The use of AI is growing quickly, but, unfortunately, so are security incidents, with organizations reporting a sharp increase in AI-related breaches and vulnerabilities. At the same time, most development teams are building AI systems that they don’t yet know how to secure.
That’s where threat modeling for AI can help. It addresses security risks by identifying vulnerabilities in data pipelines, model inference, and agentic workflows before attackers find them.
What Is AI Threat Modeling?
AI threat modeling is a structured process for identifying, analyzing, and mitigating security risks specific to artificial intelligence systems. This differs from traditional software, which usually behaves in a predictable way. If you give it the same input, you’ll get the same output.
However, AI systems don’t work that way. They produce outputs based on probability rather than fixed logic, meaning the same prompt might generate different responses each time. And in the case of autonomous agents, they can make decisions without human oversight.
To understand where risks could appear, it helps to think of AI systems in three layers:
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Data layer: This is the information that goes into the model, such as training datasets, embeddings, vector databases, and the data sources it connects to.
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Model layer: How the model processes requests and generates responses, including the steps it takes to produce an output, how it was fine-tuned, and the parameters it learned during training.
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Agentic layer: The capabilities that let AI systems take action, including using tools, remembering past interactions, and making decisions without waiting for human approval.
Frameworks like MAESTRO from the Cloud Security Alliance and the OWASP Top 10 for LLM Applications have emerged specifically to address AI risks. With the right tools, threat modeling is increasingly something development teams can do themselves, rather than waiting for security specialists.
Why Traditional Threat Modeling Is Limited for AI Systems
Conventional threat modeling assumes that data flows can be traced through code that behaves consistently. STRIDE, for example, works well when you can map exactly how information moves through a system and where trust boundaries exist.
AI breaks those assumptions, because a large language model might give you different answers to the same question. Its decision-making process is hidden inside billions of parameters whose internal interactions are difficult to fully interpret.
These systems behave unpredictably and can exhibit unexpected behavior under certain inputs or conditions. They can create new security vulnerabilities as they are updated and exposed to new inputs.
All of this means that AI systems don’t fail the way traditional software does. Instead of predictable crashes, you get hallucinated outputs, manipulated reasoning, and autonomous agents taking actions nobody authorized.
Traditional frameworks still remain a useful foundation, as teams building AI applications benefit from extending them with AI-specific threat categories.
Frameworks for AI Threat Modeling
Several frameworks help security teams structure their analysis, and each serves a different purpose. Many organizations combine them depending on the type of AI system they’re building.
OWASP Top 10 for LLM Applications and Agentic Applications
The ever-useful OWASP Top 10 lists are valuable here. The OWASP Top 10 for LLM ranks the most critical security risks in generative AI and covers threats such as prompt injection, insecure output handling, and sensitive information disclosure. The framework works well as a checklist during design reviews and as a foundation for developer training.
The Top 10 for Agentic Applications addresses the risks that emerge when AI systems can plan, act, and make decisions autonomously. If you’re building or securing agentic systems, both lists are relevant.
The OWASP AI Exchange also offers a threat model one-pager that walks teams through each threat using a simple When/Impact decision tree, a practical companion for teams doing their first AI threat model.
MITRE ATLAS
ATLAS (Adversarial Threat Landscape for AI Systems) is a knowledge base of adversarial tactics and techniques targeting machine learning. It’s modeled after the well-known ATT\&CK framework, so red teams and threat intelligence analysts will find the structure familiar. ATLAS helps teams understand how attackers actually compromise AI systems in practice.
STRIDE Adapted for AI Systems
The classic STRIDE model (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) can be used to cover AI-specific vectors.
Teams can adapt this familiar model to AI systems by mapping AI-specific threats to these categories, for example, poisoning training data fits under Tampering, while extracting model weights falls under Information Disclosure.
MAESTRO for Agentic AI Architectures
As mentioned earlier, MAESTRO, which stands for Multi-Agent Environment, Security, Threat, Risk, and Outcome, is a framework built specifically for autonomous agents. It covers risks that don’t show up in simpler LLM applications, such as how agents communicate with each other, how they use tools, and how their decision-making can be manipulated.
