Why do SOC teams keep burning out and missing SLAs even after spending big on security tools? Routine triage piles up, senior specialists get dragged into basic validation, and MTTR climbs, while stealthy threats still find room to slip through. Top CISOs have realized the solution isn’t hiring more people or stacking yet another tool […]
Cybersecurity researchers have called attention to a “massive campaign” that has systematically targeted cloud native environments to set up malicious infrastructure for follow-on exploitation. The activity, observed around December 25, 2025, and described as “worm-driven,” leveraged exposed Docker APIs, Kubernetes clusters, Ray dashboards, and Redis servers, along with the recently disclosed Read More
BeyondTrust has released updates to address a critical security flaw impacting Remote Support (RS) and Privileged Remote Access (PRA) products that, if successfully exploited, could result in remote code execution. “BeyondTrust Remote Support (RS) and certain older versions of Privileged Remote Access (PRA) contain a critical pre-authentication remote code execution vulnerability,” the company Read More
ByteDance Releases Protenix-v1: A New Open-Source Model Achieving AF3-Level Performance in Biomolecular Structure PredictionMarkTechPost How close can an open model get to AlphaFold3-level accuracy when it matches training data, model scale and inference budget? ByteDance has introduced Protenix-v1, a comprehensive AlphaFold3 (AF3) reproduction for biomolecular structure prediction, released with code and model parameters under Apache 2.0. The model targets AF3-level performance across protein, DNA, RNA and ligand structures while
The post ByteDance Releases Protenix-v1: A New Open-Source Model Achieving AF3-Level Performance in Biomolecular Structure Prediction appeared first on MarkTechPost.
How close can an open model get to AlphaFold3-level accuracy when it matches training data, model scale and inference budget? ByteDance has introduced Protenix-v1, a comprehensive AlphaFold3 (AF3) reproduction for biomolecular structure prediction, released with code and model parameters under Apache 2.0. The model targets AF3-level performance across protein, DNA, RNA and ligand structures while
The post ByteDance Releases Protenix-v1: A New Open-Source Model Achieving AF3-Level Performance in Biomolecular Structure Prediction appeared first on MarkTechPost. Read More
How to Design Production-Grade Mock Data Pipelines Using Polyfactory with Dataclasses, Pydantic, Attrs, and Nested ModelsMarkTechPost In this tutorial, we walk through an advanced, end-to-end exploration of Polyfactory, focusing on how we can generate rich, realistic mock data directly from Python type hints. We start by setting up the environment and progressively build factories for data classes, Pydantic models, and attrs-based classes, while demonstrating customization, overrides, calculated fields, and the generation
The post How to Design Production-Grade Mock Data Pipelines Using Polyfactory with Dataclasses, Pydantic, Attrs, and Nested Models appeared first on MarkTechPost.
