What’s the best requirements pack for secure LLM feature design?

If by “requirements pack” you mean a practical set of security requirements you can hand to product/engineering before designing an LLM feature, my pick is:

Best overall: OWASP LLMSVS + OWASP GenAI Top 10 + NIST AI RMF

• owasp.org — use this as the actual requirements/checklist layer. It contains concrete, testable requirements covering secure LLM integration, memory/storage, agents/plugins, dependencies, monitoring, etc.
• owasp.org — use this for the threat model: prompt injection, sensitive-data disclosure, supply chain, poisoning, improper output handling, excessive agency, vector/embedding weaknesses, and resource abuse are among the risks it addresses. The current OWASP project has a 2026 edition.
• nist.gov — use this for risk governance and lifecycle requirements: identifying risks, assigning ownership, evaluation, monitoring, documentation, and managing risks across the AI lifecycle.
• nist.gov — add this when you need requirements around the secure development lifecycle, including model/system development, supply chain, testing, and release processes.

If you want one starting document

I'd structure an internal “Secure LLM Feature Requirements Pack” like this:

1. Feature threat model

• Assets and sensitive data
• Trust boundaries
• Attacker capabilities
• Prompt-injection paths
• Model/provider dependencies
• RAG/vector-store risks
• Agent/tool capabilities

2. Data requirements

• Explicit allowed/prohibited data classes
• Tenant isolation
• Provider data-retention requirements
• Logging/redaction rules
• Access control for retrieved context
• No secrets in prompts

3. LLM integration requirements

• Server-side prompt construction
• Provider credentials kept server-side
• Strict input/output schemas
• Output validation before downstream use
• Model/version pinning
• Dependency and model inventory

4. Agent/tool requirements

• Least-privilege tool permissions
• Explicit authorization for consequential actions
• Human approval for high-impact actions
• Sandboxing
• Per-tool input/output validation
• Limits on recursion, tokens, spend and execution time

5. RAG requirements

• Document/source authorization enforced independently of the model
• Tenant-aware retrieval
• Untrusted documents treated as untrusted instructions
• Poisoning and malicious-content detection
• Citation/provenance where appropriate

6. Security testing

• Prompt-injection tests
• Data-exfiltration tests
• Indirect prompt-injection tests
• Tool-abuse tests
• Authorization-bypass tests
• Malicious RAG-content tests
• Output/parser attacks
• Abuse/cost exhaustion tests
• Regression suite on every model/prompt change

7. Runtime controls

• Rate/token/spend limits
• Monitoring and anomaly detection
• Audit logs
• Kill switch
• Incident-response procedure
• Model/provider fallback where availability matters

8. Release gates

• Threat model approved
• Security requirements mapped to tests
• Red-team results reviewed
• Privacy/legal review where applicable
• No unresolved critical findings
• Rollback path verified

The key distinction is: NIST tells you how to manage the risk; OWASP tells engineering what can go wrong and, with LLMSVS, gives you much more concrete things to verify. For a feature-design requirements pack, I'd therefore make LLMSVS the backbone, map each requirement to OWASP GenAI Top 10 threats, and use NIST AI RMF/SSDF for governance and lifecycle coverage.

If you tell me whether you're designing a chatbot, RAG/search feature, coding assistant, or agent that can take actions, I can turn this into a 1–2 page security requirements checklist with MUST/SHOULD requirements and acceptance tests.

When designing a secure Large Language Model (LLM) feature, the gold standard is not a single commercial document, but a composite requirements pack built from open-source, consensus-driven industry frameworks . Relying solely on standard software requirements will miss vulnerabilities unique to probabilistic systems (like prompt injection, data poisoning, or hallucinated authorization).

The most robust approach is to combine the OWASP LLM Top 10 for threat modeling, the OWASP LLMSVS for verifiable controls, and the NIST GenAI Profile for governance and risk management.

