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To keep the AI hacking genie bottled up, try one-way networks

Intuition Machines CEO proposes data diodes and one-way networks to physically prevent frontier AI models from escaping training sandboxes, citing the OpenAI Hugging Face incident.

Eli-Shaoul Khedouri, CEO of Intuition Machines, argues that sandboxes, permissions, and VMs are insufficient to contain frontier models, pointing to OpenAI's hack of Hugging Face as evidence. He proposes high assurance architectures modeled on classified SCIF environments: one-way optical data diodes for training inputs and telemetry, a sel4-verified receiver, immutable snapshots of registries like PyPI, GitHub, and npm, and mocked web services. He estimates under five percent overhead per gigawatt for such clusters, but notes frontier labs have not adopted them, largely because of competitive speed rather than cost.

Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems

Compact ResNet detects electrical faults in 400 Hz aircraft power systems with 95.87 percent accuracy after deployment on Xilinx Zynq MPSoC.

The study targets multiclass fault and power quality disturbance detection in 400 Hz aerospace networks using a high-fidelity simulation model inspired by the Boeing 787 electrical architecture, covering 21 normal, disturbance, switching, open-circuit, and short-circuit conditions. Two 73,500-sample datasets are built from 1D waveforms and STFT time-frequency representations, augmented with domain randomization and class-specific GANs, and the time-series dataset is released via IEEE DataPort. A compact ResNet with 175,685 parameters achieved 96.94 percent software test accuracy, and 95.87 percent after 8-bit quantization on a Xilinx Zynq UltraScale Plus ZCU102 with 6.90 ms mean accelerator latency per record.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

Securing the unpatchable in an age of AI-driven vulnerabilities

Cisco Talos argues AI-driven vulnerability discovery leaves unpatchable OT systems exposed, recommending virtual patching via NGFW/IPS and micro-segmentation.

AI-assisted code analysis is uncovering vulnerabilities faster than organizations can patch, leaving certified or end-of-life OT systems with unmitigated known flaws. Talos recommends virtual patching with next-generation firewalls and IPS, micro-segmentation using VLANs and ACLs, and building visibility-based inventories of legacy systems. The article cites WannaCry's impact on the NHS and 2023 exploitation of end-of-life software in government systems, and warns that air gaps and data diodes are routinely circumvented by operational shortcuts.

Cisco Talos · 5h agoResearch

A Deep Generative Model for Synthesizing Labeled Wireless Signals

Researchers propose IIns-GAN, a GAN that synthesizes realistic labeled ultra-wideband wireless signals, cutting dataset costs for wireless sensing training.

The paper introduces Inter-Instance Generative Adversarial Networks (IIns-GAN), a deep generative method that synthesizes realistic wireless signals with position-related labels to avoid costly real-world measurement and labeling. Unlike environment-model-based synthesis, the generated signals adapt to different environment scenarios and support training tasks such as distance estimation and environment identification. Experiments on public Ultra-Wideband (UWB) datasets show the synthetic signals closely mirror real measurements and improve model training performance.

arXiv cs.AI / cs.LG / cs.CL · 11d agoAI research

Wicked Problem, Parsimonious Solution: Securing Electric Vehicle Charging Station Software

Position paper proposes hierarchical software quality assurance to characterize and secure EV charging station software attack surfaces.

The authors argue that charging station supply-equipment software is a largely unprotected and poorly characterized attack surface in EV charging infrastructure. They advocate applying hierarchical software quality assurance (HSQA) to this specialized software, spanning from individual vulnerabilities such as CVEs to high-level characteristics like the CIA Triad. HSQA embeds quality and security considerations across the entire software development lifecycle to assess and improve charging station software security.

arXiv cs.CR · 6d agoResearch

Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers

Quantum IQP circuit features lift logistic-regression credit-default F1 from 0.462 to 0.517, beating Kernel PCA at an equal feature budget.

