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1,407 papers
The results show that source deletion should be evaluated behaviorally at the level of the agent and its memory system, not only as a storage operation.
ReDence is presented, an open-source hybrid framework for adaptive, evidence-grounded detection across diverse SE scam categories, and achieves 96\% accuracy, substantially outperforming existing detectors while being at least four times faster than comparable agentic models without compromising investigative depth.
The paper delineates an adaptive, arbitrary-precision architecture dedicated exclusively to primality testing, and deeply analyzes the theoretical foundations of testing methods, hardware-level optimization techniques, and the application of quantum-secured entropy sources, while providing rigorous mathematical proofs...
MIG-COW is proposed, a novel adversarial attack framework that generates highly generalizable and visually imperceptible adversarial examples that captures both shared and model-specific vulnerabilities across heterogeneous CNN and ViT detectors.
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LLVMhammer is presented, the first Rowhammer attack that causes LLM inference slowdown by inducing a bit-flip in Triton's compilation pipeline and it is shown that LLVMhammer can also corrupt LLM outputs across different GPU vendors, reducing BERTScore to as low as 25\%, illustrating the broader security implications o...
This paper presents AIRO (Autonomous Intelligence-Driven Cybersecurity Risk Re- duction System), an efficient framework for IIoT monitoring and response that combines Apache Kafka streaming, attack- specific feature extraction, machine learning (ML) inference, confidence-based decision logic and risk reduction measures...
Six new safety datasets of sufficient size and difficulty are constructed and it is found that probe error follows two simple scaling laws, which allow safety researchers to determine how to create the best probe for any given model and label budget.
This work proposes a Hybrid Fuzzy Ensemble that synergizes the precision of supervised learning with the adaptability of unsupervised clustering, and introduces a robust Min Distance metric that exploits the raw geometric isolation of botnet activation attacks.
It is shown that standard gradient-based attacks applied end-to-end through an unrolled low-density parity-check decoder fail to significantly outperform a matched-power Gaussian perturbation, and two structurally independent loss functions are proposed that restore attack efficacy.
A method to detect synchronous model training in facility-level power by Viterbi tracking of the wandering iteration cadence and a modest passive meter is sufficient to make hidden training both detectable and expensive, which makes power metering a promising part of compute verification that does not depend on trustin...
This work investigates both gray-box and black-box threat models and proposes a unified attack framework, named OP-Print, based on the finding that the supervision imprint in on-policy self-distillation is sparse and structured, concentrating on uncertainty-sensitive correction sites rather than across the entire rollo...
The certificate preserves the mean-dependent variance of multinomial batch averages, yielding the near-optimal robust rate $O(\eta\sqrt{\log(1/\eta)/m})$ and providing the score required by the privacy reduction.
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
GPT-Lab
· gpt-lab.eu
Oct 30, 2026
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
Gemini 4 Carbon is our strongest model with state-of-the-art cybersecurity and long horizon capabilities.
Gemini 3.8 Flash and 3.8 Flash Cyber deliver next-generation intelligence for agentic workflows and cybersecurity.
Google introduces Gemini 3.5 Flash Cyber, a lightweight cybersecurity model to find and patch vulnerabilities.
Students from the MIT Cybersecurity Clinic help local governments and other vulnerable organizations defend against digital threats.
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