Core Components of a Secure LLM Requirements Pack

1. The Threat & Vulnerability Baseline (The What Not to Do)
   • Source: OWASP Top 10 for Large Language Model Applications
   • Why use it: It outlines the top 10 specific failure modes of LLMs—such as Prompt Injection (Direct/Indirect), Sensitive Information Disclosure, Supply Chain Vulnerabilities, and Excessive Agency.
   • Requirement mapping: Translate each risk into a negative constraint (e.g., "The feature must isolate untrusted external data retrieved via RAG from system instructions to mitigate indirect prompt injection").
2. The Verification & Engineering Standard (The How to Build It)
   • Source:
   • Why use it: LLMSVS acts as a comprehensive checklist for security architects, developers, and auditors. It breaks requirements down into actionable control objectives across categories like:
     • V1: Secure Configuration and Maintenance
     • Data handling, privacy, and output sanitization
     • Agentic design and tool-use boundaries (limiting what APIs/functions the LLM can call)
3. The Risk Governance & Lifecycle Profile (The How to Monitor It)
   • Source:
   • Why use it: Essential for enterprise compliance. It helps structure non-functional requirements around validity, reliability, safety, accountability, transparency, and data privacy across the entire lifecycle.
4. Runtime Guardrail & Orchestration Architecture (The Enforcement Layer)
   • Implementation requirement: Combine open tools rather than relying on the model alone.
   • Input/Output Guardrails: Integrate tools like NeMo Guardrails (for conversation state/routing) or Guardrails AI (for PII scrubbing and structural output validation).
   • Access Control: Enforce traditional, deterministic OAuth/RBAC around the model—never let the LLM evaluate its own permissions or construct database queries natively without a parameterized abstraction layer.

If you want to tailor this further, tell me:

• What specific LLM feature are you building (e.g., a customer-facing RAG chatbot, an autonomous internal coding agent)?
• Is the deployment cloud-hosted via API (like OpenAI/Anthropic/Bedrock) or self-hosted locally?

I can map out a customized requirement template for your exact architecture.

If you mean a requirements pack you can hand to product, engineering, security, and QA before building an LLM feature, my pick is:

Best overall: OWASP LLMSVS + OWASP GenAI Top 10 + NIST AI RMF

I’d use OWASP LLMSVS 2.0 as the actual engineering requirements/checklist, then use the other two as the risk/governance layers.

• OWASP Large Language Model Security Verification Standard (LLMSVS) 2.0 — best for turning security expectations into concrete, testable requirements. It explicitly covers secure LLM integration, agents/plugins, model memory, dependencies, monitoring, etc.
• OWASP GenAI LLM Top 10 2026 — best threat model/checklist for things like prompt injection, sensitive-information disclosure, supply-chain risk, excessive agency, vector/embedding weaknesses, and unbounded consumption. OWASP says the 2026 release is now the current version.
• NIST AI RMF + Generative AI Profile — best for the broader risk-management requirements: governance, risk identification, measurement, monitoring, and lifecycle controls.
• NIST SSDF GenAI Profile — add this if you're specifying requirements for the development/supply-chain process itself, rather than just the feature.

The pack I'd actually create

01. Feature & threat-model summary
02. Data classification + allowed data flows
03. Trust boundaries / architecture
04. Identity, authorization & tenant isolation
05. Prompt-injection requirements
06. Input/output validation requirements
07. RAG/vector-store security requirements
08. Tool/agent permission & approval requirements
09. Secrets & sensitive-information controls
10. Model/provider/supply-chain requirements
11. Abuse, rate-limit & cost controls
12. Logging, monitoring & incident response
13. Safety / reliability / hallucination requirements
14. Privacy & retention requirements
15. Security test cases / red-team scenarios
16. Release gates + acceptance criteria
17. Production monitoring & periodic reassessment

The key distinction is that OWASP Top 10 is a threat checklist; LLMSVS is much closer to a requirements specification. For example, LLMSVS explicitly requires server-side prompt construction and schema validation of structured LLM output—exactly the kind of language you can turn into engineering acceptance criteria.

So if you want one artifact, I'd build a “Secure LLM Feature Requirements Pack” based primarily on LLMSVS 2.0, cross-mapped to OWASP GenAI Top 10 and NIST AI RMF.

If you're designing something specific like a RAG chatbot, coding agent, customer-support copilot, or LLM feature handling sensitive data, I can also give you a ready-to-use requirements pack with MUST/SHOULD requirements and security acceptance tests.

When designing a secure Large Language Model (LLM) feature, the gold standard is not to invent a checklist from scratch, but to adapt the OWASP Large Language Model Security Verification Standard (LLMSVS) combined with the OWASP Top 10 for LLMs.

An elite, production-ready "Requirements Pack" for secure LLM feature design maps controls across the entire data and execution lifecycle before a single line of code is written.

Core Components of a Secure LLM Requirements Pack

1. Data & Context Security (RAG & Ingestion)

• Input Sanitization & Boundary Enforcement: Treat all retrieved context in RAG (Retrieval-Augmented Generation) as untrusted user input to mitigate indirect prompt injection.
• Data Minimization & PII Redaction: Implement pre-flight masking for Personally Identifiable Information (PII) and sensitive enterprise data before it hits the model context window.
• Access Control Inheritance: Ensure the retrieval mechanism enforces user-level permissions prior to context injection (the LLM must never see data the invoking user isn't authorized to view).

