AI CyberSecurity Podcast
Innovating Security Practices with AI
Feb 2, 2024
Topics discussed include: custom AI agents, prompt engineering, data loss prevention, artificial general intelligence (AGI), AI's role in data and API security, risks of prompt engineering, and the latest innovations in AI security.
42:26
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Quick takeaways
- Prompt injection in language models (LMs) is a significant concern for security and trustworthiness, requiring a multi-layered approach to protect against malicious commands and data extraction.
- Advancements in data loss prevention (DLP) are needed to handle complex AI data types and automatically identify and protect sensitive information specific to AI applications, enhancing overall security.
Deep dives
The challenge of prompt injection and its impact on LMs
Prompt injection in language models (LMs) is a significant concern when it comes to their security and trustworthiness. Prompt injection is similar to SQL injection or cross-site scripting and can be exploited to execute malicious commands or extract sensitive data. It poses a particular risk when LMs are in decision-making roles or have access to critical information. Prompt injection can come from various sources, including third-party websites, documents, and metadata. Protecting against prompt injection requires a multi-layered approach, including using models that specialize in prompt injection detection and employing sandboxed versions of LMs to mitigate the risk.
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