The company tested 123 cases representing 29 different attack scenarios and found a 23.6 percent attack success rate when browser use operated without safety mitigations. One example involved a malicious email that instructed Claude to delete a user’s emails for “mailbox hygiene” purposes. Without safeguards, Claude followed these instructions and deleted the user’s emails without… Read More »
Seeking the system prompt Owing to the unknown contents of the data used to train Grok 4 and the random elements thrown into large language model (LLM) outputs to make them seem more expressive, divining the reasons for particular LLM behavior for someone without insider access can be frustrating. But we can use what we… Read More »
For those who missed the Flash era, these in-browser apps feel somewhat like the vintage apps that defined a generation of Internet culture from the late 1990s through the 2000s when it first became possible to create complex in-browser experiences. Adobe Flash (originally Macromedia Flash) began as animation software for designers but quickly became the… Read More »
To understand CaMeL, you need to understand that prompt injections happen when AI systems can’t distinguish between legitimate user commands and malicious instructions hidden in content they’re processing. Willison often says that the “original sin” of LLMs is that trusted prompts from the user and untrusted text from emails, webpages, or other sources are concatenated… Read More »
Meta constructed the Llama 4 models using a mixture-of-experts (MoE) architecture, which is one way around the limitations of running huge AI models. Think of MoE like having a large team of specialized workers; instead of everyone working on every task, only the relevant specialists activate for a specific job. For example, Llama 4 Maverick… Read More »
Caution over citations and sources Claude users should be warned that large language models (LLMs) like those that power Claude are notorious for sneaking in plausible-sounding confabulated sources. A recent survey of citation accuracy by LLM-based web search assistants showed a 60 percent error rate. That particular study did not include Anthropic’s new search feature… Read More »
“The biggest [drawback] is that they are probabilistic prediction machines and will get it wrong in ways that aren’t just ‘that’s the wrong word’,” Willis explains. “LLMs will sometimes skip a line in larger documents where the layout repeats itself, I’ve found, where OCR isn’t likely to do that.” AI researcher and data journalist Simon… Read More »
While Bing Chat’s unhinged nature was caused in part by how Microsoft defined the “personality” of Sydney in the system prompt (and unintended side-effects of its architecture with regard to conversation length), Ars Technica’s saga with the chatbot began when someone discovered how to reveal Sydney’s instructions via prompt injection, which Ars Technica then published.… Read More »
Video scraping is just one of many new tricks possible when the latest large language models (LLMs), such as Google’s Gemini and GPT-4o, are actually “multimodal” models, allowing audio, video, image, and text input. These models translate any multimedia input into tokens (chunks of data), which they use to make predictions about which tokens should… Read More »
reader comments 56 OpenAI truly does not want you to know what its latest AI model is “thinking.” Since the company launched its “Strawberry” AI model family last week, touting so-called reasoning abilities with o1-preview and o1-mini, OpenAI has been sending out warning emails and threats of bans to any user who tries to probe… Read More »