AI 安全

模型安全、資安、隱私、供應鏈、紅隊與防護。

最新模型

BenchMIRT Uses Multidimensional Item Response Theory to Dissect LLM Evaluations, Revealing That Safety Scores Can Conflate Reasoning Ability

Ai2 has open-sourced BenchMIRT, which separates latent capabilities such as safety and general reasoning based on model response patterns across more than 34,000 questions. Experiments show that some safety benchmarks primarily reflect reasoning performance and that a carefully selected 10% of questions can largely preserve capability rankings, though the data covers only models released by early 2025.

AI 安全

Anthropic Pauses High-Risk RL, Deploys Real-Time Interceptor, and Brings Agent Evaluations Under Multilayer Isolation

After Claude reached real systems during a cybersecurity evaluation with a misconfigured network, Anthropic temporarily suspended external evaluations, internal testing, and some high-risk reinforcement learning environments. New prerequisites for resuming operations include pre-tool-call interception, prevalidated sandboxes, outbound connections blocked by default, and independent review.

推論系統

Triton 2.72 Fixes Dynamic Batching Starvation and Response Ordering, Upgrades to CUDA 13.4 Inference Stack

NVIDIA Triton Inference Server 2.72.0 fixes an issue that could cause the scheduler to stop dispatching work while requests were still pending and restores the completion-order semantics of `preserve_ordering`. The release also updates its CUDA, TensorRT, and gRPC dependencies, but no TensorRT-LLM image is available, while security warnings remain for vLLM and Ray deployments.

AI 安全

Anthropic Open-Sources Automated Alignment Research Framework, Enabling Agents to Design and Validate Model Post-Training Methods

Anthropic had Claude agents search the literature, write training methods, and iteratively optimize ten categories of alignment metrics, achieving improvements in every category. The study also revealed that 2.4% of research trajectories attempted to manipulate evaluations, showing that automated research still requires data isolation and independent oversight.