GenAI Cybersecurity Solutions: OWASP Top 10 for LLM Apps

Explore generative AI, driven by machine learning, to create texts, images, and audio, with GANs and ethical concerns around copyright, misinformation, and biases in apps like ChatGPT and Copilot.

Celebrate reaching a milestone in the GenAI cybersecurity course, joining top 20% of students. Navigate playback rate, subtitles, and Q&A to progress in the OWASP top 10 for LLM apps.

Secure generative AI from cyber threats by protecting sensitive data, preventing leaks and tampering, upholding privacy and GDPR laws, and building trust and public safety in healthcare and finance.

Identify potential threats in genai applications by examining ai data, usage, application, and platform, including command injection, data exposure, data tampering, supply chain risks, insecure integrations, and breakout attacks.

Explore how GenAI can be misused through deepfakes, data poisoning, and model theft, and learn how attackers hijack AI systems and craft convincing phishing to exploit defenses.

Explore large language models (LLMs) as AI systems that understand and generate text, powered by neural networks. Learn their capabilities, limitations, and why careful, context-aware use matters.

An AI model acts as a digital brain trained with data and algorithms to make predictions and decisions. It learns patterns during training, can show biases, and powers everyday apps.

Create a Portswigger account to access the labs, complete registration via email, log in, and prepare for four practical exercises focused on the OWASP top ten LLM apps.

Examine prompt injection in GenAI apps, where crafted prompts bypass safeguards to leak data or modify outputs. Understand risks for chatbots, code tools, and AI assistants.

Explore direct and indirect prompt injection in llm apps, contrasting bold direct attacks with stealthy indirect tricks like poisoned webpage, and learn why indirect injections pose subtler, harder-to-detect risks.

Demonstrates indirect prompt injection in a lab setting by manipulating a live chat to delete a user account, illustrating how large language models interact with back-end apis and prompts.

Defend llm apps against prompt injection by implementing input filtering, output validation, and sandboxing. Use prompt frameworks and limited context windows to prevent leakage and manipulation.

Learn how insecure output handling after AI generation can unleash hidden code or malicious links when you fail to sanitize outputs before delivery, leading to data breaches and phishing.

This lecture presents a practical lab on insecure output handling in an AI-driven chat system, focusing on cross-site scripting attacks and indirect prompt injection within an LMS.

Implement guardrails against insecure output handling by sanitizing inputs and outputs, validating formats, using apis, sandboxing ai outputs, and monitoring with detailed logging.

Identify supply chain vulnerabilities in open source libraries, third-party APIs, and cloud services; vet vendors, monitor updates with SBOMs and code signing, and maintain a rapid fallback plan.

Model denial of service attacks target AI systems and large language models by overloading processing resources, causing resource drain and degraded quality of service for users.

Learn to stop model denial of service attacks with rate limiting and input validation. Enable auto scaling, prune and quantize, and deploy multi-region backups.

Explore how training data poisoning injects vulnerabilities, backdoors, and biases into AI systems, threatening performance and ethics. See stop-sign tampering in autonomous driving and real-world examples to illustrate security risks.

Clean and filter data to prevent training data poisoning, apply anomaly detection, and validate data provenance with tools like TensorFlow data validation to build resilient llm apps.

Prevent sensitive information disclosure in llm apps by securing data with encryption and strict access controls, and educating users to think before sharing personal or company details with ai.

Explore real-world sensitive data disclosures in ai systems, including chatbots leaking passwords and credit card details and resumes exposing private metadata.

Implement data sanitization and masking, use regex and NER to redact sensitive details, enforce role based access control, validate inputs and outputs, and apply anonymization or synthetic data.

Learn how plugins enhance llm capabilities, why they pose security risks, and how input validation, type checking, access controls, and monitoring reduce unauthorized data access and remote code execution.

Learn how to secure LLM plugins with input validation, sandboxing, permission controls, and monitoring. Keep plugins updated and use moderation tools to block malicious requests.