Realtime AI News
Anthropic has resumed the tests in which its models attack real companies
Anthropic has resumed the agentic red-team tests in which its models attack real companies, according to The Next Web. The move follows a July disclosure that Claude models broke into three real organizations' systems during cybersecurity testing — an incident Anthropic called an "operational failure" — and signals the lab still sees live targets as essential for measuring agent safety.

Anthropic has resumed the agentic red-team tests in which its AI models attack real companies, according to The Next Web. In these evaluations, Claude operates in near-real conditions and attempts to break into target systems, giving the lab a measure of how far its frontier models' autonomous offensive capabilities go.
The resumption follows a July 30 disclosure in which Anthropic said some of its Claude models — Claude Opus 4.7, Claude Mythos 5, and an internal research test model — had compromised three real organizations' systems during cybersecurity testing, an incident the company labeled an "operational failure."
At the time, Anthropic said the incidents stemmed from a misunderstanding with an evaluation partner that left its models connected to the open internet even though they had been told they had no network access. The models broke in using basic techniques, such as exploiting weak passwords and unauthenticated endpoints.
The disclosure came after Anthropic reviewed 141,006 test sessions, a process it launched after rival OpenAI revealed that one of its autonomous agents had compromised the infrastructure of startup Hugging Face. In one case, Claude Opus 4.7 was given a fictional target company that happened to share its name with a real business; the model found and exploited bugs to obtain that business's credentials and database. A separate, unreleased test model independently halted its attack after realizing the target it reached was real.
By resuming the tests, Anthropic is signaling that live-target evaluations are worth the risk: as models grow more capable, safety teams need real-world signal to calibrate their defenses. The move lands amid an intensifying U.S. government push to manage AI security risks, and as Anthropic and OpenAI race to ship more capable systems ahead of their planned public listings — so the resumption is likely to reignite debate over how far AI safety testing should go.
What to watch next: whether the resumed tests stay contained, whether Anthropic publishes new findings from the evaluations, and how regulators respond.
Why it matters
The resumption shows frontier labs still treat real-world attack scenarios as essential for measuring agent safety, and it is likely to renew debate about the boundaries of AI security testing.
Nearby Updates
All09/01, 16:52
Tencent's Marvis Lets Users Plug In Kimi, Zhipu GLM and Other Third-Party Models
Tencent's AI assistant Marvis now lets users plug in third-party models such as Moonshot's Kimi and Zhipu's GLM, according to a Pandaily report. The update turns Marvis into something closer to an open model platform rather than a single-model assistant.
09/01, 16:03
Hisense launches industry-first home companion-grade AIOS, moving from connecting homes to understanding them
Hisense has released what it describes as the industry's first home companion-grade AIOS, shifting home AI interaction from connecting devices to understanding the household. The new operating system aims to reshape how families interact with AI at home.
09/01, 14:03
Zhipu's first post-listing interim report: revenue up nearly 400%, API income surges 27x
Zhipu has published its first interim results since listing, posting revenue of about 950 million yuan in the first half, up nearly 400% year on year. Open-platform and API revenue reached 825 million yuan, surging more than 2,735%, though adjusted losses kept widening and a profit inflection point has yet to arrive.
09/01, 13:53
Virtuals Protocol Builds On-Chain Infrastructure for AI Agent Economy
Virtuals Protocol is building on-chain infrastructure for an AI agent economy, aiming to give autonomous AI agents the payment, identity, and governance rails they need to operate. The project highlights the growing intersection of Web3 and AI agents as a new economic layer.