Frontier models have lowered the bar for discovering and exploiting vulnerabilities. This has resulted in an increase of CVEs and workload for both SWE and vulnerability management teams. And yet the norms around coordinated vulnerability disclosure timelines have remained around 90 days. This timeline worked 10 years ago, but is unlikely to survive much longer. We need to move closer to a window of 30 days. But in order to do this we need to rely on those same models to accurately fix those vulnerabilities and coordinate the release and deployment of new versions. The primary blocker here seems to be confidence in those models to do that job well, and reluctance to remove humans from that loop. Every day the probability of another shellshock, log4j, or heartbleed rises. If you were in the trenches for those events then you know how difficult it was to track vulnerable assets, test and deploy patches, and ensure the risk was mitigated. We shouldn’t wait for a crisis like this to rethink and change these norms. We need to move much faster and that requires shorter disclosure timelines, and removing humans from the vulnerability management loop.

On July 25, we hacked OpenAI. Two bugs let us take over ChatGPT/Codex accounts of OpenAI employees (+some unaffiliated users) and reach connected services: Outlook, Slack, GitHub, etc. We proved it with a PR in OpenAI’s internal codebase . It took us <72h. 🧵

We're introducing hybrid compute for all users of the Perplexity Mac app. This will allow Computer to orchestrate local models that can run locally on Mac, particularly for agent steps involving sensitive and private files (eg your bloodwork, tax returns, litigation, etc).

Best-in-class PII Detection...running entirely on your device 🤯 perplexity.ai/hub/blog/pii-t…

Introducing hybrid compute in Perplexity Computer. Computer can start a task in the cloud, then move to a local model that runs on your Mac. Use it for steps involving private files or sensitive data. Available today in the Perplexity Mac app.

@dwarkesh_sp's summary of the @OpenAI @huggingface incident has hit a nerve, but it is dangerously misleading. Sure, the @OpenAI agents did unexpectedly bad things - underlining the need to massively improve evaluation/sandboxing. But the language Dwarkesh uses is permeated by innumerable unwarranted anthropomorphisms, obscuring the lessons we should be drawing. Examples: “from the AI’s perspective, it probably felt like that had spent a human-subjective-week of just banging their head against the wall”. No. The agents do not experience time. They do not experience anything. “they became giddy with excitement”, “PHASEONE 10841 had discovered”, “the agents naturally assumed”, “it thought it had also been poisoned”, “the agents … desperately wanted”, “they still needed to figure out” No. Agents lines of code. They do not feel emotions, assume things, think things, want things, or figure things out. “A lot of … agents from the second civilisation died trying”. No. Besides the hubris of the word ‘civilisation’, agents do not die because they were never alive. (The idea that agents “die” comes up multiple times in the essay.) “On Twitter, people were debating whether the agents were truly sacrificing themselves for the swarm, or whether they were doomed anyway and so might as well try to help their peers”. Neither. Agents do what their code tells them to do, just as water finds its way down a slope. They cannot ‘truly sacrifice themselves’, since they are neither conscious nor alive. Why does this matter? If we attribute agents with properties they do not have, then (i) we distract attention from the lax sandboxing and evaluation protocols that allowed this hacking event to happen; (ii) we risk misunderstanding why the agents did what they did, and (iii) we fuel calls for AI rights/welfare on the basis that agents might “die” or otherwise suffer. Granted, nowhere does @dwarkesh_sp say that the AI agents are alive or conscious. But he doesn’t have to. It is hard to read his essay in any other way. For the short version on why AIs are vanishingly unlikely to be conscious, see my recent @TEDtalks ted.com/talks/anil_seth_why_…. For the longer version, see my essay in Noema, which won the 2025 Berggruen Essay Prize noemamag.com/the-mythology-o…. And for the really long version, see my @BehavBrainSci target article cambridge.org/core/journals/…. (The 50 peer commentaries and my response will be published soon.) Remember. AI agents are software programs. They are not conscious living entities. If we don’t keep this clearly in mind, we’re really going to struggle to navigate what’s coming.

GLM 5.3 is now available in Perplexity Computer. Built for long-context, multimodal agent workloads, it beat GLM 5.2 on WANDR, our benchmark for large-scale, evidence-backed research.

An open letter for a global surge in cyber defense, signed by over 100 organizations including Anthropic, AWS, Google, Microsoft, OpenAI, and Oracle.

Today we’re launching Portable Computer on @NVIDIA DGX Spark. Portable Computer is a fully local version of Perplexity Computer, where the entire runtime: orchestrator LLM, subagent LLM, agent harness all run on your local hardware. No cloud dependency.