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AI 安全

模型安全、資安、隱私、供應鏈、紅隊與防護。

AI 基礎設施

AtumAI Compiles Data Center Control Policies Into Searchable Specifications, Beating Human Baselines Across Three Task Categories

AtumAI converts natural-language requirements into an intermediate representation containing objectives, hard constraints, decision variables, and evaluation methods, then combines diffusion models, evolutionary algorithms, and surrogate models to search for policies. The study reports improvements in workload placement, resource scaling, and power management, but the results come from simulators and do not yet demonstrate that the system can safely take over a production control plane.
GitHub Repo

Open Secure AI Alliance Launches, with NVIDIA Advancing Agent Testing, Tracing, and Auditing Tools Through NOOA

NVIDIA, the Linux Foundation, Hugging Face, Microsoft, and other organizations have formed the Open Secure AI Alliance to address vulnerabilities in agents and AI software with open models and tools. Its first concrete contributions include NVIDIA’s NOOA framework, but the alliance has yet to announce common specifications, governance processes, or a delivery timeline.
AI 安全

OWASP Agentic Skills Top 10 Completes Public Review for v1, Treating the Skill Supply Chain as a Distinct Attack Surface

The public review of the OWASP Agentic Skills Top 10 v1 draft has concluded. The framework organizes malicious skills, excessive permissions, dependency confusion, and cross-platform portability risks into ten control categories. It treats skills as the behavioral layer between models and MCP tools and proposes a universal format for declaring permissions, provenance, and integrity.
AI 安全

Google Earth Pulled Its Image Generation Feature After One Day as SynthID Failed to Stop Fake Satellite Images From Spreading

After Google connected Nano Banana 2 to real-world geospatial imagery, users quickly generated and shared screenshots of fabricated explosions, airstrikes, and other scenes. The company withdrew the feature the next day. Although generated images carried watermarks and were not added to shared maps, the incident shows that provenance labels cannot replace input restrictions and interface isolation.
AI 安全

Five Open-Weight Models Report Nearly Constant Confidence, Making Low-Confidence-First Auditing Potentially Equivalent to Random Sampling

A new study simulates auditing agent populations under limited human-review budgets and identifies the tipping point at which distorted self-reported confidence causes ranking-based strategies to underperform random sampling. Five small and midsize open-weight models achieved confidence AUROCs of only about 0.5 on two benchmarks, although the findings are based on a single prompt and direct-answer setup.
AI 安全

DFOT Quantifies LLM Over-Trust in Derived Sensor Values; Cross-Modal Distillation Improves Correction Rates by Up to 6.69 Points

A new framework breaks down “model over-trust in derived values” into metrics covering conflict acceptance, contextual misdirection, error correction, and utility harm. In a PPG-based rhythm interpretation case study, a reliability model that uses ECG during training but requires only PPG at deployment improves four endpoints, although the data and weights were not released with the code.
AI 安全

Microsoft Routes Vulnerability Analysis Through a Security Model With 5B Active Parameters; MDASH Scores 95.95% on CyberGym

MAI-Cyber-1-Flash is a sparse MoE model with 137B total parameters, but activates only about 5B per token. It handles roughly 90% of MDASH’s workload before passing difficult cases to GPT-5.4. Microsoft says the new configuration cuts costs by nearly half, but the model is currently available only in private preview on Azure AI Foundry, and the benchmark results have yet to be independently reproduced.