Not sure it's practical, but it's certainly striking.

Interesting research: "Stealing Reasoning Traces from Proprietary LLM APIs": Abstract: Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different sessions, users, and models within a provider's ecosystem.

YouTube video by The PrimeTime

Last year, we published our rubric for what comprehensive and protective location privacy laws should look like, outlining the baseline standards states should meet to shield individuals from pervasiv...

Nathan E. Sanders and I are writing a series of essays on real-world examples of democratic technologies for The Renovator. I haven't been posting the full text on the blog because they're a bit long, but here are links. Part 1 is about the Japanese digital democracy party, Team Mirai. Part 2 is about the Swiss Public AI model, Apertus. Part 3 is about the civic technologists of Open Knowledge Brazil. And the new one, Part 4, is about civic AI in Scotland.

This seems to work: Researchers from Tracebit on Monday said they found that placing prompt injections alongside passwords, cryptographic keys, and other secrets stored on Amazon Web Services was often all that was needed to shut down attacks from AI hacking agents. The prompts direct the attacking LLM to perform an action forbidden by its guardrails, the safety barriers AI developers erect to prevent it from taking harmful actions.

Through data brokers, ICE is buying the information you provided to open a credit card.

This essay originally appeared in The Guardian. I teach public policy at the Harvard Kennedy School and the Munk School at the University of Toronto. And it will come as no surprise to you that my students regularly use AI to complete their writing assignments. Doing so is a waste of their tuition money. But if their entire career is going to include AI writing assistants, why shouldn't they embrace their future?

Open source maintainers are rejecting AI-generated PRs. Why code got cheap while review got expensive, and whether banning contributions actually helps.

There's new benchmark measuring AI's ability to perform mathematical cryptanalysis. Anthropic's frontier model actually found new attacks. The benchmark: "CryptanalysisBench: Can LLMs do Cryptanalysis?" The idea is to benchmark the ability of LLMs to discover new mathematical cryptanalytic attacks against a series of historical algorithms. Abstract: Cryptanalysis -- the task of finding attacks against cryptographic schemes -- its at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced fastest.