Threat Modeling in AI and LLMs: Key Risks & Strategies

Fiza Nadeem
July 28, 2025
5
min read

Traditional threat models assume static behaviors and predictable logic. In contrast, AI and LLMs (large language models) are data-driven, interactive systems with dynamic and often unpredictable outputs.

They enable conversational interfaces and decision-making engines, but they also introduce novel attack vectors.

Generative AI and LLMs can be manipulated using methods like prompt injection, model inversion, and data leakage — bypassing many safeguards built for traditional software.

This is why AI-specific threat modeling is critical. It helps identify new risks early, design proactive controls, and secure components like training data, model weights, inference APIs, and real-time prompts.

Both CISOs and AI developers must be involved to safeguard generative AI systems effectively.

What Is Threat Modeling in the Context of AI and LLMs?

Threat modeling is a proactive method to identify and mitigate security risks before they are exploited. In the context of AI and LLMs, it adapts to systems that rely on probabilistic logic, massive datasets, and black-box behaviors.

Unlike traditional apps, generative AI and LLMs involve:

  • Dynamic inputs and outputs
  • Complex training pipelines
  • Fine-tuning processes
  • Third-party datasets and plugins

These introduce risks such as prompt hijacking and data extraction. AI threat modeling maps these components, identifies how they can be attacked, and creates defense strategies tailored to generative models.

Why Threat Modeling for AI/LLMs Is Critical?

With AI and LLMs embedded in critical business applications, security blind spots can lead to major breaches. Public-facing generative AI systems expand the attack surface dramatically.

Threats include:

  • Malicious prompt inputs
  • Training data leakage
  • Unauthorized model access

Compliance frameworks like the EU AI Act and NIST AI RMF also demand secure and transparent AI systems.

Threat modeling provides a structured way to meet these expectations and implement risk-based controls specific to LLMs and generative AI.

Core Components of AI/LLM Threat Modeling

Effective threat modeling for AI and LLMs starts by identifying all valuable assets:

  • Training datasets
  • Model weights and prompts
  • Embeddings and APIs
  • Downstream outputs

These assets often carry sensitive or proprietary data.

Adversaries can include:

  • External users probing for prompt leaks
  • Insiders with access to data
  • Advanced actors trying to reverse-engineer models

Attack vectors specific to LLMs and generative AI include:

  • Prompt injection
  • Model inversion
  • Jailbreaks
  • Data poisoning

You may want to read: Penetration Testing for LLMs.

Threat Modeling Methodologies for AI Systems

Several threat modeling frameworks are adaptable for AI and LLMs:

LLMs and Generative AI: Key Frameworks

  • STRIDE: Stride originally for traditional systems, it now covers AI-specific risks like training data tampering.
  • MITRE ATLAS: Mitre Atlas focuses on adversarial attacks in ML and generative AI systems.
  • OWASP Top 10 for LLMs: OWASP highlights common LLM vulnerabilities, such as prompt leakage and insecure plugins.
  • NIST AI RMF: NIST AI RMF offers compliance-aligned best practices for managing AI risk.

Each framework helps teams model threats and choose mitigation strategies effectively across the generative AI and LLM lifecycle.

Specific Threats to LLMs

Prompt Injection

Attackers can hijack AI responses using cleverly crafted prompts, overriding system logic or accessing sensitive data.

Model Inversion

This technique allows threat actors to reconstruct training data — posing significant risk when models are trained on private or regulated data.

Common Threats to LLM Systems
Specific Threats to LLM Systems

Adversarial Inputs

Malicious inputs can trigger biased, harmful, or manipulated responses. These can bypass safeguards and harm users or organizations.

Fine-tuning Abuse

Bad actors may fine-tune open models with misleading content and distribute them as reliable alternatives, weaponizing LLMs and generative AI.

Supply Chain Risks

Compromised plugins or datasets integrated into AI workflows can introduce backdoors or harmful behaviors into otherwise secure systems.

Building a Threat Model for an LLM-Powered System

The process includes:

Identify Critical Assets

Determine which model components need protection:

  • Training datasets
  • Model weights
  • Prompts and logs
  • API endpoints
  • Generated content

Define Trust Boundaries

Clarify where and how different users, systems, and services interact with the AI model. Identify which inputs are trusted and which are not.

Steps to build threat model for LLM-powered system.
How to Build a Threat Model for an LLM System

Threat Enumeration

Map possible threats using frameworks like STRIDE and the OWASP Top 10 for LLMs:

  • Is prompt injection possible?
  • Could a plugin misuse model outputs?
  • Could unauthorized users access sensitive prompts?

Assess the impact and likelihood of each risk and prioritize accordingly.

Define Mitigation Strategies

Implement:

  • Input validation
  • Rate limiting
  • Output filtering
  • Access controls
  • Behavior monitoring

All aligned with internal security policies and compliance guidelines.

Mitigation Strategies for AI/LLM Threats

To secure AI and LLMs, combine system-level and model-level controls:

Input Validation and Prompt Control

Use strict input formatting and sanitize user input to prevent prompt manipulation.

Privacy-preserving Techniques

Incorporate techniques like differential privacy and federated learning to minimize sensitive data exposure during training.

Output Filtering and Moderation

Prevent harmful outputs with filters and human-in-the-loop moderation for high-risk scenarios.

Secure Model Hosting

Isolate production models, restrict access, and monitor for abnormal activity in generative AI systems.

Real-time Monitoring

Track usage, outputs, and system behavior. Alert security teams when suspicious activity occurs.

Address Plugin and Integration Risks

Vet all third-party tools and enforce strict permissions. Poorly vetted plugins can compromise even robust models.

Continuous Threat Modeling for Generative AI and LLMs

AI and LLMs are not static — they evolve with:

  • New datasets
  • Plugin updates
  • Application context changes

Each of these shifts can create new vulnerabilities. Embed continuous threat modeling into your MLOps or DevSecOps pipelines.

Encourage collaboration across:

  • Security teams
  • AI developers
  • Risk officers
  • Product owners

Monitor live environments to uncover threats that were missed during testing.

Secure the Future of AI and LLMs

Traditional threat models can’t keep pace with the dynamic nature of AI and LLMs. As generative models become more powerful, they also become more vulnerable.

Continuous threat modeling equips organizations to anticipate, detect, and prevent threats before damage occurs.

Security professionals, engineers, and AI teams must work together to keep LLMs and generative AI safe, ethical, and compliant.

Ready to secure your AI stack? Contact us to integrate LLM-specific threat modeling into your pipeline.

Frequently Asked Questions

What are the security threats of LLM?

LLMs and generative AI introduce risks like prompt injection, fine-tuning abuse, and data leaks. These threats arise from model openness, plugin use, and untrusted inputs.

What is the LLM model of risk management?

Risk management in large language models (LLMs) involves zero-trust principles — verifying all inputs, minimizing permissions, and continuously monitoring model use.

What is the AI model for threat detection?

AI threat detection uses machine learning and behavioral analysis to detect abnormal activity and potential cyberattacks in real-time.

What is the difference between an LLM model and an AI agent?

LLMs are foundational models, while AI agents use LLMs to interact and perform tasks. Agents may include reasoning, memory, or plugin tools built atop LLMs.

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