Unveiling The Top 10 Devastating AI Attacks According to OWASP.

In this video, according to the OWASP Top 10 for Large Language Model Applications, you will see the top 10 security risks for AI systems, particularly those using large language models (LLMs), are:
1. Prompt Injection: Manipulating LLMs via crafted inputs to gain unauthorized access or compromise decision-making.
2. Insecure Output Handling: Failing to validate LLM outputs can potentially lead to downstream security exploits like code execution.
3. Training Data Poisoning: Tampering with training data to impair LLM models, affecting security, accuracy, or ethical behavior.
4. Model Denial of Service: Overloading LLMs with resource-heavy operations causes service disruptions and increased costs.
5. Supply Chain Vulnerabilities: Relying on compromised components, services, or datasets that can undermine system integrity.
6. Sensitive Information Disclosure: Failing to protect against disclosure of sensitive information in LLM outputs.
7. Insecure Plugin Design: LLM plugins process untrusted inputs with insufficient access control, risking severe exploits.
8. Excessive Agency: Granting LLMs too much autonomy to take action, potentially leading to unintended consequences.
9. Overreliance: Failing to assess LLM outputs critically can lead to compromised decision-making and security vulnerabilities.
10. Model Theft: Unauthorized access to proprietary large language models, risking theft and dissemination of sensitive information.
These risks highlight AI systems' unique challenges, particularly those utilizing LLMs, and emphasize the need for robust security measures in their development and deployment.