June 20, 2025
AI Risk Assessment: Strategies, Tools, and Best Practices
AI Risk Assessment: Strategies, Tools, and Best Practices
Fiza Nadeem
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Conventional threat models assume predictable logic and static behaviors. In contrast, LLMs are data-driven systems capable of generating unpredictable outputs and interacting with users in open-ended ways.
Attacks like prompt injection, model inversion, and data leakage bypass many of the safeguards designed for traditional applications.
AI-specific threat modeling is identifying these emerging risks early and designing appropriate controls. It enables teams to understand unique attack surfaces across the AI lifecycle such as training data, model weights, inference APIs, and real-time prompts.
CISOs, AI developers, engineers, and risk managers all have a role to play in securing LLM-powered systems.
Threat modeling is a structured approach to mitigate potential security risks in a system before they can be exploited. When applied to AI, especially LLMs, it takes on a new dimension, as these systems don’t follow fixed rules or logic.
Instead, they rely on probabilistic outputs, vast training datasets, and often opaque internal processes. These include model inputs and outputs, training pipelines, third-party datasets, fine-tuning processes, and model-serving endpoints.
The variability and complexity of LLMs make them susceptible to subtle but serious attacks, like:
Threat modeling in this context involves:
It focuses on understanding how the system behaves under adversarial conditions, where attackers may target the model’s logic, training data, or interaction flow.
The goal is the same as traditional threat modeling: reduce risk by anticipating threats before they cause harm.
But the approach must evolve to match the unpredictability, scale, and nuance of modern AI systems.
The growing reliance on AI systems in core business operations means that security oversights can have serious consequences. LLMs can be exploited in ways that are difficult to detect and even harder to control.
As LLMs are increasingly used in public-facing applications, attackers can interact with them directly, turning them into entry points for exploitation.
Beyond technical risks, there’s also growing regulatory pressure. Frameworks like the EU AI Act and NIST AI RMF emphasize the need for transparency, accountability, and security in AI systems.
Threat modeling helps meet these expectations with a structured way to identify vulnerabilities and enforce risk-based controls.
First, identify the assets that need protection. These go beyond source code and infrastructure. They include training data, model weights, prompt logs, embeddings, inference APIs, and downstream outputs. Each of these can hold sensitive information or be manipulated to alter model behavior.
Next, consider the adversaries. These can range from casual users trying to bypass safeguards, to insiders with access to training data, or sophisticated attackers aiming to extract proprietary models. The open-ended nature of LLM interactions makes it easier for attackers to experiment and discover new exploit paths.
Threat modeling must also account for attack vectors unique to AI, such as prompt injection, data poisoning, model inversion, and jailbreaks. These threats exploit how the model processes information and learns from data.
You may want to read: Penetration Testing for LLMs.
One of the most widely used models is STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege).
While originally designed for traditional applications, STRIDE can be extended to cover AI-specific concerns. For instance, tampering with training data or inducing information disclosure through prompt engineering.
The MITRE ATLAS framework is specifically designed for AI and machine learning systems. It maps adversary tactics, techniques, and case studies to understand how attackers exploit models.
Another relevant guide is the OWASP Top 10 for LLMs, which outlines the most common and impactful vulnerabilities in LLM deployments. Such as insecure plugin use, training data poisoning, and prompt leakage. It serves as a practical checklist for teams evaluating LLM security posture.
Compliance-focused frameworks like the NIST AI Risk Management Framework (AI RMF) also offer structured guidance on identifying, measuring, and mitigating risks associated with AI systems.
Though not a threat model itself, it complements technical methodologies by promoting responsible AI practices and risk governance.
Choosing the right methodology or combining several depends on your system’s complexity, the threats you're most concerned about, and your organization's security maturity.
One of the most prominent risks is prompt injection, where an attacker embeds malicious instructions within user input or system context to manipulate the model’s behavior. This can lead to unauthorized actions, output manipulation, or leakage of sensitive prompts and data.
Another serious threat is model inversion, where attackers attempt to reconstruct private training data by querying the model. This is particularly dangerous when LLMs are trained on sensitive or proprietary datasets, such as medical records, chat logs, or internal documents.
