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1,407 papers
Side-channel evaluators routinely inspect leakage tests while acquisition is still running, and extend or stop the campaign based on what they see. Fixed-horizon screening such as the Welch $t$-test with threshold $|t|>4.5$ gives no error guarantee for this monitored decision rule. We study anytime-valid leakage detect...
A pure $\varepsilon$-differentially private mechanism that combines private depth selection with the authors' pruned variant of the Private Measure Mechanism (PMM) is proposed, and a lower bound under a corresponding internal packing-growth condition is proved, showing that the exponent $1/k$ is sharp within this frame...
It is argued that evaluation validity is a prerequisite for, not a footnote to, defense claims in agentic security, and a harness is provided to provide an instrument that enforces it.
DegreeSpar is introduced, which formulates secure Transformer compression as structured sparsification over nonlinear polynomial degrees, establishing structured polynomial degree as an effective shared optimization space for secure Transformer compression.
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Evaluating the impact of black-box evasion attacks on a cost-utility-based adversarial training defense strategy in an Online AutoML context for IoT networks shows that the AT versions of LB and SRP performed best, achieving the highest adversarial accuracy and high clean accuracy.
TANGO is presented, a watermark for masked-diffusion language models that keys each new token to a nearby token that is already unmasked, and TANGO biases the new token toward a color determined by the key and the nearby token's color.
The key insight is that fast behavioral fingerprints with occasional replay challenges are sufficient for auditing neural network training, and the method is applicable at scale with minimal overhead to the trainer, is cheap for the verifier, rejects bad training runs with amplifiable probability, and allows for exact...
Across vision and language models, it is shown that efficiency-oriented training increases susceptibility to adversarial and privacy attacks, and is called for a paradigm shift toward multi-objective training that jointly optimizes for performance, cost, and security.
This work proposes a curriculum learning-based method to address the cold-start problem and can effectively red-team frontier LLMs, and shows that the attacker LLM transfers across targets, e.g., an attacker LLM trained to defeat one strong LLM also succeeds against six other frontier LLMs it was never trained on.
On-policy distillation (OPD) trains a student to match a teacher's next-token distributions on student-generated trajectories. However, privileged information supplied to the teacher for OPD training may contain sensitive data. Whether the student leaks private information about the records supplied to the teacher duri...
AnchorMixGAN is a generative semi-supervised framework that addresses distributed denial-of-service (DDoS) attacks in cloud-integrated IoT networks through anchor-aligned target construction, and analyzes how reference-classifier error, view construction, and sharpening affect the target.
DualityCert, a symbolic verifier for candidate Seiberg-duality claims in four-dimensional N=1 quiver gauge theories, is presented and used as a repair environment for language-model agents, which receive a deliberately broken claim and must edit it until it certifies.
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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