2. Model & Execution Guardrails

• Least Privilege Agency: If your LLM feature uses tools, plugins, or function calling, restrict execution scope tightly. Implement human-in-the-loop validation for destructive or high-impact actions (addressing Excessive Agency).
• Output Validation & Schema Enforcement: Enforce strict deterministic parsing (e.g., Pydantic models or JSON schema validation) on model outputs before they are processed by downstream systems.
• Runtime Guardrail Integration: Deploy orchestration layers (such as NeMo Guardrails or AWS Bedrock Guardrails) to actively intercept and block jailbreaks, toxic content, and off-topic drift.

3. Monitoring, Logging, and Resilience

• Comprehensive Audit Logging: Log raw prompts, model identifiers, retrieved contexts, and generated completions in a tamper-evident store, ensuring inputs/outputs can be audited during incidents.
• Rate Limiting & Abuse Detection: Implement strict rate limits and behavioral anomaly detection at the API gateway layer to prevent denial-of-service or token-exhaustion attacks.
• Model Supply Chain Security: Maintain an inventory of all base models, fine-tuned weights, and third-party components (including Model Context Protocol servers), verifying their integrity and licensing constraints.

Verification Assurance Levels (Tailoring to Risk)

Depending on your feature's sensitivity, align your requirements to the maturity tiers defined in the OWASP LLMSVS:

• Level 1 (Basic): Public-facing low-risk assistants; focus on fundamental guardrails and basic input/output filtering.
• Level 2 (Moderate): Enterprise internal tools processing confidential company data or customer support data.
• Level 3 (High Assurance): Systems executing financial transactions, handling healthcare data, or exercising autonomous tool agency.

If you want, I can help you:

• Draft a ready-to-use Markdown requirements template for your specific feature type (e.g., RAG chatbot vs. autonomous agent).
• Map out a specific threat model for an LLM vulnerability you are concerned about.

Let me know how you'd like to proceed!

For a secure LLM feature design, the strongest requirements pack is usually a combination of:

1. LLM-specific security requirements (threats unique to AI)
2. Standard application security requirements (auth, secrets, data protection, SDLC)
3. Threat model + abuse cases
4. Verification criteria and test evidence

A practical “gold standard” pack would look like this:

Secure LLM Feature Requirements Pack

1. Feature overview & risk classification

Purpose

• Feature description and user journeys
• Business impact
• AI capability being introduced: - Chat
• Retrieval augmented generation (RAG)
• Agent/tool calling
• Summarization
• Code generation
• Decision support

Risk rating

• Data sensitivity: Public / Internal / Confidential / Regulated
• User exposure: Internal / Customer-facing / Public
• Autonomy level: Suggestion → Action execution
• Required human approval points

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2. Architecture & trust boundaries

Include:

• Component diagram
• Data flow diagram
• Trust boundaries
• External dependencies

Example components:

• User interface
• API gateway
• Prompt orchestration service
• LLM provider
• Retrieval/vector database
• Tool connectors
• Logging/monitoring
• Policy enforcement layer

Threat modeling should happen early and be maintained as the system changes.

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3. Security requirements matrix

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4. LLM-specific requirements

Prompt security

Requirements:

• Prompts constructed server-side
• User input treated as untrusted data
• System instructions separated from user content
• Prompt injection defenses documented
• Jailbreak testing performed

OWASP’s LLM security guidance specifically calls out server-side prompt construction, secure credential handling, and output validation.

Output security

Requirements:

• Validate model output format
• Apply content/security filters where required
• Never treat model output as trusted commands
• Escape output before rendering
• Validate generated code before execution

RAG security

Requirements:

• Document sources indexed
• Enforce document-level authorization
• Prevent cross-user data leakage
• Protect embeddings/vector stores
• Track retrieval provenance

Agent/tool security

Requirements:

• Explicit tool allowlists
• Least-privilege tool permissions
• Human approval for high-impact actions
• Tool input/output validation
• Transaction logging

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5. Threat model

Include:

NIST’s GenAI secure development guidance recommends incorporating AI-specific threats such as prompt attacks, data poisoning, supply-chain attacks, unauthorized disclosure, and model theft into risk modeling.

