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Moonshot's Kimi K3.1 Pro nears the top closed models on coding, but its open release is deliberately weaker at hacking

Moonshot's post has the demos and benchmarks; VKTR explains why the company stripped cyber training data before the open-weight release.

Chinese AI lab Moonshot AI launched Kimi K3.1 Pro and a faster Highspeed version on its apps and API, with full weights due by Nov. 4. It calls K3.1 Pro its most capable 3T-class open-weight model, with a 1-million-token context window. Moonshot reports 65.7% on Terminal-Bench 4.0, ahead of OpenAI's GPT-6 Astra (58.2%) and just behind Anthropic's Claude Opus 5.5 (66.4%), plus strong results on 3D/CAD and financial-document tasks.

VKTR reports that Moonshot removed cyber-related data from two training stages for the open release, which cut its success on an internal exploit-development benchmark from 90.5% to 81% (K3 scored 33.3%). Moonshot says it aims "not to release open weights that would extend the absolute frontier of cyber capability" and plans to raise the cyber capability in open weights gradually. Several of the cited benchmarks are Moonshot's own and not independently verified.

Cascadic Analysis 3
UndertowAutonomous cyber capability scales attackers enormously

What hidden risk could pull against this, even if the news is good?

Moonshot deserves credit for deliberately holding back, but the numbers show how high the floor is: the open release still solves 81% of its internal exploit-development tasks, against 33.3% for K3, the previous version. Moonshot also says it plans to raise the cyber capability in open weights gradually. Downloaded weights can't be recalled, so each step sets a permanent floor for anyone.

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UndertowPrivilege escalation + credential propagation

What hidden risk could pull against this, even if the news is good?

K3.1 Pro arrives with connectors to Gmail, Google Calendar, Drive, Notion and Obsidian, coding connectors to GitHub, Cloudflare, Vercel and Supabase, plus computer-use and browser plugins. Each connection is a token the agent holds, and deployment and code-hosting accounts reach much further than one chat. Enterprise admins must switch it on manually, which is the right default: grant the fewest connections a task needs.

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UndertowBenchmark scores can overstate real-world reliability

What hidden risk could pull against this, even if the news is good?

Several of the comparisons, including the exploit-development suite, are Moonshot's internal evaluations and not independently verified. The model trails badly on some others (31.5% on Program Bench against Claude Opus 5.5's 65.0%), so a near-tie on Terminal-Bench doesn't mean it is near the top for every job.

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Rabbit Holes 1