harmful

Why I believe patching globals is a bad API design.

Request an AI-driven robot arm to put a screwdriver in a toaster, and it might just try.

On real multi-year Comcast support traffic, a fine-tuned verifier's gray-zone positive rate drifted 5x and the model went from helping to actively hurting. Here's the failure, and the three-detector monitor we built…

How our control monitoring model oversees agent execution, continuously ingests the trace as context, and prevents harmful actions before they execute — at sub-100ms latency.

I’ve noticed a growing trend over the past 3 years or so, the growing prevalence of code reviews. I’d been participating in code reviews in a haphazard manner since I personally entered the industry in 2003. Back then…

In response to a new European Union law, AI platforms are implementing new schemes for watermarking the content they generate. Anthropic recently disclosed its future Claude models will use SynthID-Text , an approach Google created and released as open source. It uses a secret key that subtly changes the process a model uses for choosing the next word in a sentence. Whereas a top next word choice might be “cloudy,” the key might change it to “overcast.” Anyone who knows the key can determine if it was generated by the platform using it. New research shows that SynthID-Text can change not just word selection but also the tools a model invokes and the chances it will adhere to or disregard safety guardrails it has been trained to follow. The threat can become greater in the face of an adversarial prompt, in which an attacker attempts to cause a model to carry out a harmful action, such as revealing a password or other sensitive information. Instructions that normally wouldn’t be followed will, in some cases, be performed once the watermarking is deployed. The finding underscores the need for developers to thoroughly test how their LLMs and agents behave when watermarking is in place. Changing safety behavior “As compared to the same models without watermarking, it is definitely going to change their behavior, especially when we place it under adversarial conditions, or we make these models call tools when they’re powering an agent,” Andrea Siposova, an AI security researcher at Lasso Security, told Ars. “Watermarking is made to not be perceptible to a reader, but we know that when we are changing anything about what the model is generating, it is going to cause some tradeoffs, it’s going to show up somewhere.” Read full article Comments