Generative AI and large language models (LLMs) introduce a new class of security risks that traditional cloud threat modeling frameworks were never designed to handle.
This guide provides a practical, enterprise-grade approach to AI and LLM threat modeling, focused on real-world deployments using cloud-native AI services and custom models.
Why Traditional Threat Modeling Fails for AI Systems
Most threat modeling frameworks were built for predictable systems. AI systems are fundamentally different:
- Behavior is probabilistic, not deterministic
- Inputs are unstructured and attacker-controlled
- Models can leak training data
- Outputs can cause downstream harm
If you’re new to cloud threat modeling concepts, start with our foundational guide to cloud threat modeling.
AI & LLM Threat Modeling Architecture
Before identifying threats, clearly map your AI system architecture. Typical components include:
- User-facing AI interfaces (chatbots, copilots)
- API gateways and authentication layers
- Prompt processing logic
- LLM APIs or hosted models
- Vector databases and embeddings
- Logging, analytics, and feedback loops
This architecture should be analyzed using the same lifecycle described in our cloud threat modeling lifecycle guide.
Key AI & LLM Threat Categories
1. Prompt Injection Attacks
Prompt injection occurs when attackers manipulate inputs to override system instructions.
- Instruction override (“Ignore previous rules”)
- Hidden prompts embedded in documents
- Indirect injection via external data sources
Impact: Data leakage, policy bypass, unauthorized actions.
2. Training Data Poisoning
Attackers manipulate training data to influence model behavior.
- Malicious examples in public datasets
- Bias injection
- Backdoor triggers
Impact: Long-term integrity compromise.
3. Model Extraction & Inference Attacks
Adversaries attempt to reverse-engineer models through repeated queries.
- Model weight inference
- Training data reconstruction
- Intellectual property theft
4. AI Output Abuse
Even correctly functioning models can produce harmful results.
- Hallucinated legal or medical advice
- Code generation vulnerabilities
- Automated misinformation
These risks are often underestimated and rarely mapped in traditional frameworks like STRIDE, explained in our threat modeling frameworks guide.
Applying Threat Modeling Frameworks to AI Systems
AI threat modeling works best when extending existing frameworks:
- STRIDE: Identity abuse, data disclosure, privilege escalation
- PASTA: Business impact of AI misuse
- LINDDUN: Privacy leakage from training data
Use AI-specific threat categories alongside cloud-native risks such as IAM abuse and API exposure.
Risk Prioritization for AI & LLM Systems
US enterprises typically prioritize AI threats based on:
- Likelihood of user-controlled input abuse
- Regulatory and legal exposure
- Reputational damage
- Cost of model compromise
High-priority risks almost always involve prompt injection and data leakage.
Mitigation Strategies for AI & LLM Threats
Technical Controls
- Strict input validation and prompt filtering
- Separation of system and user prompts
- Rate limiting and anomaly detection
- Output moderation and post-processing
Architectural Controls
- Least-privilege access to AI services
- Isolated inference environments
- Controlled access to vector databases
These mitigations should be mapped into the broader cloud lifecycle described in Part 3 of this series.
AI Threat Modeling Across IaaS, PaaS, and SaaS
AI risks vary significantly depending on service model:
- IaaS: Model theft, infrastructure compromise
- PaaS: API abuse, serverless injection risks
- SaaS: User data leakage, over-permissioned access
For a deeper breakdown, see our IaaS vs PaaS vs SaaS threat modeling guide.
Key Takeaways
- AI threat modeling requires new categories beyond traditional frameworks
- Prompt injection is the most common real-world AI attack
- Lifecycle-based modeling is essential for AI systems
- AI risks must be integrated into cloud threat modeling programs
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