#160 Security in Generative AI

In this episode, host Darren Pulsipher is joined by Dr. Jeffrey Lancaster to delve into the intersection of generative AI and security. The conversation dives deep into the potential risks and challenges surrounding the use of generative AI in nefarious activities, particularly in the realm of cybersecurity. The Threat of Personalized Phishing Attacks
One significant concern highlighted by Dr. Lancaster is the potential for personalized and sophisticated phishing attacks. With generative AI, malicious actors can scale their attacks and craft personalized messages based on information they gather from various sources, such as social media profiles. This poses a significant threat because personalized phishing attacks are more likely to bypass traditional spam filters or phishing detection systems. Cybercriminals can even leverage generative AI to clone voices and perpetrate virtual kidnappings.
To combat this threat, organizations and individuals need to be extra vigilant in verifying the authenticity of messages they receive. Implementing secure communication channels with trusted entities is essential to mitigate the risks posed by these personalized phishing attacks.
 Prompt Injection: A New Avenue for Hacking
The podcast also delves into the concept of prompt injection and the potential security threats it poses. Prompt injection involves manipulating the input to large language models, allowing bad actors to extract data or make the model behave in unintended ways. This opens up a new avenue for hacking and cyber threats.
Companies and individuals utilizing large language models need to ensure the security of their data inputs and outputs. The recent Samsung IP leak serves as a cautionary example, where sensitive information was inadvertently stored in the model and accessible to those who know the right prompts. The podcast emphasizes the importance of considering the security aspect from the beginning and incorporating it into conversations about using large language models.
 The Implications of Sharing Code and Leveraging AI Tools
Another key topic discussed in the podcast is the potential risks and concerns associated with sharing code and utilizing AI tools. While platforms like GitHub and StackOverflow provide valuable resources for developers, there is a need to be cautious about inadvertently sharing intellectual property. Developers must be mindful of the potential risks when copying and pasting code from public sources.
The podcast highlights the importance of due diligence in evaluating trustworthiness and data handling practices of service providers. This is crucial to protect proprietary information and ensure the safe use of AI tools. The conversation also touches on the growing trend of companies setting up private instances and walled gardens for enhanced security and control over intellectual property.
 Harnessing AI for Enhanced Cybersecurity
The podcast delves into the future of AI and its potential impact on cybersecurity. One notable area of improvement is the use of smaller, specialized AI models that can be easily secured and controlled. These models can be leveraged by companies, particularly through partnerships with providers who utilize AI tools to combat cyber threats.
AI can also enhance security by detecting anomalies in patterns and behaviors, such as unusual login times or locations. Additionally, the expansion of multifactor authentication, incorporating factors like voice recognition or typing cadence, further strengthens security measures.
While AI presents great potential for improving cybersecurity, the podcast stresses the importance of conducting due diligence, evaluating service providers, and continuously