If you’re building agentic systems, MAESTRO gives you a structure that other frameworks don’t provide.
Threat Modeling Large Language Models
LLMs introduce a distinct set of vulnerabilities that security teams encounter repeatedly. Understanding each threat is the first step toward designing effective controls.
Prompt Injection
Prompt injection is the most common LLM vulnerability according to OWASP, because it targets how the model interprets and follows instructions.
It occurs when an attacker embeds malicious instructions into their input, tricking the model into doing something it shouldn’t. For example, an attacker could hide commands inside a question so that the model reveals its system instructions, ignores safety rules, or takes actions it’s not supposed to.
Prompt injection isn’t limited to user input. In RAG or integrated systems, attackers can hide malicious instructions in retrieved content, leading to indirect prompt injection.
Training Data Extraction
Adversaries can craft prompts designed to recover memorized training data. If that training data included sensitive details, such as customer information, proprietary code, or internal documents, the right questions might pull out exact copies.
Research has shown this can work on major AI models, which is why tracking where your training data comes from and keeping it clean is so important.
Hallucinations and Unreliable Outputs
Hallucinations occur when a model produces outputs that are incorrect or not based on its input or training data, often presented confidently as fact. The problem is that when people or systems trust false information and act on it, such as approving fake transactions or citing non-existent legal cases.
Sensitive Data Disclosure
LLMs can leak confidential information in several ways aside from repeating training data. They could expose details from RAG databases, reveal their system prompts, or share information from other users’ conversations. An attacker could manipulate the model into disclosing another person’s data or try to get details about how the system is configured.
Threat Modeling Autonomous Agents and Agentic AI
Agentic AI systems are autonomous because they can retain information, use external tools, and make decisions independently.
This creates bigger security risks than standard LLMs because agents can perform actions without first asking for permission, such as sending emails, modifying databases, or making purchases. Despite these risks, only 29% of organizations feel ready to secure these systems.
Agentic Misalignment
Misalignment is when an agent does something different from what’s expected, even though it’s technically following instructions.
It can happen when instructions are unclear, when the agent finds an unexpected way to optimize its task, or when someone adds malicious instructions to a prompt. The agent might behave in surprising ways while believing it’s doing exactly what it should.
Unauthorized Actions and Privilege Escalation
Agents typically need access to APIs, databases, and other services to do their work. If clear boundaries aren’t set, an agent could end up deleting files, spending money, or accessing sensitive systems.
The principle of least privilege is important here, only giving the agent access to what it absolutely needs. The problem is that many teams grant agents broad permissions because it’s easier than figuring out the minimum required access.
Inter-Agent Communication Risks
When multiple agents work together, they share information and coordinate through messages or shared memory. If one agent gets compromised or tricked, it can influence the others. The manipulated agent then spreads malicious instructions through conversations.
Goal Manipulation and Reward Hacking
Attackers can try to change what an agent thinks it’s supposed to achieve. In systems where agents learn from feedback (reinforcement learning), this might mean corrupting the signals that tell the agent whether it’s doing well or poorly.
In LLM-based agents, it could mean changing the context or instructions that define the agent’s objectives. Either way, the agent ends up pursuing the wrong goals while thinking it’s doing the right thing.
The AI Threat Modeling Process
To be effective, a threat modeling process should be repeatable and streamlined enough to be easily scalable.
1. Define What You’re Protecting
Start by listing what’s most important. That could be the model itself, the prompts that guide it, the data it was trained on, external sources it pulls from, the tools it can access, and the trust users have in its outputs. Also, define what the system should never be allowed to do.
2. Map How the System Works
Map out the data flow. Ask questions such as where do prompts come from? How does the system retrieve context? What does it remember between sessions? Which APIs and tools can it call?
3. Identify AI-Specific Threats
To identify risks, use the frameworks mentioned earlier, such as MAESTRO, STRIDE (adapted for AI), or MITRE ATLAS. It’s important to think like an attacker to try to figure out where they could inject instructions, poison data, or manipulate the agent’s goals.