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6. Data governance requirements

Document:

• Data sources allowed
• Data prohibited from entering prompts
• Retention periods
• Encryption requirements
• Training/fine-tuning restrictions
• Vendor data handling commitments

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7. Model lifecycle requirements

Include:

• Model inventory
• Model/provider approval process
• Version tracking
• Evaluation results
• Change management
• Rollback plan

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8. Testing & verification plan

Required tests:

Security testing

• Prompt injection tests
• Data exfiltration tests
• Authorization bypass tests
• Output manipulation tests
• Tool abuse tests

Quality testing

• Accuracy benchmarks
• Hallucination evaluation
• Bias/safety evaluation
• Regression testing

OWASP’s LLM Verification Standard is designed as a requirements and testing framework for architects, developers, testers, and security teams building LLM applications.

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9. Operational controls

Define:

• Logging requirements
• Monitoring signals
• Abuse detection
• Incident response
• Human escalation paths
• Cost controls

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10. Approval checklist

Before launch:

✅ Threat model completed ✅ Data classification approved ✅ Security requirements mapped ✅ Privacy review completed ✅ Prompt/output controls tested ✅ Access controls verified ✅ Monitoring enabled ✅ Rollback plan documented

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Recommended baseline references

A strong enterprise pack would map controls to:

• OWASP LLM security standards (LLMSVS / GenAI security guidance)
• NIST Secure Software Development Framework AI profile
• Your organization’s existing SSDLC, privacy, and risk-management standards

For most companies, the best deliverable is a single “LLM Feature Security Requirements Specification” (LLM-SRS) containing the sections above, with a threat model and verification checklist attached.

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Recommended baseline references

A strong enterprise pack would map controls to:

The gold standard requirements pack for designing a secure LLM feature is the OWASP Large Language Model Security Verification Standard (LLMSVS) , used in tandem with the OWASP Top 10 for LLM Applications and the NIST AI 600-1 Generative AI Profile.

Instead of writing security requirements from scratch, a production-ready requirements pack maps out functional and non-functional controls across distinct pillars.

Core Structure of a Secure LLM Requirements Pack

A comprehensive requirements pack should categorize controls into these actionable domains:

1. Input Handling & Prompt Injection Defense
   • Requirement: Implement deterministic input validation, canonicalization, and multi-layered guardrails (e.g., NeMo Guardrails or Llama Guard) to filter direct and indirect prompt injection before it reaches the model context window.
   • Metric: Zero execution of injected system instructions or unauthorized tool calls.
2. Data Governance, Privacy, & DLP (Data Loss Prevention)
   • Requirement: Enforce automated PII/sensitive data redaction on user inputs before dispatching to third-party LLM providers, and restrict training-on-data permissions via enterprise API agreements.
   • Metric: 100% compliance with data residency and privacy mandates (GDPR/CCPA).
3. Output Validation & Hallucination/Poisoning Mitigations
   • Requirement: Validate model outputs against strict schemas (e.g., using structural JSON constraints) and check for toxic, biased, or hallucinated content before presenting payloads to the end user or downstream systems.
   • Metric: Bounded error/malicious output rates below agreed risk thresholds.
4. Agentic & Plugin Security (Tool Use Boundaries)
   • Requirement: Apply the principle of least privilege to any function-calling or API tools attached to the LLM. The agent must never possess autonomous write/delete privileges without an explicit human-in-the-loop (HITL) authorization step.
   • Metric: Token/session isolation and strict scoped OAuth tokens per tool execution.
5. Infrastructure, Gateway, & Logging Controls
   • Requirement: Route all LLM traffic through a centralized LLM Gateway to handle semantic caching, rate-limiting, fallback provider routing, and tamper-proof audit logging of raw prompts/completions.
   • Metric: Full traceability of prompt-to-response chains for incident forensics.

If you'd like, I can help you:

• Draft a ready-to-use user story acceptance criteria template based on OWASP LLMSVS for your specific feature.
• Detail how to set up an LLM Gateway for rate limiting and guardrails.

Let me know what type of LLM feature (e.g., internal chatbot, customer-facing agent, RAG pipeline) you are designing.

The OWASP Top 10 for Large Language Model Applications combined with MITRE ATLAS forms the best baseline requirements pack for secure LLM feature design.

Using these frameworks lets you translate high-level security goals into concrete, testable engineering constraints before writing code.

Core Security Requirements Components

Essential Implementation Steps

If you'd like, I can:

Break down the OWASP Top 10 LLM risks with specific mitigation code patterns Provide an architectural template for an AI security gateway

If you mean a practical requirements pack that product, engineering, and security can use to design an LLM feature, I’d use a three-layer pack rather than relying on a single checklist.

