AI, LLMs and Security: How to Deal with the New Threats

The use of large language models (LLMs) has become widespread, but there are significant security risks associated with them. LLMs with millions or billions of parameters are complex and challenging to fully scrutinize, making them susceptible to exploitation by attackers who can find loopholes or vulnerabilities. On an episode of The New Stack Makers, Chris Pirillo, Tech Evangelist and Lance Seidman, Backend Engineer at Atomic Form discussed these security challenges, emphasizing the need for human oversight to protect AI systems.

One example highlighted was malicious AI models on Hugging Face, which exploited the Python pickle module to execute arbitrary commands on users' machines. To mitigate such risks, Hugging Face implemented security scanners to check every file for security threats. However, human vigilance remains crucial in identifying and addressing potential exploits.

Seidman also stressed the importance of technical safeguards and a culture of security awareness within the AI community. Developers should prioritize security throughout the development life cycle to stay ahead of evolving threats. Overall, the message is clear: while AI offers remarkable capabilities, it requires careful management and oversight to prevent misuse and protect against security breaches.

Learn more from The New Stack about AI and security:  

Artificial Intelligence: Stopping the Big Unknown in Application, Data Security 

Cyberattacks, AI and Multicloud Hit Cybersecurity in 2023

Will Generative AI Kill DevSecOps? 

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