Adversarial inputs are also a concern. These are specially crafted prompts designed to confuse or mislead the model, leading to harmful, biased, or incorrect outputs. Attackers can exploit this to spread misinformation, bypass content filters, or undermine user trust.
Additionally, fine-tuning abuse is becoming more common. In this scenario, bad actors take a publicly available model and fine-tune it with biased, misleading, or malicious content. Then distribute it as a “trusted” alternative.
Supply chain risks are also relevant. Many organizations rely on third-party models, datasets, or plugins. If these components are compromised, outdated, or poorly vetted, they can introduce vulnerabilities into otherwise secure systems.
While the general steps are similar to traditional threat modeling, the focus areas shift to accommodate the unique nature of LLMs.
The process typically begins with identifying critical assets. For LLMs, this could include training datasets, model weights, APIs, system prompts, user inputs, and generated outputs.
Anything that the model relies on or produces, especially if it’s exposed externally, should be evaluated as a potential risk point.
Determine which components interact with the model and at what level of access or control. For instance,
Clear boundaries help isolate where threats are most likely to occur.
Then, move on to threat enumeration. This step involves identifying how those assets and boundaries might be exploited.
Frameworks like STRIDE or OWASP Top 10 for LLMs can guide this stage.
Once threats are identified, rate their severity based on likelihood and impact. This helps prioritize which risks require immediate action and which can be monitored over time.
These may include input validation, rate limiting, output filtering, access controls, and model behavior monitoring. Where appropriate, align controls with existing security policies or compliance frameworks.
Mitigation in the AI context involves a combination of input handling, system design, model governance, and monitoring.
Start with input validation and prompt control. Restrict what types of inputs the model accepts, and sanitize user-generated content to minimize the risk of prompt injection.
For example, separating user input from system prompts using structured formatting or special tokens can reduce unintended instruction hijacking.
To protect sensitive data, implement privacy-preserving techniques such as differential privacy or federated learning during model training. These methods help reduce the risk of model inversion and data leakage by limiting the model’s ability to memorize and reproduce personal or proprietary information.
Output filtering and moderation are essential for managing harmful or inappropriate responses. Use post-processing filters or integrate human-in-the-loop systems where high-stakes outputs like medical advice or financial recommendations are involved.
Ensure access control and secure model hosting. Limit access to training data, fine-tuning capabilities, and model configuration files. Isolate production models from testing environments, and monitor API access for signs of abuse or abnormal behavior.
Enable real-time monitoring of model outputs, API usage, and user interactions. Flag unexpected patterns like repeated prompt injection attempts, excessive token usage, or attempts to access restricted functionality. Logging and alerting mechanisms help respond quickly to emerging threats.
Finally, address plugin and integration risks. Vet third-party tools, validate data sources, and restrict model actions when integrating with external systems (e.g., APIs or databases).
A compromised plugin could easily be used to exfiltrate sensitive information or trigger unintended actions.
New risks can emerge with each model version or integration. For example, an LLM fine-tuned for a customer support task might behave differently when deployed to a financial service chatbot.
Similarly, introducing plugins, adding new user personas, or changing prompt formats can open the door to unintended behavior or vulnerabilities.
To stay ahead, organizations should embed threat modeling into their MLOps or DevSecOps pipelines. Every change should trigger a reevaluation of potential threats. This includes threat assessments during development, testing, and post-deployment phases.
Cross-functional collaboration is key. Security teams, AI developers, compliance officers, and product managers should all have visibility into threat modeling outcomes.
Monitoring model performance, user behavior, and security events can surface issues that may not have been anticipated during the initial modeling phase.
AI-specific threat modeling is more urgent than ever. Traditional security approaches fall short in accounting for the unique behaviors, risks, and attack vectors introduced by dynamic, data-driven models.
Continuous threat modeling enables organizations to better understand their evolving attack surface and reduce the likelihood of costly or harmful incidents.
Security leaders, AI developers, and risk professionals all have a role to play. Early and ongoing threat modeling helps future-proof your systems against emerging threats.
Contact our experts to integrate AI-specific threat modeling into your development lifecycle.
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