OWASP Top 10 LLM and GenAI
Artificial Intelligence (AI) is now seen across multiple industries. Understanding the security implications of these technologies is more important than ever. The Open Web Application Security Project (OWASP) has identifyed and addressed the most critical security risks in software development. With the rapid rise of Large Language Models (LLMs) and generative AI, OWASP has extended its expertise to highlight the top 10 security concerns specific to these advanced AI systems. This learning path is designed to provide you with a comprehensive understanding of these risks and equip you with the knowledge to secure AI-driven applications.
By exploring these lessons, you will gain insight into the vulnerabilities inherent in LLMs, the potential consequences of these risks, and the best practices to mitigate them. Whether you are a developer, security professional, or AI enthusiast, mastering these concepts is essential to ensure the safe and ethical deployment of AI technologies in the real world.
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LLM01: Prompt Injection
Prompt injection is a vulnerability where attackers manipulate the input prompts of a language model to alter its behavior, potentially causing it to generate misleading, harmful, or unauthorized outputs.
LLM02: Insecure Output Handling
Insecure output handling refers to the failure to properly manage and sanitize the outputs generated by a system, which can lead to the disclosure of sensitive information, injection attacks, or other security vulnerabilities.
LLM03: Training Data Poisoning
Training data poisoning is a malicious attack where adversaries intentionally introduce incorrect or biased data into a machine learning model’s training set to manipulate the model’s outcomes or degrade its performance.
LLM04: Model Denial of Service
Model Denial of Service (DoS) refers to attacks that disrupt or exhaust a machine learning model’s resources, rendering it unable to process legitimate requests or operate effectively.
LLM05: Supply Chain Vulnerabilities
Supply chain vulnerabilities refers to the risks associated with third-party dependencies and components used in developing or deploying large language models, which can introduce security weaknesses and potential backdoors into the system.
LLM06: Sensitive Information Disclosure
Sensitive information disclosure involves the risk of a large language model unintentionally revealing confidential or private data through its responses or outputs.
LLM07: Insecure Plugin Design
Insecure plugin design refers to vulnerabilities in plugins or extensions used with large language models, which can introduce security flaws, leading to potential unauthorized access or data breaches.
LLM08: Excessive Agency
Excessive agency describes the risk of a large language model being given too much autonomy or control, potentially leading it to execute harmful actions or make decisions beyond its intended scope.
LLM09: Overreliance
Overreliance refers to the risk of users depending too heavily on large language models for critical tasks, which can lead to errors or harmful outcomes if the model’s outputs are inaccurate or biased.
LLM10: Model Theft
LLM10: Model Theft involves the unauthorized copying or replication of a large language model, leading to intellectual property loss and potential misuse of the stolen model.