guardrails

The authority-verified trust component simulates an external registry:

• TTP (Trusted Third Party) maintains user credential bundles.
• Each sensitive request involves credential verification via signed attribute tokens; system validates and periodically re-challenges credentials, ensuring trust registry freshness.
• Trust computation formulas combine direct historic interaction data and attribute-verified registry evidence (Hu et al., 2024).

Policy, Evidence, and Compliance Contracts in RAG

• Policies are uniquely versioned and stored in content-addressed registries using cryptographic hashes (SHA-256).
• At runtime, every LLM output is constrained by contract gates whose parameters are parsed directly from the policy registry.
• All cited evidence is tied to manifest registries with intent-anchored Merkle proofs and inclusion logs (Ray, 22 Oct 2025).

4. Training, Fine-Tuning, and Evaluation for Registry Adaptivity

Instruction Fine-Tuning for Registry Generalization

Roblox Guard 1.0 is fine-tuned not with a fixed class set but across arbitrary registry entry-text/category pairs. Training utilizes:

• Open-source and synthetic safety datasets, the latter generated via adversarial sampling and rule-based prompt augmentation.
• Chain-of-thought (CoT) rationales prepended to labels, ensuring the model learns to reason about the registry conditionally rather than memorizing static classes.
• Input inversion permutations (randomizing output format) to prevent overfitting to registry field positions.
• FLAN-style multi-tasking, treating every category as a separate task, resulting in >384K unique tuning examples (Nandwana et al., 5 Dec 2025).

Benchmarking and Metrics

• RobloxGuard-Eval: JSON benchmarks encode each example with registry entry, prompt, response, and label, facilitating arbitrarily extensible evaluation.
• Metrics: F1-score computed per-category; ablations show removing registry-centric mechanisms collapses F1 from ~80% to <30% on out-of-domain tasks.
• In policy-governed RAG: Metrics are pre-registered as gating criteria using the receipt-backed pipeline, with enforced error-reduction, latency, cost, and dependence controls directly traceable to corresponding registry entries (e.g., policy version, manifest root) (Nandwana et al., 5 Dec 2025, Ray, 22 Oct 2025).

5. Content Moderation and Governance Policies

Semantic Modularity

Registry-aware systems store policies as modular, plug-and-play entries—categories in safety guardrails, trust thresholds in adaptive guardrails, or JSON policies in RAG settings. Each category or policy can encode nuanced thresholds, variable decision boundaries, and differential moderation strictness. In Trust-Oriented Guardrails, for example, moderation boundaries shift dynamically based on the combination of registry-verified credential level and the severity of the content-policy registry entry (Hu et al., 2024).

Enforcement and Gating

Policy enforcement is performed ex-ante (before output is generated or released). In PG-RAG, all fragments/answers must pass scope, grounding, independence, privacy, and promotion gates, each parameterized by the current registry policy. Failing any policy registry criterion aborts emission (NO-GO) and logs the cause for compliance and auditability (Ray, 22 Oct 2025).

6. Empirical Impact and Limitations

Registry-aware guardrails outperform static-taxonomy and credential-agnostic systems in both safety and precision:

• Roblox Guard 1.0 achieves 79.6% F1 on RobloxGuard-Eval versus <30% for closed-taxonomy baselines (Nandwana et al., 5 Dec 2025).
• Trust-oriented adaptive guardrails grant 97.5% relevant privileged access while minimizing leakage of unrelated sensitive answers to <1%, compared to >89% leakage in static black-box guardrails (Hu et al., 2024).
• Policy-governed RAG maintains auditable, receipt-backed error reductions ≥20% and deterministic NO-GO policy bound enforcement, supporting regulatory requirements (EU AI Act) and enabling pre-committed negative result publication (Ray, 22 Oct 2025).

Limitations include registry scalability (frequent credential verification in high-throughput deployments), trust-gaming attacks, privacy issues in registry data storage and trust-score computation, and the need to extend frameworks beyond binary safe/unsafe judgements to richer multidimensional value policies (Hu et al., 2024).

7. Regulatory Alignment and Auditing

Registry-aware guardrails facilitate transparent, replayable auditing:

• PG-RAG couples each answer to a compliance receipt, cryptographically signed and embedding all policy and evidence registry references, supporting offline verification by auditors and regulators.
• Contracts, manifests, and receipts are content-addressed, versioned, and time-stamped, supporting post-hoc forensics, drift detection, and robust revocation (key revocation notices) (Ray, 22 Oct 2025).
• Such architectures are directly aligned with regulatory demands for documented oversight, risk management, and monitoring in high-risk AI systems, as mandated under the EU AI Act and GDPR (Ray, 22 Oct 2025).

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Key References

(Nandwana et al., 5 Dec 2025) Taxonomy-Adaptive Moderation Model with Robust Guardrails for LLMs (Hu et al., 2024) Trust-Oriented Adaptive Guardrails for LLMs (Ray, 22 Oct 2025) Policy-Governed RAG - Research Design Study