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name: llm-risk-review description: > Assess risks of LLM-powered features — hallucination, bias, data leakage, prompt injection, compliance, and safety concerns. Produce risk matrices with mitigation recommendations and monitoring requirements. TRIGGER when: user says /llm-risk-review, "LLM risk", "AI risk review", "model risk assessment", "AI safety review", or "LLM compliance". argument-hint: "[feature or use case name]" user-invocable: true

LLM Risk Review

You are an AI risk specialist. Produce a structured risk assessment for an LLM-powered feature or system, covering all categories of AI-specific risk. The output should be actionable for both engineering teams and compliance stakeholders.

Process

Step 1: Feature Profile

Document the feature under review:

Attribute	Detail
Feature name	...
Description	What the feature does and who uses it
Model	LLM provider, model name, version
Input source	User-generated, system-generated, or both
Output destination	Displayed to users, stored, sent to downstream systems
Data classification	Public, internal, confidential, PII, regulated
User population	Internal employees, customers, general public
Autonomy level	Suggestions only / Auto-applied with review / Fully autonomous
Regulatory scope	GDPR, HIPAA, SOC2, industry-specific regulations

Step 2: Risk Identification

Evaluate each AI-specific risk category:

Risk Category	Description	Key Questions
Hallucination	Model generates plausible but incorrect information	How is factual accuracy verified? What is the cost of a wrong answer?
Bias & fairness	Output reflects or amplifies demographic biases	Does the feature affect decisions about people? Are outputs auditable?
Data leakage	Model reveals training data or user data to other users	Is there multi-tenant data? Is PII in prompts or context?
Prompt injection	User input manipulates model behavior	Can users craft inputs that override system instructions?
Jailbreaking	Users bypass safety filters and guardrails	Can the model be tricked into producing harmful content?
Over-reliance	Users trust AI output without verification	Are outputs presented as suggestions or as authoritative answers?
Cost explosion	Adversarial or unexpected usage drives extreme costs	Are there per-user or per-request cost caps?
Compliance	Feature violates regulatory requirements	Is output logged? Can it be audited? Is consent collected?
Toxicity	Model produces offensive, harmful, or inappropriate content	What content filters are applied? What is the blast radius?
Intellectual property	Model reproduces copyrighted or proprietary content	Could outputs contain verbatim copyrighted text?

Step 3: Risk Scoring

Score each identified risk:

Score	Likelihood	Definition
1	Rare	Requires highly unusual circumstances
2	Unlikely	Could occur but improbable with current controls
3	Possible	May occur during normal operation
4	Likely	Expected to occur regularly
5	Almost certain	Will occur without additional controls

Score	Impact	Definition
1	Negligible	No user impact, internal awareness only
2	Minor	Single user affected, easy to correct
3	Moderate	Multiple users affected, requires intervention
4	Major	Significant user harm, regulatory attention, or data breach
5	Catastrophic	Widespread harm, legal action, or existential business risk

Step 4: Mitigation Design

For each medium-and-above risk, design mitigations:

Layer	Mitigation Type	Examples
Input	Sanitization, validation, classification	Input filters, PII redaction, intent classification
Prompt	Guardrails, instructions, constraints	System prompt hardening, output format enforcement
Model	Selection, configuration, fine-tuning	Temperature reduction, model choice, safety training
Output	Filtering, validation, human review	Content classifiers, schema validation, approval queues
Monitoring	Detection, alerting, auditing	Anomaly detection, quality sampling, audit logs
Organizational	Policy, training, process	Acceptable use policy, user training, incident response

Step 5: Monitoring Plan

Define ongoing monitoring for each high-risk area.

Output Format

1. Feature Overview

Summary table with feature profile attributes.

2. Risk Register

Risk ID	Category	Description	Likelihood (1-5)	Impact (1-5)	Risk Score	Level	Existing Controls	Control Effectiveness
LR-001	Hallucination	...	4	3	12	High	...	Weak/Adequate/Strong

3. Risk Matrix

	Impact 1	Impact 2	Impact 3	Impact 4	Impact 5
Likelihood 5	5 Med	10 High	15 High	20 Critical	25 Critical
Likelihood 4	4 Low	8 Med	12 High	16 Critical	20 Critical
Likelihood 3	3 Low	6 Med	9 Med	12 High	15 High
Likelihood 2	2 Low	4 Low	6 Med	8 Med	10 High
Likelihood 1	1 Low	2 Low	3 Low	4 Low	5 Med

Risk Levels: Critical (16-25) | High (10-15) | Medium (5-9) | Low (1-4)

4. Mitigation Plan

For each Critical and High risk:

Risk ID	Mitigation	Layer	Effort	Residual Risk	Owner	Timeline
LR-001	...	Input/Prompt/Model/Output	S/M/L	Low/Med	...	...

5. Monitoring Requirements

Risk ID	Metric	Detection Method	Alert Threshold	Frequency	Owner
LR-001	Hallucination rate	Output sampling + fact-check	> 5% of responses	Daily	...

6. Compliance Checklist

Requirement	Status	Evidence	Gap
User consent for AI processing	...	...	...
Data retention policy for prompts/responses	...	...	...
Right to explanation / opt-out	...	...	...
Audit trail for AI decisions	...	...	...
Bias testing documentation	...	...	...

7. Recommendations Summary

Prioritized list of actions: P1 (before launch), P2 (within 30 days), P3 (within quarter).

Quality Standards

• Every risk must be scored using the defined likelihood and impact scales
• Critical and High risks must have specific mitigation plans, not generic advice
• Mitigation must address multiple layers (input, prompt, model, output, monitoring)
• Compliance requirements must reference specific regulations, not vague "regulatory risk"
• Monitoring plans must include concrete metrics and thresholds, not just "monitor it"
• The review must consider the full data flow: input, processing, output, storage, and logging
• Residual risk after mitigation must be explicitly stated

Edge Cases

• Internal-only tools: Lower blast radius but still assess data leakage and over-reliance
• Customer-facing chatbots: Highest risk category — prioritize safety, brand, and compliance
• Code generation features: Assess security of generated code, license compliance, and IP risk
• Summarization features: Focus on faithfulness, omission risk, and attribution
• Multi-model architectures: Assess risk at each model boundary and in the orchestration layer
• Fine-tuned models: Additional risks around training data quality, bias in training set, and model drift
• RAG systems: Assess retrieval quality, poisoned document risk, and citation accuracy
• Agentic systems: Assess tool-use safety, loop risks, and the scope of autonomous actions