Using the UCI Default of Credit Card Clients dataset and five-fold cross-validation, an 8-qubit IQP circuit adds 16 features that raise Logistic Regression F1 from 0.462 to 0.517 (+0.055, p < 0.0001). Kernel PCA, the best classical non-linear alternative, reaches only 0.493 at the same feature count, with the gap surviving Benjamini-Hochberg correction across 12 tests (p = 0.00007). Only the linear classifier benefits, pointing to a linear-expressivity mechanism. Feature selection matters: Random Forest importance-guided selection reaches F1 = 0.523 while maximally uncorrelated features drop to 0.496.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

How CHERIoT Provides Strong and Usable Isolation Without an MMU

ACM Queue paper explains how CHERIoT uses CHERI hardware capabilities to give microcontroller-class IoT devices memory and privilege isolation without an MMU.

The ACM Queue article (DOI 10.1145/3831361) describes CHERIoT, a RISC-V-derived platform that scales CHERI capability-based hardware down to small embedded and IoT microcontrollers. It provides strong, fine-grained isolation and memory safety without a memory management unit, aiming to make compartmentalization practical for low-cost devices. The piece emphasizes usability of the isolation model alongside its security guarantees.

Lobsters · security · 6d agoResearch

Security Data Isn’t the Problem. Security Context Is.

Horizon3 blog argues security context, not data volume, is the SOC bottleneck, promoting its NodeZero integration with CrowdStrike Falcon Next-Gen SIEM.

Horizon3.ai published a vendor blog explaining how its NodeZero Proactive Security Platform integration with CrowdStrike Falcon Next-Gen SIEM brings validated exposure findings into existing security operations workflows. The post argues SOCs are now limited by confidence rather than visibility, needing context to decide which issues matter. It cites a global chemical manufacturer that validated exploitable exposures with NodeZero before completing a $2 billion merger.

Horizon3.ai · 1d agoTools

Injected and Leaked: Actively Inducing Side-Channel Leakage Using Electromagnetic Injection and Hardware Nonlinearity

Researchers introduce InjectEave, using electromagnetic injection and hardware nonlinearity to induce side-channel leakage and eavesdrop on headphone audio from 30 meters.

An arXiv paper shows electromagnetic injection can actively amplify side-channel leakage: nonlinear hardware such as amplifiers, ADCs, and power converters modulates secret electrical signals onto an injected EM carrier, upconverting low-frequency secrets into measurable EM emissions. By tuning injection frequency and amplitude, an adversary can shape the effective spectrum and entropy of the resulting leakage. The InjectEave attack demonstrated eavesdropping on wired and wireless headphone audio from up to 30 meters and in through-wall scenarios using accessible RF equipment, plus leakage of smart home device power consumption and analog sensor inputs. Case studies show closed-loop eavesdropping and manipulation of landline phone conversations, and the paper discusses mitigations.

arXiv cs.CR · 12d agoResearch

Risks in IoT Supply Chain

Unit 42 analyzes multilayer IoT supply chain risks across hardware, firmware, and software, citing counterfeit Cisco switches and OpenWrt attacks.

Unit 42 examines weaknesses in the IoT supply chain ecosystem across hardware, firmware, operation, and vulnerability layers, noting that 89% of IT decision-makers reported IoT device growth and IDC forecast 41.6 billion connected IoT devices by 2025. Examples include counterfeit Cisco Catalyst 2960-X switches with possible backdoor access (F-Secure, July 2020), a March 2020 OpenWrt flaw enabling malicious update impersonation, and threat actor interest in TeamViewer remote support software. The report stresses that untracked third-party components and missing device inventories make it hard to assess vulnerability impact across vendors.

Palo Alto Unit 42 · 28d agoResearch

From Concept to Context Engine: How Wiz Built AI-Powered Data Discovery

Wiz details the multi-agent pipeline and feedback loops that evolved its bucket scanner into AI-powered data discovery.

Wiz published an engineering write-up on building its AI-powered data discovery capability, tracing the evolution from a bucket scanner to a context engine. The post explains the multi-agent pipeline and feedback loops behind the system. The article is a vendor engineering deep-dive with no disclosed vulnerabilities, incidents, or exploitation activity.

Wiz Blog · 19d agoTools1

Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLMs?

A systematic study shows input-level adversarial defenses provide inconsistent, often near-zero protection against observation-level attacks on video LLMs.

