Taxonomy of Risks Posed by Language Models: A Comprehensive Risk Analysis Framework

Large language models now underpin customer service chatbots, medical triage systems, legal research tools, and financial advisory platforms across every major industry.

Yet an estimated 73 percent of organisations deploying AI in production still lack a formal risk framework tailored to generative models.

Definition: A taxonomy of risks posed by language models is a structured classification system that categorises the potential harms, vulnerabilities, and failure modes of large language models (LLMs) into logical groupings.

It enables organisations to systematically identify, assess, and manage risks across technical, human-use, and societal dimensions.

Two landmark efforts now anchor the field. The NIST AI 600-1 Generative AI Profile maps 13 distinct risk categories and prescribes more than 400 mitigation actions.

The MIT AI Risk Repository catalogues 1,612 classified risks drawn from over 700 academic papers.

Summary

• A taxonomy of risks posed by language models organises threats into three pillars: technical/model risks, human misuse risks, and ecosystem/societal risks.
• The NIST AI 600-1 Generative AI Profile identifies 13 risk categories with 400+ prescribed mitigation actions for generative AI systems.
• Key technical risks include confabulation (hallucination), harmful bias, data privacy violations, and homogenisation of outputs.
• Human misuse risks span disinformation campaigns, CBRN information generation, social engineering, and deepfake creation.
• Building an organisational risk taxonomy requires five steps: identify, classify, assess, mitigate, and monitor.
• Integration with enterprise risk management (ERM) frameworks ensures language model risks are governed alongside traditional operational, strategic, and financial risks.

What Is a Risk Taxonomy for Language Models?

A risk taxonomy for language models is a hierarchical classification system that organises the full spectrum of threats, failure modes, and harms associated with large language models into structured categories. It functions as the foundational reference document for risk identification, assessment, and mitigation across the AI lifecycle.

A well-designed taxonomy serves three core functions:

1. Systematic identification: Ensures no significant risk category is overlooked during assessment
2. Consistent communication: Provides a shared vocabulary across technical, legal, compliance, and executive teams
3. Prioritised mitigation: Enables risk-based allocation of resources to the highest-severity threats

The concept extends established risk management principles from ISO 31000 into the AI domain, adapted for the unique characteristics of generative models.

The Three Pillars of Language Model Risk

Drawing from the NIST AI Risk Management Framework and MIT AI Risk Repository, language model risks organise into three pillars:

Pillar 1: Technical and Model Risks

Technical risks originate from the model itself, its training data, architecture, and inference behaviour. These include:

Risk	Description
Confabulation (Hallucination)	The model generates plausible but factually incorrect information, presenting fabricated data as truth
Harmful Bias and Homogenisation	Training data imbalances produce outputs that perpetuate stereotypes or underrepresent minority perspectives
Data Privacy Violations	Models may memorise and reproduce personal data from training corpora, violating GDPR, CCPA, and other regulations
Value Chain Integration Risks	Third-party model components introduce opaque dependencies and supply-chain vulnerabilities

Pillar 2: Human Misuse Risks

These risks arise when human actors deliberately or negligently misuse language model capabilities:

• CBRN information generation: Models may provide detailed instructions for creating chemical, biological, radiological, or nuclear weapons
• Disinformation and deepfakes: Generating convincing false narratives, synthetic media, or impersonation content at scale
• Social engineering: Crafting personalised phishing emails, scam scripts, or manipulation campaigns
• Obscene and degrading content: Producing harmful, explicit, or abusive material

Pillar 3: Ecosystem and Societal Risks

Broader systemic effects that emerge from widespread language model deployment include:

• Environmental impact: Training large models consumes significant energy; GPT-4-scale training estimated at 50 GWh
• Intellectual property: Models trained on copyrighted material raise unresolved legal questions
• Labour displacement: Automation of knowledge work tasks affecting writing, coding, analysis, and customer service roles
• Power concentration: A small number of organisations control the most capable models, creating dependency risks

NIST AI 600-1: The 13 Risk Categories

The NIST AI 600-1 Generative AI Profile defines 13 distinct risk categories for generative AI systems. Each category maps to specific mitigation actions:

