OWASP Top 10 for LLM Applications v2026

This course trailer introduces the OWASP Top 10 risks specific to Large Language Model (LLM) applications. It highlights the unique security challenges developers face when building with LLMs—such as prompt injection, data leakage, model poisoning, and misuse vulnerabilities—and outlines how the OWASP framework can help teams identify, assess, and mitigate these threats. You’ll get a preview of best practices, real-world examples, and a roadmap for securing LLM-enabled systems throughout their lifecycle.

Welcome to the front line of AI security. This foundational module introduces the unique security challenges of Large Language Models. We will define the new attack surface created by generative AI and provide a high-level overview of the OWASP Top 10 for LLMs, which will serve as our roadmap for the entire course.

This module explores prompt injection as a critical security risk in LLM applications, explaining how attackers manipulate inputs to override system instructions. It covers direct and indirect prompt injection techniques, their evolving sophistication, and real-world impacts such as data exfiltration, content filter bypass, and plugin abuse. The module concludes with practical prevention strategies, emphasizing a defense-in-depth approach that combines input validation, output filtering, architectural safeguards, and human oversight.

An LLM can become your organization’s biggest data leak. This module provides a deep dive into how models can inadvertently disclose sensitive information, from Personally Identifiable Information (PII) to proprietary code. You will learn the essential post-processing and sanitization techniques required to filter model outputs and prevent these critical breaches.

An LLM application is only as secure as its weakest link. This module dives into the critical risks of the AI supply chain, from compromised pre-trained models to vulnerable third-party plugins and datasets. You will learn to vet components, implement integrity checks, and apply best practices to secure the entire ecosystem your application depends on.

The integrity of a model is paramount. This module addresses the insidious threat of data and model poisoning, where attackers deliberately corrupt training data or fine-tuning processes to introduce vulnerabilities, biases, or backdoors. You will learn the critical techniques for data validation, provenance tracking, and maintaining model integrity against these foundational attacks.

The golden rule of security is to never trust input—and that includes output from an LLM. This module addresses the critical vulnerabilities that arise when downstream components blindly trust model-generated content. You will learn how this can lead to severe client-side and server-side attacks like XSS, CSRF, and SSRF, and master the essential practices of output validation, sanitization, and encoding.

Granting an LLM excessive autonomy and permissions creates a significant security risk. This module addresses the dangers of Excessive Agency, where an LLM has too much power to interact with other systems and take actions on its own. You will learn to apply the principle of least privilege to AI agents and implement critical human-in-the-loop controls for sensitive operations.

An application’s system prompt contains its core operational instructions and is often valuable intellectual property. This module addresses the critical risk of System Prompt Leakage, where attackers can manipulate an LLM into revealing these confidential instructions. You will learn the common attack vectors and the essential defensive programming techniques to protect your application’s core logic.

Embeddings are the numerical heart of an LLM, but they can also be a significant attack surface. This module addresses LLM, exploring vulnerabilities like adversarial attacks against vector representations, information leakage from embeddings, and poisoning of the vector space. You will learn the techniques to enhance adversarial robustness and secure the vector databases that underpin modern RAG systems.

The veracity of LLM-generated content is a critical security and reputational concern. This module focuses on how models can generate and disseminate believable but false information, leading to trust erosion and legal risks. You will learn to implement robust mitigation strategies, including grounding via RAG, providing citations, and establishing clear content provenance to ensure the integrity of your application’s output.

LLM applications can be exploited to consume excessive resources, leading to Denial of Service (DoS) or significant financial costs. This module addresses Unbounded Consumption, where you will learn to implement critical safeguards like setting usage quotas, monitoring resource consumption, and enforcing operational limits to prevent these costly attacks.

Having explored the individual vulnerabilities, it’s time to build a holistic security posture. This concluding module synthesizes our learnings into a practical framework, covering a defense-in-depth strategy, the role of red teaming, and how to integrate security into the AI development lifecycle. We will also look ahead at the evolving threat landscape to ensure your skills remain on the cutting edge of AI security.