OWASP Top 10 for LLM Applications 2025 Edition

Course Overview

• Immerse yourself in the critical realm of Large Language Model (LLM) security, exploring the nuanced vulnerabilities that transcend traditional cybersecurity paradigms.
• Gain a profound understanding of the 2025 update to the OWASP Top 10 for LLM Applications, the definitive framework for assessing and mitigating risks in generative AI systems.
• Discover the evolving threat landscape specific to AI, from sophisticated adversarial prompts to data exfiltration risks inherent in LLM interactions.
• Explore the architectural considerations for deploying LLMs securely, emphasizing design principles that prevent common and emerging attack vectors.
• Unpack the inherent challenges of securing non-deterministic systems, where output variability introduces unique security complexities.
• Grasp the societal and business impact of LLM security failures, from reputational damage to regulatory non-compliance.
• Understand the interplay between LLM security and broader enterprise security policies, integrating AI safeguards into your organizational defense strategy.
• Contextualize the urgency of proactive LLM security in an era where AI-powered applications are rapidly becoming ubiquitous across industries.

Requirements / Prerequisites

• A foundational understanding of software development principles and common programming paradigms.
• Basic familiarity with the concepts behind large language models, including their typical applications and operational mechanisms.
• An interest in cybersecurity and an eagerness to explore new and emerging threat categories specific to artificial intelligence.
• Comfort with reviewing code snippets and understanding logical flows, particularly in a security context.
• Access to a development environment and the ability to interact with web APIs (e.g., through Python or similar languages) for practical exercises.
• While not strictly mandatory, prior exposure to general web application security concepts will enhance the learning experience.
• A commitment to ethical hacking principles and responsible disclosure practices during security testing.

Skills Covered / Tools Used

• Master the art of crafting robust input validation and sanitization pipelines specifically engineered to counter sophisticated prompt injection attacks.
• Develop dynamic content moderation and output filtering mechanisms capable of detecting and redacting sensitive information or malicious code generated by LLMs.
• Learn to secure LLM API endpoints and third-party plugin integrations against unauthorized access, manipulation, and data leakage.
• Acquire advanced techniques for adversarial testing and red teaming of LLM applications, including crafting intricate jailbreaks and data exfiltration attempts.
• Implement continuous security monitoring frameworks tailored for LLM deployments, tracking anomalies and detecting real-time security events.
• Utilize specialized open-source tools and frameworks designed for LLM vulnerability assessment, such as prompt injection testing kits and AI-specific fuzzers.
• Apply secure prompt engineering best practices, including instruction tuning, system message hardening, and response format enforcement to minimize attack surfaces.
• Design and implement robust guardrail mechanisms and content policy enforcement layers to maintain control over LLM behavior and outputs.
• Explore methods for securing multi-agent LLM architectures, understanding the unique attack vectors introduced by inter-agent communication.
• Investigate tokenization and embedding security, identifying risks associated with embedding manipulation and privacy leakage through vector stores.
• Develop strategies for securing data used in LLM fine-tuning processes, preventing data poisoning and model backdooring.
• Conduct forensic analysis of LLM security incidents, identifying attack origins, compromised data, and mitigation strategies.
• Apply industry-standard risk assessment methodologies, adapted for the unique context of AI systems, to build comprehensive security postures.
• Implement identity and access management (IAM) controls specifically tailored for LLM resource access and data interaction.
• Leverage secure coding practices for integration code that interfaces with LLM APIs, preventing common integration vulnerabilities.

Benefits / Outcomes

• Gain the expertise to confidently design, deploy, and manage LLM-powered applications with an embedded, robust security posture.
• Become a pivotal security resource within your organization, capable of navigating the complex and rapidly evolving landscape of AI security.
• Contribute significantly to the development and responsible deployment of ethical and secure AI systems across various industries.
• Acquire a competitive edge in the high-demand field of AI/ML security engineering, opening doors to advanced career opportunities.
• Proactively identify, analyze, and mitigate novel attack vectors unique to generative AI models and their integration points.
• Cultivate a security-first mindset throughout the entire LLM application lifecycle, from conception and development to deployment and maintenance.
• Lead strategic discussions on AI risk, compliance, and governance within your teams and organizations, establishing yourself as an authority.
• Empower yourself to audit existing LLM systems, identify weaknesses, and implement fortifications against sophisticated and zero-day threats.
• Understand the legal, ethical, and reputational implications of LLM security failures, enabling informed decision-making.
• Develop the practical skills to build and manage a comprehensive LLM security program, ensuring long-term resilience against cyber threats.

PROS

• Highly Topical: Addresses the most current and urgent cybersecurity challenges facing artificial intelligence in 2025.
• Practical & Actionable: Provides concrete, hands-on strategies and techniques directly applicable to real-world LLM deployments.
• Industry-Standard Framework: Structured around the authoritative OWASP Top 10, ensuring comprehensive and recognized coverage.
• Career-Advancing: Equips learners with in-demand skills for a rapidly growing and critical domain of cybersecurity.
• Updated Content: Reflects the latest threat models and defense mechanisms, ensuring relevance and effectiveness.
• Holistic Approach: Covers a wide spectrum of LLM security, from technical vulnerabilities to risk management and compliance.

CONS

• As an introductory-to-intermediate course, deeply specialized, cutting-edge LLM attack research may require further dedicated, advanced study.