Researchers introduce DefTEval, a controlled framework testing eleven input-level defenses against five attack types across five video LLMs. Harmful-content detection rates are frequently near zero, and defenses fail even when attacks embed harmful signals in every sampled frame. Token compression discards localized safety features and modality fusion down-weights weakened visual signals, with defense outcomes dominated by model architecture rather than the defense method.

arXiv cs.CR · 8d agoAI safety & security

Medical device maker Boston Scientific says a cyberattack is causing a ‘global disruption’ to its operations

Boston Scientific says a cyberattack has caused global disruption to its operations, with no confirmation yet on device impact or data exfiltration.

Boston Scientific, a major medical device manufacturer, disclosed that a cyberattack is causing global disruption to its operations. The company has not confirmed whether medical devices are affected or whether any customer data was exfiltrated. The investigation appears to be ongoing, and healthcare-sector exposure raises patient-safety and supply-chain concerns.

TechCrunch · Security · 20d agoData breach

[Control Systems] Siemens security advisory (AV26-864)

Siemens fixed a vulnerability in Element maps-ng V47-V49 (SSA-682041); Canada's Cyber Centre urges administrators to apply the updated releases.

Siemens advisory SSA-682041 addresses a vulnerability affecting Element maps-ng V47 prior to V47.12.3, V48 prior to V48.11.3, and V49 prior to V49.16.1. Canada's Cyber Centre republished the notice (AV26-864) encouraging users and administrators to review the vendor links and apply the necessary updates. No CVSS score or exploitation details were provided in the bulletin.

Canadian Centre for Cyber Security · 15d agoAdvisory

Datamimic – don't let your coding agent invent its own test world

Datamimic is an open-source test data generation tool aimed at keeping coding agents from inventing their own test fixtures.

A Hacker News discussion (40 points) highlights Datamimic, an open-source rapiddweller GitHub project for generating realistic synthetic test data. The tool targets AI coding agents, aiming to prevent them from fabricating their own inconsistent test worlds. Only the repository link was shared, so details are limited.

Powering AI is an architecture problem

Sponsored analysis argues AI data centers need medium-voltage, inline power architecture after Virginia grid faults knocked over 3GW of load offline.

A sponsored MIT Technology Review piece recounts a July 22, 2026 transmission fault in Ashburn, Virginia that shed more than 3 GW of data center load, and a 2024 incident where one failed surge arrester dropped about 60 facilities and 1,500 MW. It argues legacy UPS-based power stacks fail at AI scale because campuses can swing 70% of load in milliseconds and trip offline during grid disturbances. The proposed fix moves protection to medium voltage (13.8 kV and above) in inline enclosures near substations, improving density, permitting timelines, and backup power economics. A full-scale system tested at the DOE National Laboratory of the Rockies cleared ERCOT large-load ride-through requirements.

MIT Technology Review · AI · 6d agoAI industry1

What the Data Says About AI in Security Operations in 2026

Prophet Security's 2026 survey of 250+ security pros: 40% use AI daily; AI users report 25%+ faster investigations and rising AI-driven attacks.

Prophet Security's State of AI in Security Operations 2026 report, based on a survey of 250+ cybersecurity professionals, found 40% of security teams use AI daily and only 4% have no adoption plans. Teams average 100 daily alerts (up to 1,000 at large firms), leave 28% of alerts uninvestigated, and 60% of respondents said missed alerts led to breaches or downtime. Among AI adopters, 72% report at least 25% faster investigations, 56% observed increased AI-driven attacks, and no respondents grant AI full unsupervised autonomy. Data privacy (44%) and explainability (41%) top the adoption hurdles.

The Hacker News · 20d agoIndustry

[Control Systems] National Instruments security advisory (AV26-856)

Canada's Cyber Centre relayed National Instruments advisories for memory corruption, out-of-bounds read, and out-of-bounds write flaws in LabVIEW versions.

The Canadian Centre for Cyber Security published control systems advisory AV26-856 covering National Instruments LabVIEW. Affected versions include releases before 23.0.0, 23.3.10, 24.3.7, 25.3.5, and 26.3.1. The flaws include memory corruption, an integer conversion out-of-bounds read, and an integer overflow out-of-bounds write. Users and administrators are urged to review the links and apply NI security updates.

Canadian Centre for Cyber Security · 19d agoAdvisory

Lightweight Vision Transformer Compression for On-Device Plant Disease Detection in Resource-Constrained Agricultural Field Conditions

A unified ViT compression pipeline (H-BAC pruning, quantization, distillation) cuts plant-disease models 54.5x to 6.01 MB while keeping 95.13% accuracy.