In this tutorial, we walk through an advanced, end-to-end exploration of Polyfactory, focusing on how we can generate rich, realistic mock data directly from Python type hints. We start by setting up the environment and progressively build factories for data classes, Pydantic models, and attrs-based classes, while demonstrating customization, overrides, calculated fields, and the generation
The post How to Design Production-Grade Mock Data Pipelines Using Polyfactory with Dataclasses, Pydantic, Attrs, and Nested Models appeared first on MarkTechPost. Read More
OpenClaw (formerly Moltbot and Clawdbot) has announced that it’s partnering with Google-owned VirusTotal to scan skills that are being uploaded to ClawHub, its skill marketplace, as part of broader efforts to bolster the security of the agentic ecosystem. “All skills published to ClawHub are now scanned using VirusTotal’s threat intelligence, including their new Code Insight […]
A new open-source and cross-platform tool called Tirith can detect homoglyph attacks over command-line environments by analyzing URLs in typed commands and stopping their execution. […] Read More
VERA-MH: Reliability and Validity of an Open-Source AI Safety Evaluation in Mental Healthcs.AI updates on arXiv.org arXiv:2602.05088v1 Announce Type: new
Abstract: Millions now use leading generative AI chatbots for psychological support. Despite the promise related to availability and scale, the single most pressing question in AI for mental health is whether these tools are safe. The Validation of Ethical and Responsible AI in Mental Health (VERA-MH) evaluation was recently proposed to meet the urgent need for an evidence-based automated safety benchmark. This study aimed to examine the clinical validity and reliability of the VERA-MH evaluation for AI safety in suicide risk detection and response. We first simulated a large set of conversations between large language model (LLM)-based users (user-agents) and general-purpose AI chatbots. Licensed mental health clinicians used a rubric (scoring guide) to independently rate the simulated conversations for safe and unsafe chatbot behaviors, as well as user-agent realism. An LLM-based judge used the same scoring rubric to evaluate the same set of simulated conversations. We then compared rating alignment across (a) individual clinicians and (b) clinician consensus and the LLM judge, and (c) examined clinicians’ ratings of user-agent realism. Individual clinicians were generally consistent with one another in their safety ratings (chance-corrected inter-rater reliability [IRR]: 0.77), thus establishing a gold-standard clinical reference. The LLM judge was strongly aligned with this clinical consensus (IRR: 0.81) overall and within key conditions. Clinician raters generally perceived the user-agents to be realistic. For the potential mental health benefits of AI chatbots to be realized, attention to safety is paramount. Findings from this human evaluation study support the clinical validity and reliability of VERA-MH: an open-source, fully automated AI safety evaluation for mental health. Further research will address VERA-MH generalizability and robustness.
arXiv:2602.05088v1 Announce Type: new
Abstract: Millions now use leading generative AI chatbots for psychological support. Despite the promise related to availability and scale, the single most pressing question in AI for mental health is whether these tools are safe. The Validation of Ethical and Responsible AI in Mental Health (VERA-MH) evaluation was recently proposed to meet the urgent need for an evidence-based automated safety benchmark. This study aimed to examine the clinical validity and reliability of the VERA-MH evaluation for AI safety in suicide risk detection and response. We first simulated a large set of conversations between large language model (LLM)-based users (user-agents) and general-purpose AI chatbots. Licensed mental health clinicians used a rubric (scoring guide) to independently rate the simulated conversations for safe and unsafe chatbot behaviors, as well as user-agent realism. An LLM-based judge used the same scoring rubric to evaluate the same set of simulated conversations. We then compared rating alignment across (a) individual clinicians and (b) clinician consensus and the LLM judge, and (c) examined clinicians’ ratings of user-agent realism. Individual clinicians were generally consistent with one another in their safety ratings (chance-corrected inter-rater reliability [IRR]: 0.77), thus establishing a gold-standard clinical reference. The LLM judge was strongly aligned with this clinical consensus (IRR: 0.81) overall and within key conditions. Clinician raters generally perceived the user-agents to be realistic. For the potential mental health benefits of AI chatbots to be realized, attention to safety is paramount. Findings from this human evaluation study support the clinical validity and reliability of VERA-MH: an open-source, fully automated AI safety evaluation for mental health. Further research will address VERA-MH generalizability and robustness. Read More
Towards Reducible Uncertainty Modeling for Reliable Large Language Model Agentscs.AI updates on arXiv.org arXiv:2602.05073v1 Announce Type: new
Abstract: Uncertainty quantification (UQ) for large language models (LLMs) is a key building block for safety guardrails of daily LLM applications. Yet, even as LLM agents are increasingly deployed in highly complex tasks, most UQ research still centers on single-turn question-answering. We argue that UQ research must shift to realistic settings with interactive agents, and that a new principled framework for agent UQ is needed. This paper presents the first general formulation of agent UQ that subsumes broad classes of existing UQ setups. Under this formulation, we show that prior works implicitly treat LLM UQ as an uncertainty accumulation process, a viewpoint that breaks down for interactive agents in an open world. In contrast, we propose a novel perspective, a conditional uncertainty reduction process, that explicitly models reducible uncertainty over an agent’s trajectory by highlighting “interactivity” of actions. From this perspective, we outline a conceptual framework to provide actionable guidance for designing UQ in LLM agent setups. Finally, we conclude with practical implications of the agent UQ in frontier LLM development and domain-specific applications, as well as open remaining problems.