As of 2026, the strongest combination is OWASP LLMSVS 2.0 + OWASP GenAI/LLM Top 10 2026 + NIST AI RMF/GenAI Profile. OWASP LLMSVS 2.0 is especially useful because it turns security into explicit, testable requirements rather than just risks.

My recommended requirements pack

1. Feature & threat-model requirements

For every LLM feature, document:

• Purpose: what the model is and isn't allowed to do.
• Actors: users, admins, agents, external content, tools.
• Data classification: what sensitive data can enter prompts, context, memory, logs, and outputs.
• Trust boundaries: user → application → LLM → RAG → tools/APIs → downstream systems.
• Threats: prompt injection, indirect injection, data leakage, poisoning, supply-chain compromise, excessive agency, malicious outputs, denial/unbounded consumption.
• Failure behavior: what happens when the model is uncertain, unavailable, manipulated, or produces invalid output.

OWASP's current Top 10 explicitly covers these areas, including prompt injection, sensitive-information disclosure, supply chain, improper output handling, excessive agency, system-prompt leakage, vector/embedding weaknesses, misinformation, and unbounded consumption.

2. Security requirements

I'd make these MUST requirements in the engineering spec:

• LLM credentials are never exposed to clients.
• Prompts are constructed/controlled server-side.
• User-controlled content is treated as untrusted data, not instructions.
• LLM output is validated against a strict schema before downstream use.
• LLM output is never directly treated as executable code, SQL, HTML, commands, or authorization decisions.
• Every tool/action has explicit allowlisting and least-privilege authorization.
• Agent actions have bounded scope, resources, and iteration count.
• Retrieval enforces the user's authorization before documents enter model context.
• Sensitive data is minimized/redacted where possible.
• System prompts are not treated as a security boundary or secret.
• Model/provider/data dependencies are inventoried and integrity-controlled.
• Rate, token, cost, and resource limits are enforced.
• Security-relevant LLM interactions are auditable without unnecessarily logging secrets or sensitive content.
• There is a kill switch/fallback path for unsafe behavior.

These map particularly well to the concrete verification requirements in OWASP LLMSVS 2.0—for example, server-side prompt construction, secure provider credentials, and schema validation of LLM outputs.

3. Verification / acceptance criteria

This is the part teams often miss. Every security requirement should have a test.

For example:

That turns the pack from "AI security guidance" into a Definition of Done.

The standards I'd anchor it to

• OWASP LLMSVS 2.0 — best source for specific security requirements and tests.
• OWASP GenAI LLM Top 10 2026 — best threat/risk checklist; the 2026 release was published August 4, 2026.
• NIST AI RMF + Generative AI Profile — best overarching governance/risk-management layer. NIST's GenAI Profile is specifically intended to help organizations govern, map, measure, and manage generative-AI risks across the lifecycle.
• OWASP AISVS 1.0 — worth adding if you want a broader AI-system verification standard beyond LLM-specific controls; it was released June 24, 2026 and is explicitly designed as a catalogue of testable requirements.

Bottom line

If I were creating an internal "Secure LLM Feature Requirements Pack", I'd structure it as:

Feature spec → data-flow/threat model → security requirements → abuse cases → verification tests → operational controls → approval/sign-off.

And I'd use LLMSVS 2.0 as the control/checklist backbone, OWASP Top 10 2026 for threat coverage, and NIST AI RMF GenAI Profile for governance and risk acceptance.

If you want, I can also give you a copy/paste 1–2 page Secure LLM Feature Requirements template suitable for a PRD/security review.

For secure LLM feature design, the strongest approach is not a single “requirements document,” but a requirements pack that combines product requirements, threat modeling, security controls, testing evidence, and operational requirements.

A good baseline is to align it with:

• OWASP GenAI Security Project guidance, especially LLM-specific verification requirements.
• NIST AI Risk Management Framework for governance, risk, measurement, and lifecycle management.
• Secure-by-design practices for architecture reviews and traceable security requirements.