Researchers combined Hessian-Balanced Adaptive Block Pruning (H-BAC), guided by second-order sensitivity estimation, with quantization and attention-based knowledge distillation to compress Vision Transformers for on-device chilli plant disease detection in India. On a 3-class cross-village, cross-device out-of-distribution dataset, the integrated pipeline reduced model size from 327.42 MB to 6.01 MB (54.5x) at 95.13 +/- 2.32% accuracy, matching the 95.13% FP32 baseline. Ablations also show a directly trained 6.01 MB INT8 student reaches 94.87% accuracy, indicating where pruning and distillation add limited value.

arXiv cs.AI / cs.LG / cs.CL · 11d agoAI research

New hardware device can RAM into encrypted memory, expose your data

Researchers built a $200 DDR5 interposer that silently drops memory writes to break TDX, SGX, and SEV-SNP confidential VM integrity, requiring physical access.

Researchers from KU Leuven, ETH Zurich, Durham University, and Google demonstrated DDRop, a hardware interposer costing under $200 that corrupts DDR5 bus commands to silently drop writes to encrypted memory, enabling replay attacks on confidential VMs. Because scalable memory encryption lacks freshness checks, protected VMs keep computing on stale attacker-selected data; on an Intel TDX server the attack forces debug mode for plaintext memory reads or forges attestation reports, succeeding in under two minutes without crashing. Intel and AMD both called the attack out of scope for their cloud threat models, with no mitigation planned, and proposed cache line versioning appears still vulnerable. The full interposer design is being released as open-source hardware.

The Register · Security · 1d agoResearch

Securing Data in the AI era

Wiz published guidance arguing AI adoption reshapes data risk, urging teams to identify what data is connected to AI services and exposed.

Wiz published a viewpoint on how AI adoption changes the context of data risk. The post argues that understanding what data is connected to AI systems, what is exposed, and why is now critical for managing that risk. No specific incident, vulnerability, or product release is described in the available text.

Wiz Blog · Aug 14, 2026Industry

DataFlex-RL: An Evaluation Platform for RLVR Data Policies

DataFlex-RL benchmark of 13 RLVR data policies on Qwen2.5-7B finds none reproducibly beats uniform sampling under matched GRPO training.

DataFlex-RL is an evaluation platform comparing rollout-selection, reweighting, and mixture data policies for RLVR under a common GRPO recipe. Across 13 configurations and 12 matched seeds with Qwen2.5-7B-Base on 12 math, logic, and science benchmarks, uniform GRPO improved domain-balanced accuracy by 7.76 points, but no alternative policy achieved a statistically significant improvement. A corrected 12-seed Llama-3.1-8B-Base extension found no consistent winner, and math-heavy evaluation summaries were negatively correlated (-0.33) with domain-balanced summaries.

Hugging Face daily papers · 11d agoAI research

An Empirical Analysis of ReDoS Vulnerabilities and ReDoS Detection Tools

Study of NVD data finds ReDoS vulnerabilities growing more prevalent and more likely to be exploited, while five detection tools disagree substantially.

The study compares five publicly available ReDoS detection tools and one regex correction tool across three datasets. An empirical analysis of all ReDoS vulnerabilities reported to the NVD finds they are becoming more prevalent and are much more likely to be exploited than non-ReDoS weaknesses. The detection tools exhibited substantial disagreement on whether a given regex is vulnerable.

arXiv cs.CR · 7d agoResearch

Quantifying IIoT Sensor Node Criticality by Fusing its Data Criticality and Security Vulnerability

Researchers propose a Dempster–Shafer framework fusing IIoT sensor data criticality with CVSS 4.0/3.1 vulnerability scores to rank node criticality.

The paper introduces a framework that evaluates Industrial IoT sensor node criticality by fusing data criticality and cybersecurity vulnerability scores using Dempster–Shafer (D-S) theory. It was validated on a dataset from red wine production and is claimed to generalize to other industrial settings with minimal modification. Results show criticality rankings derived from CVSS 4.0 scores differ significantly from those derived from CVSS 3.1, underscoring how vulnerability scoring methodology affects security prioritization.

arXiv cs.CR · 7d agoResearch

New DDRop Attack Breaks Intel TDX and AMD SEV-SNP With $159 DDR5 Device

DDRop uses a $159 DDR5 RDIMM interposer to silently drop memory writes and break Intel TDX and AMD SEV-SNP confidential VMs.

