Don't expect quick fixes in 'red-teaming' of AI models. Security was an afterthought

– White House officials and Silicon Valley companies are invested in a three-day competition at the DefCon hacker convention to uncover flaws in AI chatbots and large-language models.

– Around 3,500 competitors are participating, aiming to expose vulnerabilities in leading AI models.

– The results of the competition won't be made public until February, highlighting the extensive effort required to address the identified issues.

– Current AI models are criticized for their unwieldiness, brittleness, and susceptibility to biases due to inadequate security considerations during their training.

– Experts caution against the notion of easily applying security measures to built AI systems and emphasize the challenges of fixing these flaws.

– OpenAI's ChatGPT, Google's Bard, and other language models differ from conventional software, as they are continuously evolving through the ingestion of vast amounts of data.

– The generative AI industry has faced security challenges since the release of chatbots, with instances of researchers exposing security holes.

– The complexity of AI systems makes them vulnerable to attacks that may not be immediately clear to their creators, and chatbots are particularly susceptible due to their direct interaction with users.

– "Poisoning" a small portion of data used to train AI models can have significant disruptive effects.

– Researchers demonstrate that corrupting as little as 0.01% of a model can spoil it, highlighting the need for robust security measures.

– Concerns are raised about the potential misuse of AI systems in various domains, including search engines, social media, and personal interactions.

– The lack of response plans for data-poisoning attacks and dataset theft is a widespread issue within the industry.