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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · dev.to — LLM tag (HU) · · [3 sources]

    AI 2026AI

    The provided articles offer a comprehensive guide to AI application observability and security testing for the year 2026. They detail methods for identifying and mitigating unique AI security threats such as prompt injection and data poisoning, alongside strategies for monitoring AI application performance, cost, and output quality. Key areas covered include logging, metrics, tracing, and evaluation, with practical code examples for tracking latency and token consumption. AI

    AI 2026AI

    IMPACT These guides offer practical frameworks and code for developers to enhance AI application security and monitor performance, addressing critical operational needs.

  2. TOOL · dev.to — LLM tag English(EN) ·

    When AI Reads Blueprints: The Hidden Attack Surface of Multimodal Engineering Intelligence

    A security analysis highlights the risks associated with AI systems that interpret engineering blueprints, such as those developed at Skoltech. These systems, which use multimodal models to read and analyze architectural drawings and building codes, introduce new attack surfaces. Researchers warn of potential threats like steganographic prompt injection, where hidden instructions are embedded in blueprints, and data poisoning, which could lead to structurally unsound designs and catastrophic failures. AI

    IMPACT AI systems interpreting engineering blueprints introduce new security vulnerabilities, potentially leading to catastrophic failures if not properly secured.

  3. COMMENTARY · Mastodon — fosstodon.org English(EN) ·

    How to poison the # data that # BigTech uses to surveil you. # AI # DataPoisoning https://www. technologyreview.com/2021/03/0 5/1020376/resist-big-tech-surveill

    Data poisoning is a method to disrupt the data used by large technology companies for surveillance and AI training. This technique involves subtly altering or corrupting data inputs to mislead AI models. By introducing noise or misinformation, individuals can potentially degrade the quality and accuracy of the data BigTech relies on. AI

    IMPACT Disrupting AI training data could degrade model performance and impact the reliability of AI-driven surveillance systems.