AI Security Framework

⚡ Quick Summary

This white paper from Snowflake outlines a practical, threat-driven AI Security Framework focused on the real-world risks of deploying machine learning systems. It presents a taxonomy of 20+ security threats—including data leakage, model stealing, prompt injection, adversarial samples, and insider attacks—and provides actionable mitigation strategies for each. What sets this framework apart is its thoroughness: each threat is broken down by asset category, risk type, trigger condition, and includes summary, impact, example attack, and mitigation techniques. While not a formal security standard, the document offers an accessible blueprint for building a secure AI deployment pipeline—from training and inference to infrastructure.

🧩 What’s Covered

The report is structured as a catalog of AI-specific threats, each presented using a consistent format that includes:

Asset Category: Dataset, Model, Infrastructure, or General
Main Risk Type: Confidentiality, Integrity, Availability, Legal, Compliance
Trigger Condition: A real-world scenario that activates the risk

It covers over 20 threat types, including:

Training Data Leakage – Where generative models expose training data via prompts or storage breaches.
Privacy – Inference attacks and data re-identification techniques.
Bias – Especially when outputs are used in regulated or socially sensitive contexts.
Lack of Explainability – The security implications of “black box” models.
Backdooring (Insider Attacks) – Trojan or poisoned models introduced during training.
Prompt Injection / Indirect Prompt Injection – Exploits on LLM input handling and chaining with external systems.
Adversarial & Sponge Samples – Inputs that manipulate or degrade model behavior and availability.
Model Stealing & Inversion – Attacks to recreate or reverse-engineer proprietary models.
Training Data Poisoning / Model Poisoning – Supply chain threats that skew model learning.
Multitenancy & Inference Exposure – Risks in AI-as-a-service and API-based models.
Attacks on Infrastructure – Exploits on CI/CD, data pipelines, or OSS model vulnerabilities.

Each threat includes concrete mitigations, such as:

Differential privacy
Adversarial training
Rate limiting
Secure multiparty computation
Cryptographic signing
Monitoring and anomaly detection

A robust source section (pp. 23–24) references over 30 academic and industry papers from OWASP, IEEE, and ArXiv to ground the framework in current research.

💡 Why it matters?

Most AI security discussions remain vague or abstract. This framework is pragmatic, scenario-based, and immediately actionable. It recognizes that modern AI systems are not just code—they are composed of sensitive data, powerful inferential models, complex pipelines, and human inputs. By systematically mapping vulnerabilities across the lifecycle (training, inference, deployment), it helps security and ML teams anticipate and defend against real-world attacks. It’s especially valuable as enterprises increasingly deploy LLMs and third-party OSS models in production without mature security processes.

❓ What’s Missing

No architectural model: There’s no high-level diagram connecting threat types to system components.
No maturity model: Readers don’t get a phased roadmap (e.g., foundational vs. advanced defenses).
Limited regulatory mapping: Legal risks are mentioned but not aligned with GDPR, NIS2, or AI Act frameworks.
Mitigation depth varies: Some recommendations (e.g., watermarking, SMPC) are listed without implementation detail.
No threat prioritization: All threats are treated equally; it lacks scoring or risk ranking.

👥 Best For

Security architects and ML engineers seeking to build secure ML pipelines
CISOs and compliance leads responsible for AI governance and risk management
Product teams integrating LLMs or AI features into customer-facing tools
Red teams / adversarial ML researchers looking for threat modeling structures