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AI is accelerating cyberattacks. Is your network prepared? BleepingComputerSponsored by Corelight

AI-driven attacks now automate reconnaissance, generate malware variants, and evade detection at a speed that overwhelms traditional defenses. Corelight explains how network detection and response (NDR) provides the visibility and behavioral insights SOC teams need to spot and stop these fast-moving threats. […] Read More 

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NANOREMOTE Malware Uses Google Drive API for Hidden Control on Windows Systems The Hacker Newsinfo@thehackernews.com (The Hacker News)

Cybersecurity researchers have disclosed details of a new fully-featured Windows backdoor called NANOREMOTE that uses the Google Drive API for command-and-control (C2) purposes. According to a report from Elastic Security Labs, the malware shares code similarities with another implant codenamed FINALDRAFT (aka Squidoor) that employs Microsoft Graph API for C2. FINALDRAFT is attributed to a Read […]

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OpenAI Introduces GPT 5.2: A Long Context Workhorse For Agents, Coding And Knowledge Work MarkTechPost

OpenAI Introduces GPT 5.2: A Long Context Workhorse For Agents, Coding And Knowledge WorkMarkTechPost OpenAI has just introduced GPT-5.2, its most advanced frontier model for professional work and long running agents, and is rolling it out across ChatGPT and the API. GPT-5.2 is a family of three variants. In ChatGPT, users see ChatGPT-5.2 Instant, Thinking and Pro. In the API, the corresponding models are gpt-5.2-chat-latest, gpt-5.2, and gpt-5.2-pro. Instant
The post OpenAI Introduces GPT 5.2: A Long Context Workhorse For Agents, Coding And Knowledge Work appeared first on MarkTechPost.

 OpenAI has just introduced GPT-5.2, its most advanced frontier model for professional work and long running agents, and is rolling it out across ChatGPT and the API. GPT-5.2 is a family of three variants. In ChatGPT, users see ChatGPT-5.2 Instant, Thinking and Pro. In the API, the corresponding models are gpt-5.2-chat-latest, gpt-5.2, and gpt-5.2-pro. Instant
The post OpenAI Introduces GPT 5.2: A Long Context Workhorse For Agents, Coding And Knowledge Work appeared first on MarkTechPost. Read More  

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CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent Hook MarkTechPost

CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent Hook MarkTechPost

CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent HookMarkTechPost Agent frameworks are now good at reasoning and tools, but most teams still write custom code to turn agent graphs into robust user interfaces with shared state, streaming output and interrupts. CopilotKit targets this last mile. It is an open source framework for building AI copilots and in-app agents directly in your app, with real
The post CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent Hook appeared first on MarkTechPost.

 Agent frameworks are now good at reasoning and tools, but most teams still write custom code to turn agent graphs into robust user interfaces with shared state, streaming output and interrupts. CopilotKit targets this last mile. It is an open source framework for building AI copilots and in-app agents directly in your app, with real
The post CopilotKit v1.50 Brings AG-UI Agents Directly Into Your App With the New useAgent Hook appeared first on MarkTechPost. Read More  

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Grounding the Ungrounded: A Spectral-Graph Framework for Quantifying Hallucinations in Multimodal LLMs AI updates on arXiv.org

Grounding the Ungrounded: A Spectral-Graph Framework for Quantifying Hallucinations in Multimodal LLMscs.AI updates on arXiv.org arXiv:2508.19366v4 Announce Type: replace-cross
Abstract: Hallucinations in LLMs–especially in multimodal settings–undermine reliability. We present a rigorous information-geometric framework, grounded in diffusion dynamics, to quantify hallucinations in MLLMs where model outputs are embedded via spectral decompositions of multimodal graph Laplacians, and their gaps to a truth manifold define a semantic distortion metric. We derive Courant-Fischer bounds on a temperature-dependent hallucination profile and use RKHS eigenmodes to obtain modality-aware, interpretable measures that track evolution over prompts and time. This reframes hallucination as quantifiable and bounded, providing a principled basis for evaluation and mitigation.

 arXiv:2508.19366v4 Announce Type: replace-cross
Abstract: Hallucinations in LLMs–especially in multimodal settings–undermine reliability. We present a rigorous information-geometric framework, grounded in diffusion dynamics, to quantify hallucinations in MLLMs where model outputs are embedded via spectral decompositions of multimodal graph Laplacians, and their gaps to a truth manifold define a semantic distortion metric. We derive Courant-Fischer bounds on a temperature-dependent hallucination profile and use RKHS eigenmodes to obtain modality-aware, interpretable measures that track evolution over prompts and time. This reframes hallucination as quantifiable and bounded, providing a principled basis for evaluation and mitigation. Read More