In the AI world, a vulnerability called "prompt injection" has haunted developers since chatbots went mainstream in 2022. Despite numerous attempts to solve this fundamental vulnerability—the digital equivalent of whispering secret instructions to override a system's intended behavior—no one has found a reliable solution. Until now, perhaps.

Google DeepMind has unveiled CaMeL (CApabilities for MachinE Learning), a new approach to stopping prompt-injection attacks that abandons the failed strategy of having AI models police themselves. Instead, CaMeL treats language models as fundamentally untrusted components within a secure software framework, creating clear boundaries between user commands and potentially malicious content.

Prompt injection has created a significant barrier to building trustworthy AI assistants, which may be why general-purpose big tech AI like Apple's Siri doesn't currently work like ChatGPT. As AI agents get integrated into email, calendar, banking, and document-editing processes, the consequences of prompt injection have shifted from hypothetical to existential. When agents can send emails, move money, or schedule appointments, a misinterpreted string isn't just an error—it's a dangerous exploit.

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In the AI world, a vulnerability called "prompt injection" has haunted developers since chatbots went mainstream in 2022. Despite numerous attempts to solve this fundamental vulnerability—the digital equivalent of whispering secret instructions to override a system's intended behavior—no one has found a reliable solution. Until now, perhaps.

Google DeepMind has unveiled CaMeL (CApabilities for MachinE Learning), a new approach to stopping prompt-injection attacks that abandons the failed strategy of having AI models police themselves. Instead, CaMeL treats language models as fundamentally untrusted components within a secure software framework, creating clear boundaries between user commands and potentially malicious content.

Prompt injection has created a significant barrier to building trustworthy AI assistants, which may be why general-purpose big tech AI like Apple's Siri doesn't currently work like ChatGPT. As AI agents get integrated into email, calendar, banking, and document-editing processes, the consequences of prompt injection have shifted from hypothetical to existential. When agents can send emails, move money, or schedule appointments, a misinterpreted string isn't just an error—it's a dangerous exploit.

Read full article

Comments

In the AI world, a vulnerability called "prompt injection" has haunted developers since chatbots went mainstream in 2022. Despite numerous attempts to solve this fundamental vulnerability—the digital equivalent of whispering secret instructions to override a system's intended behavior—no one has found a reliable solution. Until now, perhaps.

Google DeepMind has unveiled CaMeL (CApabilities for MachinE Learning), a new approach to stopping prompt-injection attacks that abandons the failed strategy of having AI models police themselves. Instead, CaMeL treats language models as fundamentally untrusted components within a secure software framework, creating clear boundaries between user commands and potentially malicious content.

Prompt injection has created a significant barrier to building trustworthy AI assistants, which may be why general-purpose big tech AI like Apple's Siri doesn't currently work like ChatGPT. As AI agents get integrated into email, calendar, banking, and document-editing processes, the consequences of prompt injection have shifted from hypothetical to existential. When agents can send emails, move money, or schedule appointments, a misinterpreted string isn't just an error—it's a dangerous exploit.

Read full article

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There is a signal, born in the earliest days of the cosmos. It’s weak. It’s faint. It can barely register on even the most sensitive of instruments. But it contains a wealth of information about the formation of the first stars, the first galaxies, and the mysteries of the origins of the largest structures in the Universe.

Despite decades of searching for this signal, astronomers have yet to find it. The problem is that our Earth is too noisy, making it nearly impossible to capture this whisper. The solution is to go to the far side of the Moon, using its bulk to shield our sensitive instruments from the cacophony of our planet.

Building telescopes on the far side of the Moon would be the greatest astronomical challenge ever considered by humanity. And it would be worth it.

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There is a signal, born in the earliest days of the cosmos. It’s weak. It’s faint. It can barely register on even the most sensitive of instruments. But it contains a wealth of information about the formation of the first stars, the first galaxies, and the mysteries of the origins of the largest structures in the Universe.

