Microsoft has released an out-of-band cumulative update to fix a known issue causing the November 2025 KB5068966 hotpatch update to reinstall on Windows 11 systems repeatedly. […] Read More
The regime’s cyber-espionage strategy employs dual-use targeting, collecting info that can support both military needs and broader political objectives. Read More
Nvidia has confirmed that last month’s security updates are causing gaming performance issues on Windows 11 24H2 and Windows 11 25H2 systems. […] Read More
Even the most advanced systems like Cloudflare can fall victim to software issues and become a global point of failure, Dr. David Utzke argues, adding that the recent outage should be a warning for enterprises. Read More
A new security framework responds to a shift in attackers’ tactics, one that allows them to infiltrate enterprises “silently” through their own policies. Read More
Can MLLMs Detect Phishing? A Comprehensive Security Benchmark Suite Focusing on Dynamic Threats and Multimodal Evaluation in Academic Environmentscs.AI updates on arXiv.org arXiv:2511.15165v1 Announce Type: cross
Abstract: The rapid proliferation of Multimodal Large Language Models (MLLMs) has introduced unprecedented security challenges, particularly in phishing detection within academic environments. Academic institutions and researchers are high-value targets, facing dynamic, multilingual, and context-dependent threats that leverage research backgrounds, academic collaborations, and personal information to craft highly tailored attacks. Existing security benchmarks largely rely on datasets that do not incorporate specific academic background information, making them inadequate for capturing the evolving attack patterns and human-centric vulnerability factors specific to academia. To address this gap, we present AdapT-Bench, a unified methodological framework and benchmark suite for systematically evaluating MLLM defense capabilities against dynamic phishing attacks in academic settings.
arXiv:2511.15165v1 Announce Type: cross
Abstract: The rapid proliferation of Multimodal Large Language Models (MLLMs) has introduced unprecedented security challenges, particularly in phishing detection within academic environments. Academic institutions and researchers are high-value targets, facing dynamic, multilingual, and context-dependent threats that leverage research backgrounds, academic collaborations, and personal information to craft highly tailored attacks. Existing security benchmarks largely rely on datasets that do not incorporate specific academic background information, making them inadequate for capturing the evolving attack patterns and human-centric vulnerability factors specific to academia. To address this gap, we present AdapT-Bench, a unified methodological framework and benchmark suite for systematically evaluating MLLM defense capabilities against dynamic phishing attacks in academic settings. Read More
Physics-Based Benchmarking Metrics for Multimodal Synthetic Imagescs.AI updates on arXiv.org arXiv:2511.15204v1 Announce Type: cross
Abstract: Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accuracy, especially for domain-specific or context-dependent scenarios. For this, this paper proposes a Physics-Constrained Multimodal Data Evaluation (PCMDE) metric combining large language models with reasoning, knowledge based mapping and vision-language models to overcome these limitations. The architecture is comprised of three main stages: (1) feature extraction of spatial and semantic information with multimodal features through object detection and VLMs; (2) Confidence-Weighted Component Fusion for adaptive component-level validation; and (3) physics-guided reasoning using large language models for structural and relational constraints (e.g., alignment, position, consistency) enforcement.
arXiv:2511.15204v1 Announce Type: cross
Abstract: Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accuracy, especially for domain-specific or context-dependent scenarios. For this, this paper proposes a Physics-Constrained Multimodal Data Evaluation (PCMDE) metric combining large language models with reasoning, knowledge based mapping and vision-language models to overcome these limitations. The architecture is comprised of three main stages: (1) feature extraction of spatial and semantic information with multimodal features through object detection and VLMs; (2) Confidence-Weighted Component Fusion for adaptive component-level validation; and (3) physics-guided reasoning using large language models for structural and relational constraints (e.g., alignment, position, consistency) enforcement. Read More
The U.S. Securities and Exchange Commission (SEC) has abandoned its lawsuit against SolarWinds and its chief information security officer, alleging that the company had misled investors about the security practices that led to the 2020 supply chain attack. In a joint motion filed November 20, 2025, the SEC, along with SolarWinds and its CISO Timothy […]
An Implementation of Fully Traced and Evaluated Local LLM Pipeline Using Opik for Transparent, Measurable, and Reproducible AI WorkflowsMarkTechPost In this tutorial, we implement a complete workflow for building, tracing, and evaluating an LLM pipeline using Opik. We structure the system step-by-step, beginning with a lightweight model, adding prompt-based planning, creating a dataset, and finally running automated evaluations. As we move through each snippet, we see how Opik helps us track every function span,
The post An Implementation of Fully Traced and Evaluated Local LLM Pipeline Using Opik for Transparent, Measurable, and Reproducible AI Workflows appeared first on MarkTechPost.
In this tutorial, we implement a complete workflow for building, tracing, and evaluating an LLM pipeline using Opik. We structure the system step-by-step, beginning with a lightweight model, adding prompt-based planning, creating a dataset, and finally running automated evaluations. As we move through each snippet, we see how Opik helps us track every function span,
The post An Implementation of Fully Traced and Evaluated Local LLM Pipeline Using Opik for Transparent, Measurable, and Reproducible AI Workflows appeared first on MarkTechPost. Read More
ChatGPT group chats may help teams bring AI into daily planningAI News OpenAI has introduced group chats inside ChatGPT, giving people a way to bring up to 20 others into a shared conversation with the chatbot. The feature is now available to all logged-in users after a short pilot earlier this month, and it shifts ChatGPT from a mostly one-on-one tool to something that supports small-group collaboration.
The post ChatGPT group chats may help teams bring AI into daily planning appeared first on AI News.
OpenAI has introduced group chats inside ChatGPT, giving people a way to bring up to 20 others into a shared conversation with the chatbot. The feature is now available to all logged-in users after a short pilot earlier this month, and it shifts ChatGPT from a mostly one-on-one tool to something that supports small-group collaboration.
The post ChatGPT group chats may help teams bring AI into daily planning appeared first on AI News. Read More