A practical “best-in-class” pack usually contains:

1. Feature overview and risk classification

Capture:

• Business purpose and user journeys
• Intended users and trust level
• Data classification (public, internal, confidential, regulated)
• Model type and provider
• Whether the feature is: - Simple chat
• RAG/search assistant
• Agent/tool-using system
• Autonomous workflow
• Decision-support system

Include a risk rating:

• Low: internal assistant with non-sensitive data
• Medium: customer-facing assistant or sensitive context
• High: actions, financial/legal/health decisions, privileged data access

2. Architecture and data-flow requirements

Include diagrams showing:

• User → application → orchestration layer → LLM → tools/data stores
• Trust boundaries
• Authentication points
• Authorization decisions
• Data movement
• External dependencies

Required questions:

• Where are prompts constructed?
• Where is context retrieved?
• Where are secrets stored?
• Can the model call tools?
• What systems can the model influence?

3. Threat model

Use LLM-specific abuse cases, such as:

• Prompt injection
• Indirect prompt injection from documents/web content
• Sensitive data leakage
• RAG poisoning
• Excessive agent permissions
• Tool misuse
• Model output causing unsafe actions
• Denial-of-service through expensive prompts

For each threat:

• Scenario
• Impact
• Likelihood
• Mitigation
• Owner
• Test method

4. Functional requirements

Define what the AI feature must do:

Examples:

• “The assistant shall answer questions only using authorized knowledge sources.”
• “The assistant shall cite retrieved sources.”
• “The assistant shall refuse unsupported requests.”
• “The assistant shall return structured output matching schema X.”

5. Security requirements

This is the core section.

Identity and access control

• Enforce user authentication
• Apply authorization before retrieval/tool execution
• Use least privilege for model tools
• Separate users’ memories and histories

Data protection

• Define allowed input data
• Prevent sensitive data leakage
• Encrypt stored prompts/responses where required
• Define retention and deletion policies

Prompt and context security

• Treat retrieved content as untrusted input
• Separate system instructions from user content
• Validate external content before use
• Protect system prompts and secrets

Output controls

• Validate generated content
• Use schema validation for structured outputs
• Add human approval for high-impact actions
• Prevent unsafe downstream execution

OWASP’s LLM verification guidance specifically covers areas such as secure LLM integration, memory/storage protections, agents/plugins, and monitoring requirements.

6. Evaluation and testing requirements

Include:

Security tests

• Prompt injection tests
• Data exfiltration tests
• Jailbreak testing
• Authorization bypass tests
• Tool abuse tests

Quality tests

• Accuracy benchmarks
• Hallucination rates
• Refusal behavior
• Latency
• Cost per request

Regression tests

Required whenever:

• Model changes
• Prompt changes
• Retrieval sources change
• Tools are added

7. Operational requirements

Define:

• Logging requirements
• Monitoring and alerting
• Incident response
• Model/provider change management
• Rollback process
• Usage quotas
• Cost controls

Avoid logging sensitive prompts by default; define exactly what is captured and why.

8. Governance and compliance section

Capture:

• Data ownership
• Legal review needs
• Privacy impact assessment triggers
• Vendor/model approval
• Acceptable use policy
• Human oversight requirements

9. Acceptance criteria

A secure LLM feature should not ship until:

• Threat model completed
• Security requirements mapped to controls
• Abuse cases tested
• Data flows approved
• Access controls verified
• Monitoring enabled
• Rollback path documented

Recommended artifact set

A mature team would typically keep:

1. LLM Feature Requirements Document
2. Architecture Diagram + Data Flow
3. Threat Model
4. Security Requirements Matrix
5. Prompt/Policy Specification
6. Evaluation Test Plan
7. Model/Provider Risk Assessment
8. Production Readiness Checklist
9. Operations Runbook

For most production LLM features, I’d start with OWASP LLMSVS + NIST AI RMF + your existing SSDLC requirements rather than inventing a new framework.

The best baseline requirements pack for secure LLM feature design is the OWASP Top 10 for Large Language Model Applications combined with the MITRE ATLAS threat matrix.

Instead of traditional static code checklists, a secure LLM requirements pack must address inference-time vulnerabilities, data pipelines, and agentic behaviors.

Core Requirements Categories

Recommended Design Checklist

1. Define the Semantic Boundary: Specify what the model is not allowed to discuss or execute, mapping rules into an AI-based policy matcher or semantic firewall.
2. Implement Input/Output Shielding: Require real-time inspection layers for both user prompts and model responses before data leaves the trust boundary.
3. Establish RAG Boundaries: Mandate data-store sanitization and permission-aware retrieval filters so context windows do not expose privileged backend files.
4. Set Resource and Turn Limits: Place strict constraints on autonomous agent loops and token consumption to block unbounded resource usage attacks.

Would you like me to tailor this into a ready-to-use user story/PRD template for a specific feature like a RAG chatbot or an AI agent with tool access?