OWASP Top 10 for LLM Applications (latest version)

Discover the essential security vulnerabilities in Large Language Model (LLM) applications! This presentation is perfect for developers, data scientists, and security experts looking to enhance their understanding and protection of LLM systems.

Key Highlights:
Who is it for? Developers, Data Scientists, Security Experts
Purpose: Practical security guidance for navigating LLM application security
Making of the List: Insights from over 500 experts, refined into the top 10 vulnerabilities
Top Vulnerabilities Covered:
Prompt Injection: Learn how attackers manipulate LLMs through crafted inputs.
Insecure Output Handling: Understand the risks of inadequate validation and sanitization.
Training Data Poisoning: Discover methods to prevent manipulation of training data.
Model Denial of Service: Protect against resource-consuming attacks.
Supply Chain Vulnerabilities: Secure your pre-trained models and third-party datasets.
Sensitive Information Disclosure: Prevent unintended leaks of sensitive data.
Insecure Plugin Design: Avoid vulnerabilities in plugin inputs and access controls.
Excessive Agency: Restrict unauthorized actions by LLM-based applications.
Overreliance: Mitigate risks of trusting LLM outputs without validation.
Model Theft: Protect against unauthorized access and exfiltration of models.
Enhance your security posture and stay ahead of potential threats with our detailed guide.

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Don't miss out on these critical insights—watch now!

[anshajbhushan]

00:06 Overview of OWASP Top 10 for LLM Applications and its target audience.
02:30 Understanding and mitigating prompt injection threats in LLM applications.
04:57 Implement human oversight and secure output handling for LLMs.
07:22 Understanding and mitigating training data poisoning in LLM applications is crucial.
09:50 Understanding and mitigating model denial of service attacks is crucial for LLM security.
12:17 Supply chain vulnerabilities pose risks in machine learning operations.
14:45 Safeguard user privacy in LLM applications to prevent sensitive information leaks.
17:08 Insecure plugin design poses severe security risks.
19:17 Excessive agency poses security risks in LLM applications.
21:26 Overreliance on LLMs risks misinformation and security vulnerabilities.
23:33 Mitigating model theft in large language models is essential.
25:43 Implement robust defenses against model theft to prevent economic losses.
Crafted by Merlin AI.