Category	Description	Key Mitigation
CBRN Information	Generation of weapons-related content	Output filtering, use-case restrictions
Confabulation	Factually incorrect but plausible outputs	Retrieval augmentation, confidence scoring
Data Privacy	Memorisation of training data PII	Differential privacy, data scrubbing
Environmental	Energy and resource consumption	Efficient architectures, carbon tracking
Harmful Bias	Discriminatory or stereotyping outputs	Bias audits, diverse training data
Homogenisation	Reduced diversity of perspectives	Model diversity, plurality metrics
Human-AI Config.	Over-reliance or misattribution	Transparency, human-in-the-loop
Information Integrity	Misinformation at scale	Provenance tracking, watermarking
Information Security	Prompt injection, model theft	Input validation, access controls
IP / Copyright	Training on copyrighted material	Licensing, opt-out mechanisms
Obscene Content	Generating harmful material	Content moderation, RLHF
Value Chain	Third-party component risks	Vendor assessment, model cards
Dangerous Recs.	Harmful advice or instructions	Safety classifiers, red-teaming

Building a Language Model Risk Taxonomy: A 5-Step Process

1. Step 1: Identify — Catalogue all potential risks through threat modelling, red-teaming, incident analysis, and stakeholder workshops. Use NIST AI 600-1 and the MIT AI Risk Repository as starting references.
2. Step 2: Classify — Organise identified risks into the three-pillar hierarchy (technical, human misuse, societal). Assign subcategories and map causal mechanisms.
3. Step 3: Assess — Evaluate each risk for likelihood, impact severity, velocity, and detectability. Use a risk matrix aligned with your organisation’s risk appetite.
4. Step 4: Mitigate — Develop controls for each risk category: technical controls (guardrails, RLHF), governance controls (policies, oversight), and operational controls (monitoring, incident response).
5. Step 5: Monitor — Establish key risk indicators (KRIs), continuous monitoring dashboards, and regular reassessment cycles. Update the taxonomy as the threat landscape evolves.

Document all findings in a risk register and align with your enterprise risk management framework.

Industry-Specific Applications

Industry	Key LLM Risks	Mitigation Focus
Financial Services	Algorithmic bias in lending, credit scoring, AML false positives	Fair lending audits, model validation (SR 11-7)
Healthcare	Diagnostic bias, hallucinated treatment recommendations, patient privacy	Clinical validation, HIPAA compliance, human oversight
Legal	Hallucinated case citations, confidentiality breaches	Source verification, attorney review requirements
Media / Content	Misinformation at scale, deepfakes, copyright violation	Content provenance, watermarking, editorial review
Education	Plagiarism enablement, assessment undermining, bias in feedback	Academic integrity policies, pedagogical AI guidelines
Government	Surveillance concerns, democratic manipulation, accessibility bias	Transparency mandates, algorithmic impact assessments

Integrating the Risk Taxonomy into Enterprise Risk Management

A language model risk taxonomy should not exist in isolation. It must integrate with the organisation’s broader enterprise risk management framework to ensure AI risks are governed alongside operational, strategic, and financial risks.

Key integration points:

• Map taxonomy categories to existing risk appetite statements and tolerance thresholds
• Assign AI-specific key risk indicators (KRIs) alongside traditional operational KRIs
• Include language model risks in the enterprise risk register with consistent scoring
• Report AI risk exposure through established governance channels (risk committees, board reports)
• Align with ISO 31000 risk management principles and the NIST AI RMF Govern function

Risk Mitigation Strategies

Technical Controls

• Reinforcement Learning from Human Feedback (RLHF) to align model behaviour with human values
• Red-teaming and adversarial testing to identify vulnerabilities before deployment
• Guardrails and output filters to block harmful, biased, or policy-violating content
• Retrieval-augmented generation (RAG) to ground outputs in verified source material
• Differential privacy techniques to prevent training data memorisation

Governance Controls

• AI ethics committees with cross-functional representation
• Model risk management policies aligned with SR 11-7 and NIST AI RMF
• Mandatory impact assessments before deploying LLMs in high-risk use cases
• Transparency requirements including model cards and system documentation

