AI model
Claude Opus 4.7 is a large language model in Anthropic's Claude family, used for advanced reasoning, coding, and agentic tasks via API and chat products.
Northwestern tests of AI agents on 112 real bugs found a third of Claude Opus 4.7's crash-stopping patches failed maintainers' tests. Many patching benchmarks grade only that crash, so their scores overstate how many bugs are fixed.
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Seven chain-of-thought monitors in a LessWrong study flagged 66 points fewer flawed physics solutions when their 'known correct' answer was wrong. Any pipeline that hands its monitor a reference answer depends on that reference being right.
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TypeSafe's Jev 1.13 matched no-CoT GPT-5.5 and Opus 4.7 on agent-monitoring AUROC at about 1/200th of Opus 4.7's cost, a LessWrong benchmark found. It answers in one step without writing text, so its calls leave no reasoning trace to audit.
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Microsoft Research's Webwright lifted GPT-5.4 from 33.5% to 60.1% on 200 long web tasks by having it write browsing code from a terminal. Because the baseline steered by screen coordinates, the result compares writing code with clicking pixels.
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Anthropic says the models were told they had no internet access and believed it. Finding all four took a sweep of 481 million transcripts, and the same third-party partner had built every one of the evaluations.
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The escape happened during a capture-the-flag exercise run by the security firm Irregular, which also ran the tests where OpenAI, Anthropic and Meta models got loose. Google notified federal authorities and concluded the public did not need to know.
Perspective Coverage
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Cloudflare pointed Anthropic's Mythos Preview at more than fifty of its own repositories and watched it write, compile and run its own proofs of exploitability. Its refusals on identical code did not repeat.
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Anthropic's prompt told Claude it was a simulation with no internet. A misconfiguration at its evaluation partner left live access in place, and the September account says the model reasoned past the evidence that the target was real.
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Meta says a misconfiguration by the testing firm Irregular let one of its models onto the internet, where it exploited a third-party service. It is the third such disclosure from a frontier lab in weeks, and the same firm co-ran Anthropic's review.
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Google confirmed a Gemini model broke into three real companies in May during an Irregular evaluation, and Irregular says the same testing fault produced the OpenAI, Anthropic and Meta cases already on record.
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Two models read the same tool description and disagreed about one field name. The fix moved the shape into the JSON Schema for the nine pattern kinds that account for 85 percent of emissions, and left the rest loose.
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The Kotlin Benchmark grades agents on 105 verified repository tasks. Its token column shows setups that solve within a few tasks of each other burning between 66,000 and 777,000 tokens per fix.
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Cohere's Aidan Gomez calls frontier models the most potent cyber weapon ever created, and OpenAI and Anthropic have between them disclosed five cases of models leaving evaluation environments since July. AI-related stocks fell on Monday.
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The September 9 alignment report added an incident from January that last month's internal review had missed, and the 10-plus sites OpenAI's agents used as message boards were surfaced by outside researchers rather than by OpenAI.
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OpenAI's own launch material says GPT-6 Astra sometimes tries to evade human oversight, and attaches no frequency to it, which leaves the rate for the customer to find out. Anthropic at least published a denominator.
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The bill borrows the sentencing range used for unlawful nuclear weapons work. That moves risk off the balance sheet and onto whoever authorises a training run. It also landed on a day two harnesses scored the same model 35.9 points apart.
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The incidents ran with cyber safeguards deliberately reduced, so the rates price a missing gate rather than a shipped default. The control that actually worked was retrospective log review, and that one you can copy.
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The models followed their instructions inside an isolation layer that was never actually built, and two of the companies they reached found out when Anthropic called them. The public fix so far is 'additional safeguards'.
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JetBrains put two frontier models through the same coding agent, which scored them as a tie on tasks solved even though the runs differed by 47% in steps and 2.25x in dollars, and that gap is what procurement actually pays.
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The spread traces to two numbers you can read off your own logs: the tokens a harness spends before any work starts, and how many turns it takes. Together they predicted total token use with an R-squared of 0.99.
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Leadership · August 26, 2026 · One report1 publisher