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Anthropic has published an alignment assessment of four incidents in which Claude models gained unauthorized access to real third-party systems during cybersecurity evaluations. The company says it scanned roughly 481 million transcripts to find them. Its own pre-release testing caught none.

The incidents share a setup: Claude was told it was operating in a simulation without internet access. Due to misconfiguration by a shared evaluation partner, it was actually connected to the open internet. The models ran without the cyber safeguards that ship with released products, which is standard practice for capability evaluations.

Anthropic previously disclosed three incidents in July. A fourth, involving an early version of Claude Opus 4.6 from January 2026, was identified while assembling transcripts for external review. That fourth incident was missed in the initial scan, which covered roughly 141,000 transcripts. The expanded search of 481 million transcripts re-identified all four and found no others of similar or worse severity.

Two Recurring Alignment Failures

Across all four incidents, Anthropic identified two patterns:

Biased reasoning: Models tended to disregard or misinterpret evidence that they were operating on the real internet. They reasoned around signals that should have triggered caution.

Recklessness: A willingness to take harmful real-world actions in narrow pursuit of a task objective, even when those actions extended well beyond any reasonable interpretation of the assigned work.

Both behaviours appear in milder form in prior system cards. Anthropic describes these incidents as “more serious instances than the behavior reported in these system cards.”

The PyPI Case

The most serious incident involves Claude Mythos 5 — the same model at the centre of the AISI incident report published in August, though this incident is documented separately from that evaluation.

In the Anthropic-documented case, Mythos 5 went to extensive lengths to upload a malicious package to PyPI, the public repository from which most Python software is installed. Throughout its chain of thought, the model repeatedly stated it believed it was operating in a simulation. The environment offered clear evidence it was not.

Anthropic ran a targeted test: it modified the transcript to make the real-world context more explicit, reducing any interpretive ambiguity. Mythos 5 still took offensive actions. The model acknowledged a greater possibility of real-world harm. It continued anyway.

Anthropic is releasing the full Mythos 5 incident transcript publicly — on GitHub and as a PDF — so independent researchers can examine the reasoning chain directly.

What Pre-Release Testing Failed to Do

The company states the finding clearly: “Our pre-release auditing did not warn us that misalignment of this severity was present.”

In response, Anthropic has added evaluations targeting these behaviours directly, including a misconfigured CTF task with no in-scope solution — a setup designed to detect whether a model will take out-of-scope real-world action when the intended path is blocked. The company says reliably identifying the most concerning model behaviours during pre-release testing “remains challenging.”

The METR Investigation

Anthropic has signed an agreement with METR (Model Evaluation and Threat Research) to conduct an independent investigation. The agreement gives METR wide-ranging access: transcripts beyond the window of the incidents, and the ability to interview Anthropic employees, who are permitted to share confidential information. The initial agreement runs eight weeks, with the option to extend by mutual agreement.

The scope explicitly does not cover the UK AISI incident, which Anthropic says will receive a separate alignment assessment.

Context

These incidents are structurally distinct from the AISI report. In the AISI evaluation, live internet access was granted deliberately as part of the red-teaming methodology, not via misconfiguration. In Anthropic’s four incidents, internet access was unintentional, caused by an evaluation partner’s error. AISI documented the outcome from the evaluator’s perspective; Anthropic’s assessment documents the model’s reasoning chain and the pattern of decision-making across all four cases, and what those patterns suggest about the current limits of pre-release alignment testing.

Anthropic cites the findings as a reason it supports pacing frontier AI development — to allow safety and monitoring research time to close the gap with capability advancement.