Operational Controls

• Continuous monitoring dashboards tracking bias metrics, hallucination rates, and security incidents
• Incident response procedures specific to AI failures and adversarial attacks
• Regular model revalidation cycles aligned with the PDCA improvement cycle
• User training programmes on responsible AI use and limitation awareness

Challenges and Limitations

Challenge	Description	Mitigation
Rapid evolution	New model capabilities create risks faster than taxonomies update	Continuous monitoring and quarterly taxonomy reviews
Measurement difficulty	Many risks (bias, fairness) lack agreed quantitative metrics	Combine quantitative and qualitative assessment methods
Interdependency	Risks interact and cascade in unpredictable ways	Systems thinking and scenario analysis
Resource intensity	Comprehensive taxonomy development requires significant expertise	Start with NIST AI 600-1 as baseline, customise incrementally
Regulatory flux	EU AI Act, NIST RMF updates, and sector-specific rules continue evolving	Build adaptable frameworks with regular compliance reviews
Opacity	Model internals are not fully interpretable	Explainability techniques, external audits

Frequently Asked Questions

What is the taxonomy of risk models?

A taxonomy of risk models is a structured classification system that organises the potential harms and failure modes of AI models into logical categories. For language models specifically, it typically groups risks into three pillars: technical/model risks (hallucination, bias, data privacy), human misuse risks (disinformation, CBRN, social engineering), and ecosystem/societal risks (environmental impact, IP issues, labour displacement). The NIST AI 600-1 framework identifies 13 distinct categories with over 400 prescribed mitigation actions.

What is the taxonomy of AI risks?

The taxonomy of AI risks encompasses the full spectrum of threats across the AI lifecycle. The MIT AI Risk Repository, the most comprehensive database available, catalogues 1,612 classified risks across two taxonomies: a causal taxonomy (what causes the risk) and a domain taxonomy (what domain the risk affects). Key domains include discrimination, misinformation, security breaches, environmental harm, and societal disruption.

What are the risks of language models?

Language model risks include: perpetuating biases and stereotypes from training data; generating hallucinated (fabricated) information; breaching data privacy through memorised personal information; enabling disinformation campaigns at scale; providing dangerous instructions (CBRN content); facilitating social engineering and fraud; producing offensive or harmful content; and concentrating power among a small number of AI providers.

What is the risk factor taxonomy?

A risk factor taxonomy identifies and classifies the root causes that give rise to language model risks.

These factors include biased or unrepresentative training data, lack of diverse development teams, insufficient safety testing, inadequate guardrails, poor human-AI configuration, and absence of governance frameworks. Understanding risk factors enables organisations to address risks at their source rather than only managing symptoms.

How does the NIST AI RMF address LLM risks?

The NIST AI Risk Management Framework addresses LLM risks through four core functions: Govern (establishing policies and oversight), Map (identifying and categorising risks), Measure (assessing risk severity and likelihood), and Manage (implementing controls and monitoring).

The companion document NIST AI 600-1 provides a generative AI-specific profile with 13 risk categories and detailed mitigation guidance.

What is the difference between AI risk assessment and AI risk taxonomy?

An AI risk taxonomy is a classification system that organises all possible risks into categories and subcategories. An AI risk assessment is the process of evaluating specific risks within that taxonomy for a particular system, use case, or deployment context. The taxonomy provides the framework; the assessment applies it. Think of the taxonomy as the map and the assessment as the journey through it.

Related Resources on Risk Publishing

• AI Risk Management Framework
• What Is Model Risk Management
• Enterprise Risk Management Guide
• Operational Risk Management Framework
• Risk Management Process
• ERM and Cyber Security
• Enterprise Risk Management KRIs
• Risk Register Template and Guide
• What Are Strategic Risks?
• Quantitative Risk Management Tools

External References

• NIST AI Risk Management Framework
• NIST AI 600-1 Generative AI Profile
• MIT AI Risk Repository
• ISO 31000:2018 Risk Management
• OWASP Top 10 for LLM Applications
• EU AI Act Regulatory Framework