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first_img The Dark Side of the Moon open-source Kimi K3.1 Pro tops the open-weight model rankings

According to a report by Phoenix Technology on November 5, the artificial intelligence company Moonshot AI announced that it has released the Kimi K3.1 Pro model weights on the Hugging Face platform. The AI model evaluation platform Arena revealed that Kimi K3.1 Pro (Max) ranks first among open-weight models in the Agent Arena intelligent agent leaderboard and 13th globally among all evaluated models, also ranking first among open models in work, programming, and chat categories.Arena stated that this ranking is based on over 17,500 real, long-term intelligent agent task conversations from global users. The net improvement rate of Kimi K3.1 Pro (Max) is +5.1%, an increase of 4.2 percentage points compared to the previous generation Kimi K3 (Max) at +0.9%. The Shell command execution reliability metric, Bash Reliability, is +7.9%, ranking first among open models, while the previous generation K3 was -4.5%, ranking 13th.The praise and complaint metric is +7.7%, compared to +1.3% for the previous generation; the controllability metric is +6.5%, also ranking first among open models. The task completion confirmation metric is +3.9%, ranking fourth among open models, slightly higher than the previous generation's +3.8%. Moonshot AI's data shows that this model uses a mixture of experts architecture, with a total parameter count of 2.8 trillion, approximately 104 billion parameters activated per inference, supporting around 1 million tokens of context, and possessing native multimodal capabilities, focusing on enhancing programming, 3D creation, financial research, factual accuracy, and the execution capabilities of complex intelligent agent tasks.

OpenAI reveals autonomous vulnerability pipeline: a run that produced 948 local privilege escalation vulnerabilities in Linux

According to disclosures from OpenAI's official website, the company has established an autonomous security research pipeline covering vulnerability discovery, dynamic verification, exploitability assessment, and patch generation. In a run centered on an upcoming network model, the pipeline completed scanning and verification of targets related to the Linux kernel, generating 948 exploits that can achieve local privilege escalation (LPE) on commonly used Linux distributions, with an API cost of approximately $1.213 million, or about $1,279 per LPE exploit.It was reported that the pipeline identified and analyzed a total of 2,308 candidate discoveries, dynamically reproducing 1,724 vulnerabilities, and producing 291 information leakage proofs of concept (PoC); the target virtual machine ran a standard desktop system with complete protection. The Scout agent initially identified about 5,034 audit targets (approximately 2.8 million lines of code, accounting for about 7% of the kernel), and the Analyst agent produced candidate discoveries in about 12 hours. OpenAI stated that it has prioritized reporting critical vulnerability patches as recommended by the Linux community and continues to advance the remaining reports in collaboration with Patch the Planet partners.OpenAI also mentioned that in the adapted kernelCTF environment, an additional 25 information leakage PoCs and 21 LPEs were obtained; among 166 sampled exploits, the agent reportedly used at least 10 newer Linux exploitation techniques that it deemed more recent. The company emphasized that maintainers are quick to fix: over 100 LPEs have been patched or entered temporary patches within about 10 days of scanning, and approximately 300 others have entered public discussion.
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