Researchers built an inexpensive device that circumvents chipmakers’ confidential computing protections and reveals weaknesses in scalable memory encryption. Read More
Year-end budgeting is the perfect time to close real security gaps by strengthening identity controls, reducing redundant tools, and investing in outcome-driven engagements. The article highlights how targeting credential risks and documenting results helps teams maximize spend and justify next year’s budget. […] Read More
Thousands of credentials, authentication keys, and configuration data impacting organizations in sensitive sectors have been sitting in publicly accessible JSON snippets submitted to the JSONFormatter and CodeBeautify online tools that format and structure code. […] Read More
A new ClickFix variant ratchets up the psychological pressure to 100 and addresses some technical mitigations to classic ClickFix attacks. Read More
e-Conomy SEA 2025: Malaysia takes 32% of regional AI fundingAI News Malaysia has captured 32% of Southeast Asia’s total AI funding – equivalent to US$759 million – between H2 2024 and H1 2025, establishing itself as the region’s dominant destination for artificial intelligence investment as massive infrastructure expansion and high consumer adoption converge to reshape the country’s technology landscape, according to the e-Conomy SEA 2025 report
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Malaysia has captured 32% of Southeast Asia’s total AI funding – equivalent to US$759 million – between H2 2024 and H1 2025, establishing itself as the region’s dominant destination for artificial intelligence investment as massive infrastructure expansion and high consumer adoption converge to reshape the country’s technology landscape, according to the e-Conomy SEA 2025 report
The post e-Conomy SEA 2025: Malaysia takes 32% of regional AI funding appeared first on AI News. Read More
The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Monday issued an alert warning of bad actors actively leveraging commercial spyware and remote access trojans (RATs) to target users of mobile messaging applications. “These cyber actors use sophisticated targeting and social engineering techniques to deliver spyware and gain unauthorized access to a victim’s messaging app, Read […]
Cybersecurity researchers have disclosed details of a new campaign that has leveraged Blender Foundation files to deliver an information stealer known as StealC V2. “This ongoing operation, active for at least six months, involves implanting malicious .blend files on platforms like CGTrader,” Morphisec researcher Shmuel Uzan said in a report shared with The Hacker News. […]
Dartmouth College has disclosed a data breach after the Clop extortion gang leaked data allegedly stolen from the school’s Oracle E-Business Suite servers on its dark web leak site. […] Read More
SGDFuse: SAM-Guided Diffusion for High-Fidelity Infrared and Visible Image Fusioncs.AI updates on arXiv.org arXiv:2508.05264v4 Announce Type: replace-cross
Abstract: Infrared and visible image fusion (IVIF) aims to combine the thermal radiation information from infrared images with the rich texture details from visible images to enhance perceptual capabilities for downstream visual tasks. However, existing methods often fail to preserve key targets due to a lack of deep semantic understanding of the scene, while the fusion process itself can also introduce artifacts and detail loss, severely compromising both image quality and task performance. To address these issues, this paper proposes SGDFuse, a conditional diffusion model guided by the Segment Anything Model (SAM), to achieve high-fidelity and semantically-aware image fusion. The core of our method is to utilize high-quality semantic masks generated by SAM as explicit priors to guide the optimization of the fusion process via a conditional diffusion model. Specifically, the framework operates in a two-stage process: it first performs a preliminary fusion of multi-modal features, and then utilizes the semantic masks from SAM jointly with the preliminary fused image as a condition to drive the diffusion model’s coarse-to-fine denoising generation. This ensures the fusion process not only has explicit semantic directionality but also guarantees the high fidelity of the final result. Extensive experiments demonstrate that SGDFuse achieves state-of-the-art performance in both subjective and objective evaluations, as well as in its adaptability to downstream tasks, providing a powerful solution to the core challenges in image fusion. The code of SGDFuse is available at https://github.com/boshizhang123/SGDFuse.
arXiv:2508.05264v4 Announce Type: replace-cross
Abstract: Infrared and visible image fusion (IVIF) aims to combine the thermal radiation information from infrared images with the rich texture details from visible images to enhance perceptual capabilities for downstream visual tasks. However, existing methods often fail to preserve key targets due to a lack of deep semantic understanding of the scene, while the fusion process itself can also introduce artifacts and detail loss, severely compromising both image quality and task performance. To address these issues, this paper proposes SGDFuse, a conditional diffusion model guided by the Segment Anything Model (SAM), to achieve high-fidelity and semantically-aware image fusion. The core of our method is to utilize high-quality semantic masks generated by SAM as explicit priors to guide the optimization of the fusion process via a conditional diffusion model. Specifically, the framework operates in a two-stage process: it first performs a preliminary fusion of multi-modal features, and then utilizes the semantic masks from SAM jointly with the preliminary fused image as a condition to drive the diffusion model’s coarse-to-fine denoising generation. This ensures the fusion process not only has explicit semantic directionality but also guarantees the high fidelity of the final result. Extensive experiments demonstrate that SGDFuse achieves state-of-the-art performance in both subjective and objective evaluations, as well as in its adaptability to downstream tasks, providing a powerful solution to the core challenges in image fusion. The code of SGDFuse is available at https://github.com/boshizhang123/SGDFuse. Read More
BioDisco: Multi-agent hypothesis generation with dual-mode evidence, iterative feedback and temporal evaluationcs.AI updates on arXiv.org arXiv:2508.01285v2 Announce Type: replace
Abstract: Identifying novel hypotheses is essential to scientific research, yet this process risks being overwhelmed by the sheer volume and complexity of available information. Existing automated methods often struggle to generate novel and evidence-grounded hypotheses, lack robust iterative refinement and rarely undergo rigorous temporal evaluation for future discovery potential. To address this, we propose BioDisco, a multi-agent framework that draws upon language model-based reasoning and a dual-mode evidence system (biomedical knowledge graphs and automated literature retrieval) for grounded novelty, integrates an internal scoring and feedback loop for iterative refinement, and validates performance through pioneering temporal and human evaluations and a Bradley-Terry paired comparison model to provide statistically-grounded assessment. Our evaluations demonstrate superior novelty and significance over ablated configurations and generalist biomedical agents. Designed for flexibility and modularity, BioDisco allows seamless integration of custom language models or knowledge graphs, and can be run with just a few lines of code.
arXiv:2508.01285v2 Announce Type: replace
Abstract: Identifying novel hypotheses is essential to scientific research, yet this process risks being overwhelmed by the sheer volume and complexity of available information. Existing automated methods often struggle to generate novel and evidence-grounded hypotheses, lack robust iterative refinement and rarely undergo rigorous temporal evaluation for future discovery potential. To address this, we propose BioDisco, a multi-agent framework that draws upon language model-based reasoning and a dual-mode evidence system (biomedical knowledge graphs and automated literature retrieval) for grounded novelty, integrates an internal scoring and feedback loop for iterative refinement, and validates performance through pioneering temporal and human evaluations and a Bradley-Terry paired comparison model to provide statistically-grounded assessment. Our evaluations demonstrate superior novelty and significance over ablated configurations and generalist biomedical agents. Designed for flexibility and modularity, BioDisco allows seamless integration of custom language models or knowledge graphs, and can be run with just a few lines of code. Read More