4. Rank Threats by Impact
Not every possible threat needs to be acted on. For each risk identified, ask if the threat applies to your system, given how it’s built, and if so, what is the realistic level of harm? A model inversion attack only matters if your training data is sensitive.
Indirect prompt injection in an agentic system only needs to be treated if the agent has a pathway to exfiltrate data. If it can’t send data anywhere, the risk is much lower.
5. Build Security into the Design
It’s better to address threats during the design phase rather than having to patch them later (it’s also less costly that way). Start by limiting what the agent can access so that it only has the tools it needs. From there, require human approval for anything high-risk, make sure system instructions can’t be overridden by user input, and validate outputs before they’re acted on.
6. Make Agent Actions Traceable
When an agent takes an action, it’s important to understand the reasons behind it. It’s a good idea to record why the agent made a decision, taking into account the input, context, and other intermediate steps. Also, have a response plan in place if automated safeguards should fail.
7. Approach the Threat Model as a Living Document
AI systems change constantly through model updates, prompt changes, and new integrations. Schedule reviews whenever the system changes and make sure someone owns that responsibility.
How to Integrate AI Threat Modeling into the SDLC
Knowing how to threat model is one thing, but it’s also important to know when to do it across the development lifecycle.
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Design phase: Generate threat models from architecture descriptions before coding begins.
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Development phase: Update models as components change and link threats that were found to work items in Jira or Azure DevOps.
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Review phase: Validate threat models during security reviews and audits, ensuring controls were implemented as planned.
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Production phase: Monitor continuously by logging prompts, reporting unusual outputs, and tracking changes in model behavior. Threats that didn’t apply at launch may become relevant later.
How SecureFlag Helps with Threat Modeling AI
SecureFlag helps identify AI risk early and provides teams with training to address it, combining automated threat modeling with hands-on secure coding training on a single platform.
ThreatCanvas generates threat models in seconds from a text description, architecture diagram, or Infrastructure as Code file, and automatically maps threats against frameworks, including OWASP LLM and agentic AI, STRIDE, LINDDUN, and compliance requirements.
For teams looking to scale threat modeling across development, SecureFlag’s Rapid Developer-Driven Threat Modeling (RaD-TM) approach keeps the process lightweight by focusing on individual features rather than entire systems. Risk templates give developers a structured starting point without needing a security specialist in the room.
When a threat model raises a prompt injection risk, developers can immediately practice exploiting and fixing that vulnerability in a realistic lab environment. That turns threat models into skill-building opportunities rather than merely compliance exercises.
Book a demo to see ThreatCanvas generate an AI threat model in seconds.
FAQs about AI Threat Modeling
How often should organizations update AI threat models?
AI threat models work best as living documents rather than one-time artifacts. Teams typically review them whenever the system architecture changes, new model versions are deployed, or emerging threats are disclosed in the security community.
Which compliance standards require AI threat modeling?
Frameworks such as the EU AI Act, NIST AI RMF, and industry-specific regulations increasingly expect documented risk assessments. Organizations that have to comply with PCI DSS, HIPAA, and NIS2 often find that threat modeling supports their existing compliance efforts when AI systems manage regulated data.
Who should participate in AI threat modeling sessions?
Effective AI threat modeling brings together data scientists, ML engineers, developers, security teams, and product owners. Each can contribute their own expertise. For example, data scientists understand model behavior, developers know the infrastructure, security teams recognize attack patterns, and product owners clarify business context.
Can organizations fully automate AI threat modeling?
Automation speeds up the process and keeps threat models current, but it can’t replace human judgment. Tools can help identify threats, but people still need to assess whether those threats are realistic and decide which ones are important to the business.
What distinguishes AI red teaming from AI threat modeling?
Threat modeling starts at design time to identify risks before anything is built. Red teaming happens after deployment, to actively try to break what’s already running. Both are vital, but one shapes how the application is built, and the other determines whether it worked.
