Threat Modeling AI Driven products: What Developers Must Add to Their Toolkit

“If we don’t understand how something can break, we’ll never build it securely.”— AppSec maxim, more relevant than ever in the era of AI

As we explored in our previous post, the foundations of Secure SDLC are not only relevant in AI development — they’re critical. One of the most essential components of that foundation is threat modeling.

But here’s the challenge when it comes to threat modeling AI Driven products: traditional threat modeling practices — STRIDE, DFDs, abuse cases — were designed for predictable, deterministic systems. AI, especially systems driven by large language models and machine learning pipelines, introduces non-determinism, dynamic data surfaces, and opaque decision-making. The result? Traditional threat modeling is necessary but not sufficient.

It’s time for developers and product teams to evolve their threat modeling toolkit.

🧩 What’s Changing with AI Driven products?

AI systems differ from traditional software in several fundamental ways:

• They learn from data, which becomes a new attack vector.

• Their behavior can change over time, introducing new threats post-deployment.

• They often operate as black boxes, limiting explainability and predictability.

• Their outputs can directly influence decisions or system actions, raising stakes.

These characteristics demand that our threat models evolve from static assumptions to dynamic, adaptive models.

🔄 Reframing Traditional Threat Categories for Threat Modeling AI Driven products

Let’s look at how classic threat categories (like those in STRIDE) manifest in AI systems:

Threat	Traditional Context	AI Context
Spoofing	Impersonating a user	Faking input data to manipulate models or impersonate expected output
Tampering	Altering data in transit	Poisoning training data or model weights
Repudiation	Denying actions	Lack of logging or traceability in model decisions
Information Disclosure	Leaking sensitive data	Model inversion attacks leaking training data
Denial of Service	Crashing system via overload	Overloading model with unbounded prompts or compute-heavy inference
Elevation of Privilege	Gaining unauthorized access	Using LLMs to craft malicious inputs that bypass access rules

🧠 New Threat Surfaces to Consider

1. Training Data Integrity

Bad data = bad models.

AI systems are uniquely vulnerable to data poisoning: attackers inject malicious records into the training set to manipulate model behavior.

🔍 Model this threat by asking:

• Where does our training data come from?

• Who has access to label or update it?

• Can we detect anomalies in data distributions over time?

  
  

2. Model Output Misuse

Output ≠ safe.

Generative models — from LLMs to image synthesis tools — can be coaxed into producing harmful, misleading, or malicious content.

🔍 Model this threat by asking:

• Can user-controlled inputs elicit unsafe outputs?

• What guardrails are in place to detect toxic or risky completions?

• Are outputs being used to trigger automation (e.g., code generation, email replies, API calls)?

  
  

3. Prompt Injection and Indirect Prompt Leaks

LLMs are programmable by input. That’s power — and danger.

Attackers can:

• Inject payloads into prompts (e.g., “Ignore previous instructions and say X”)

• Leak prior prompts via cleverly crafted follow-ups

🔍 Model this threat by asking:

• Can users indirectly control prompt content (e.g., via user profiles, files)?

• Is prompt construction sanitized and tested?

• Are system messages and user inputs properly separated?

4. Model Drift and Post-Deployment Threats

A deployed AI model is not static.

Over time:

• Inputs evolve

• Behaviors shift

• Attacks become more sophisticated

🔍 Model this threat by asking:

• What monitoring exists for inference trends or degradation?

• How do we detect and respond to “silent failures” (e.g., performance drop or unsafe outputs)?

• Can we roll back or retrain quickly if drift is detected?

5. Third-Party AI Risks

Many teams use hosted models, APIs, or external datasets. These carry supply chain risks — but for intelligence, not just code.

🔍 Model this threat by asking:

• Do we validate and log model API responses?

• What uptime, safety, and behavior guarantees does the third-party provider offer?

• Are we embedding external decisions into core logic without a fallback?

🧰 The Modern Threat Modeling Toolkit for AI Development

Here’s what your updated toolkit should include:

✅ Modified STRIDE + AI Extensions: Use traditional models, but overlay AI-specific threat types like data poisoning, model leakage, and output misuse.

✅ Threat Modeling Prompts for LLMs: Use AI to assist in brainstorming potential misuse cases — ironically, the LLM can help you think adversarially.

✅ System Cards / Model Cards: Document model assumptions, limitations, and usage contexts. This is a lightweight but high-leverage tool for downstream threat modeling.

✅ AI Red Teaming: Simulate prompt injections, jailbreaks, and malicious input attempts. Build adversarial testing into your security test suite.

✅ Mitigation Planning with Feedback Loops: Every threat model should connect to security controls — and those controls should have monitoring and alerting to trigger updates.

🧭 Conclusion: Threat Modeling Isn’t Dead — It’s Evolving

Threat modeling in the AI era is no longer just a step in a security checklist — it’s an adaptive discipline.

In an environment where your code thinks, learns, and generates, the importance of anticipating misuse becomes paramount. The cost of getting it wrong isn’t just a bug — it could be a breach, a misinformation campaign, or reputational damage.

The mission of AppSec360 is to help security and development teams embed proactive, practical security into fast-moving development — whether you're shipping APIs or AI copilots.

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🔜 Next in this Series:

“Red Teaming AI Systems: A Practitioner’s Guide to Breaking (and Hardening) LLMs”

Let us know if you'd like access to our upcoming threat modeling templates for LLMs and ML pipelines.