Episode Details

Guest: Steve Wilson, Chief Product Officer, Exabeam [@exabeam] & Project Lead,  OWASP Top 10 for Larage Language Model Applications [@owasp]

On LinkedIn | https://www.linkedin.com/in/wilsonsd/

On Twitter | https://x.com/virtualsteve

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Hosts: 

Sean Martin, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining CyberSecurity Podcast [@RedefiningCyber]

On ITSPmagazine | https://www.itspmagazine.com/sean-martin

Marco Ciappelli, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining Society Podcast

On ITSPmagazine | https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/marco-ciappelli

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Episode Notes

In this episode of the Chat on the Road On Location series for OWASP AppSec Global in San Francisco, Sean Martin hosts a compelling conversation with Steve Wilson, Project Lead for the OWASP Top 10 for Large Language Model AI Applications. The discussion, as you might guess, centers on the OWASP Top 10 list for Large Language Models (LLMs) and the security challenges associated with these technologies. Wilson highlights the growing relevance of AppSec, particularly with the surge in interest in AI and LLMs.

The conversation kicks off with an exploration of the LLM project that Wilson has been working on at OWASP, aimed at presenting an update on the OWASP Top 10 for LLMs. Wilson emphasizes the significance of prompt injection attacks, one of the key concerns on the OWASP list. He explains how attackers can craft prompts to manipulate LLMs into performing unintended actions, a tactic reminiscent of the SQL injection attacks that have plagued traditional software for years. This serves as a stark reminder of the need for vigilance in the development and deployment of LLMs.

Supply chain risks are another critical issue discussed. Wilson draws parallels to the Log4j incident, stressing that the AI software supply chain is currently a weak link. With the rapid growth of platforms like Hugging Face, the provenance of AI models and training datasets becomes a significant concern. Ensuring the integrity and security of these components is paramount to building robust AI-driven systems.

The notion of excessive agency is also explored—a concept that relates to the permissions and responsibilities assigned to LLMs. Wilson underscores the importance of limiting the scope of LLMs to prevent misuse or unauthorized actions. This point resonates with traditional security principles like least privilege but is recontextualized for the AI age. Overreliance on LLMs is another topic Martin and Wilson discuss.

The conversation touches on how people can place undue trust in AI outputs, leading to potentially hazardous outcomes. Ensuring users understand the limitations and potential inaccuracies of LLM-generated content is essential for safe and effective AI utilization.

Wilson also provides a preview of his upcoming session at the OWASP AppSec Global event, where he plans to share insights from the ongoing work on the 2.0 version of the OWASP Top 10 for LLMs. This next iteration will address how the field has matured and new security considerations that have emerged since the initial list.

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