arXiv:2602.05073v1 Announce Type: new
Abstract: Uncertainty quantification (UQ) for large language models (LLMs) is a key building block for safety guardrails of daily LLM applications. Yet, even as LLM agents are increasingly deployed in highly complex tasks, most UQ research still centers on single-turn question-answering. We argue that UQ research must shift to realistic settings with interactive agents, and that a new principled framework for agent UQ is needed. This paper presents the first general formulation of agent UQ that subsumes broad classes of existing UQ setups. Under this formulation, we show that prior works implicitly treat LLM UQ as an uncertainty accumulation process, a viewpoint that breaks down for interactive agents in an open world. In contrast, we propose a novel perspective, a conditional uncertainty reduction process, that explicitly models reducible uncertainty over an agent’s trajectory by highlighting “interactivity” of actions. From this perspective, we outline a conceptual framework to provide actionable guidance for designing UQ in LLM agent setups. Finally, we conclude with practical implications of the agent UQ in frontier LLM development and domain-specific applications, as well as open remaining problems. Read More
Evaluating Large Language Models on Solved and Unsolved Problems in Graph Theory: Implications for Computing Educationcs.AI updates on arXiv.org arXiv:2602.05059v1 Announce Type: new
Abstract: Large Language Models are increasingly used by students to explore advanced material in computer science, including graph theory. As these tools become integrated into undergraduate and graduate coursework, it is important to understand how reliably they support mathematically rigorous thinking. This study examines the performance of a LLM on two related graph theoretic problems: a solved problem concerning the gracefulness of line graphs and an open problem for which no solution is currently known. We use an eight stage evaluation protocol that reflects authentic mathematical inquiry, including interpretation, exploration, strategy formation, and proof construction.
The model performed strongly on the solved problem, producing correct definitions, identifying relevant structures, recalling appropriate results without hallucination, and constructing a valid proof confirmed by a graph theory expert. For the open problem, the model generated coherent interpretations and plausible exploratory strategies but did not advance toward a solution. It did not fabricate results and instead acknowledged uncertainty, which is consistent with the explicit prompting instructions that directed the model to avoid inventing theorems or unsupported claims.
These findings indicate that LLMs can support exploration of established material but remain limited in tasks requiring novel mathematical insight or critical structural reasoning. For computing education, this distinction highlights the importance of guiding students to use LLMs for conceptual exploration while relying on independent verification and rigorous argumentation for formal problem solving.
arXiv:2602.05059v1 Announce Type: new
Abstract: Large Language Models are increasingly used by students to explore advanced material in computer science, including graph theory. As these tools become integrated into undergraduate and graduate coursework, it is important to understand how reliably they support mathematically rigorous thinking. This study examines the performance of a LLM on two related graph theoretic problems: a solved problem concerning the gracefulness of line graphs and an open problem for which no solution is currently known. We use an eight stage evaluation protocol that reflects authentic mathematical inquiry, including interpretation, exploration, strategy formation, and proof construction.
The model performed strongly on the solved problem, producing correct definitions, identifying relevant structures, recalling appropriate results without hallucination, and constructing a valid proof confirmed by a graph theory expert. For the open problem, the model generated coherent interpretations and plausible exploratory strategies but did not advance toward a solution. It did not fabricate results and instead acknowledged uncertainty, which is consistent with the explicit prompting instructions that directed the model to avoid inventing theorems or unsupported claims.
These findings indicate that LLMs can support exploration of established material but remain limited in tasks requiring novel mathematical insight or critical structural reasoning. For computing education, this distinction highlights the importance of guiding students to use LLMs for conceptual exploration while relying on independent verification and rigorous argumentation for formal problem solving. Read More
