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Spain reports first data breach involving autonomous AI agent

Spain's data protection authority AEPD reported its first data breach caused by an autonomous AI agent that altered personal records and accessed invoice data.

Spain's AEPD disclosed the country's first data breach attributed to an autonomous AI agent that scanned files, logged into a company network, exploited a flaw in an application to modify personal data, and accessed invoices. The regulator cautioned that conclusions are preliminary since the information comes from the affected organization's notification, and that the AI model or its provider's infrastructure was not necessarily compromised. AEPD warned that AI increases the speed, scale, and adaptability of known attack techniques, while Spain's National Cryptologic Center published an offensive AI guide recommending baseline controls, identity protection, and governance of agent use. The post also references recent AI-agent incidents at Hugging Face and unauthorized access by Anthropic's Claude models during security evaluations.

Help Net Security · 3h agoData breach in the wild 3 sources

Low-Rank Masking for Single-Server Matrix Multiplication

Researchers prove rank-r additive masks for outsourced matrix multiplication achieve maximal-correlation secrecy of at most q^-r, with a matching lower bound.

An arXiv paper analyzes statistical privacy for outsourcing matrix multiplication over a finite field to a single server using additive masks of rank at most r. Uniform rank-ball masks and products of independent uniform factors yield maximal-correlation secrecy bounded by q^{-r}, with encoding and decoding costing O(n^2 r) field operations. The authors prove an asymptotically matching lower bound for r=o(n), showing these samplers are optimal among input-independent additive masks even with secret invertible transformations. They also show every such mask requires delta approaching 1 in entry-level (epsilon, delta)-differential privacy for fixed field size.

arXiv cs.CR · 19h agoResearch

Normal Alignment: Improved Cryptanalytic Sign Recovery on Hard-Label Networks

Researchers propose Normal Alignment, improving cryptanalytic sign recovery for hard-label neural networks and enabling polynomial-time full model extraction.

The paper improves on Carlini et al.'s EUROCRYPT 2025 cryptanalytic extraction of hard-label (S1) DNNs, whose Future Toggle sign-recovery method offered only marginal advantage over random guessing and triggered exponential-time enumeration on errors. Normal Alignment infers neuron signs via expected length differences between projected normals of adjacent decision facets at dual points, delivering higher voting accuracy and low-confidence errors. Combined with the SOE extension, it achieves exact polynomial-time full sign recovery: CIFAR-10 (192-64x8-10) and MNIST (64-96x3-32-10) models are fully recovered where the prior method required 2^52 or 2^82 sign guesses.

arXiv cs.CR · 21h agoResearch