Despite decades of searching for this signal, astronomers have yet to find it. The problem is that our Earth is too noisy, making it nearly impossible to capture this whisper. The solution is to go to the far side of the Moon, using its bulk to shield our sensitive instruments from the cacophony of our planet.

Building telescopes on the far side of the Moon would be the greatest astronomical challenge ever considered by humanity. And it would be worth it.

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Comments

There is a signal, born in the earliest days of the cosmos. It’s weak. It’s faint. It can barely register on even the most sensitive of instruments. But it contains a wealth of information about the formation of the first stars, the first galaxies, and the mysteries of the origins of the largest structures in the Universe.

Despite decades of searching for this signal, astronomers have yet to find it. The problem is that our Earth is too noisy, making it nearly impossible to capture this whisper. The solution is to go to the far side of the Moon, using its bulk to shield our sensitive instruments from the cacophony of our planet.

Building telescopes on the far side of the Moon would be the greatest astronomical challenge ever considered by humanity. And it would be worth it.

Read full article

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The rate of autism in a group of 8-year-olds in the US rose from 2.76 percent (1 in 36) in 2020 to 3.22 percent (1 in 31) in 2022, according to a study out Tuesday in the Morbidity and Mortality Weekly Report , a journal published by the Centers for Disease Control and Prevention.

The report's authors—researchers at the CDC and academic institutions across the country— suggest that the slight uptick is likely due to improved access to evaluations in underserved groups, including Black, Hispanic, and low-income communities.

The data comes from the CDC-funded Autism and Developmental Disabilities Monitoring (ADDM) Network. The national network has been tracking the prevalence of autism spectrum disorder (ASD) in 8-year-olds at a handful of sites since 2000, publishing estimates every two years. In 2000, ASD prevalence was 1 in 150, with white children from high-income communities having the highest rates of the developmental disability. In 2020, when the rate hit 1 in 36, it was the first year in which higher ASD rates were seen in underserved communities. That year, researchers also noted that the link between ASD and socioeconomic status evaporated in most of the network.

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The rate of autism in a group of 8-year-olds in the US rose from 2.76 percent (1 in 36) in 2020 to 3.22 percent (1 in 31) in 2022, according to a study out Tuesday in the Morbidity and Mortality Weekly Report , a journal published by the Centers for Disease Control and Prevention.

The report's authors—researchers at the CDC and academic institutions across the country— suggest that the slight uptick is likely due to improved access to evaluations in underserved groups, including Black, Hispanic, and low-income communities.

The data comes from the CDC-funded Autism and Developmental Disabilities Monitoring (ADDM) Network. The national network has been tracking the prevalence of autism spectrum disorder (ASD) in 8-year-olds at a handful of sites since 2000, publishing estimates every two years. In 2000, ASD prevalence was 1 in 150, with white children from high-income communities having the highest rates of the developmental disability. In 2020, when the rate hit 1 in 36, it was the first year in which higher ASD rates were seen in underserved communities. That year, researchers also noted that the link between ASD and socioeconomic status evaporated in most of the network.

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The rate of autism in a group of 8-year-olds in the US rose from 2.76 percent (1 in 36) in 2020 to 3.22 percent (1 in 31) in 2022, according to a study out Tuesday in the Morbidity and Mortality Weekly Report , a journal published by the Centers for Disease Control and Prevention.

The report's authors—researchers at the CDC and academic institutions across the country— suggest that the slight uptick is likely due to improved access to evaluations in underserved groups, including Black, Hispanic, and low-income communities.

The data comes from the CDC-funded Autism and Developmental Disabilities Monitoring (ADDM) Network. The national network has been tracking the prevalence of autism spectrum disorder (ASD) in 8-year-olds at a handful of sites since 2000, publishing estimates every two years. In 2000, ASD prevalence was 1 in 150, with white children from high-income communities having the highest rates of the developmental disability. In 2020, when the rate hit 1 in 36, it was the first year in which higher ASD rates were seen in underserved communities. That year, researchers also noted that the link between ASD and socioeconomic status evaporated in most of the network.

Read full article

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