Demystifying OWASP Top 10 Large Language Models
In the rapidly evolving field of artificial intelligence, large language models (LLMs) are becoming increasingly prevalent, powering applications like chatbots, virtual assistants, machine translation systems and many more. However, as with any emerging technology, LLMs introduce unique security risks that need to be addressed.
What you’ll learn
- Technology Enthusiasts, Security Professionals, IT, All.
- OWASP Top 10 for LLM.
- OWASP Top 10 for LLM.
- OWASP Top 10 for LLM.
- Technology Enthusiasts, Security Professionals, IT, All.
Course Content
- OWSAP To 10 for LLM –> 7 lectures • 1hr 6min.
Requirements
In the rapidly evolving field of artificial intelligence, large language models (LLMs) are becoming increasingly prevalent, powering applications like chatbots, virtual assistants, machine translation systems and many more. However, as with any emerging technology, LLMs introduce unique security risks that need to be addressed.
The OWASP Top 10 LLM Security Risks is a comprehensive framework that outlines the most critical vulnerabilities facing LLM applications today. This training course delves into these risks, providing participants with the knowledge and skills to identify, prevent, and mitigate LLM-related security threats.
Course Overview:
- Prompt Injection: Exploiting the ability of LLMs to generate text based on user prompts, attackers can inject malicious code or influence the LLM’s output.
- Insecure Output Handling: Neglecting to validate LLM outputs can lead to downstream security exploits, including code execution that compromises systems and exposes data.
- Training Data Poisoning: Introducing biased or malicious data into the training process can manipulate the LLM’s behavior, leading to biased or harmful outputs.
- Model Denial of Service: Overwhelming the LLM with excessive or malicious inputs can disrupt its normal operation, rendering it unavailable for legitimate users.
- Supply Chain Vulnerabilities: Compromising third-party plugins or pre-trained models can introduce vulnerabilities into LLM applications.
- Sensitive Information Disclosure: LLMs can unintentionally disclose sensitive information during training or operation, posing privacy risks.
- Insecure Plugin Design: Poorly designed plugins can introduce vulnerabilities into LLM applications, allowing unauthorized access or manipulation.
- Excessive Agency: Granting too much autonomy to LLMs can lead to unintended consequences and ethical dilemmas.
- Overreliance: Relying solely on LLMs for critical decision-making without adequate human oversight can lead to errors and biases.
- Model Theft: Stealing or replicating trained LLM models can enable attackers to exploit the model’s capabilities for malicious purposes.