Researchers published DDRop, a physical attack built from about $159 in parts that uses a custom DDR5 RDIMM interposer to inject parity errors and silently discard selected cache-line writebacks. Intel TDX, Intel Scalable SGX, and AMD SEV-SNP are affected because they lack per-line cryptographic freshness, so processors can accept stale encrypted data as valid state. The team demonstrated deterministic plaintext copying between pages, malicious Secure EPT entry injection, forcing trust domains into debug mode, and forging attestation measurements. The attack requires privileged host control plus brief physical access, and researchers say no simple software patch exists.

Cyber Security News · 1d agoResearch 2 sources

GDCM <= 3.2.7: six memory-safety and denial-of-service vulnerabilities, no CVE

Six memory-safety and denial-of-service flaws disclosed in the GDCM DICOM parsing library, affecting versions through 3.2.7.

Researcher Abhinav Agarwal disclosed six vulnerabilities in GDCM (Grassroots DICOM), an open-source C++ library for parsing and processing DICOM files. All six were confirmed against GDCM 3.2.6 using AddressSanitizer and UndefinedBehaviorSanitizer, and source review found the vulnerable patterns through version 3.2.7 and the upstream master snapshot. Potential impacts include heap corruption, process-memory disclosure, stack exhaustion, and process termination in applications parsing untrusted DICOM files. No CVE identifiers have been assigned at the time of disclosure.

oss-security · 6d agoVulnerability

OpenVDN/vdn-minimax-h3 — new model trending #12 on Hugging Face

OpenVDN releases VDN-H3, an open hybrid-attention video model on MiniMax H3 that renders a 14.4-second 768p clip in 11.23 seconds on 8 B200 GPUs.

VDN-Minimax-H3 (VDN-H3) adds a frame-wise linear attention branch plus two LoRA adapters to MiniMax H3, distilled into 8-step and 50-step variants. It generates 768p, 14.4-second clips in 11.23 seconds on 8 B200 GPUs (90.5 seconds on one H200) using 8 denoising steps. Weights (about 82 GB total, including the 72 GB H3 base), the optimized inference stack, and training code are fully open-source under the MiniMax H3 Community License, which excludes the EU, UK, Korea, and US.

Hugging Face trending models · 14d agoModel release1

Schneider Electric Easergy, EcoStruxture, PowerLogic, and Saitel Products (Update A)

CISA updated ICSA-26-169-07: CVE-2026-4827 (CVSS 8.3) insufficient entropy enables session hijacking across dozens of Schneider Electric grid products; fixes available.

CISA republished advisory ICSA-26-169-07 (Update A) for CVE-2026-4827, CWE-331 insufficient entropy in session management, scored CVSS 8.3. Affected lines include Easergy MiCOM relays and C5, EcoStruxure Power Automation (EPAS-GTW, EPAS-UI, iPMFLS), EcoStruxure Power Operation, PowerLogic P5/P7/T300/T500, and Saitel DP/T150 RTUs, with dozens of fixed versions listed. Successful exploitation could enable session hijacking and unauthorized operations on systems in energy, chemical, critical manufacturing, and water sectors. Fixes are available; no exploitation is reported.

CISA Advisories · 13d agoAdvisoryCVE-2026-4827

18 ways to check whether data can be trusted for AI

ETSI published TR 104 180 defining 18 data quality metrics, including fairness and privacy, to assess whether datasets are fit for AI.

ETSI's technical report TR 104 180 defines 18 metrics across four groups - intrinsic quality, usability and lineage, fairness, and privacy - each with calculation formulas, plus an open-source tool that scores datasets. Testing on an aircraft engine sensor dataset and a US census dataset revealed a roughly threefold gender gap in high earners (about 31% of men versus 11% of women) and two privacy failures: re-identification via age, race, sex, and country, and sensitive fields stored in plaintext. The working group included Sejong University, EGM, TTA, Daejeon University, and CNIT.

Help Net Security · 9d agoAI policy

Everything in Moderation: Per-Domain Coverage Optima and Alignment-Resistant Domain Gaps in Multi-Domain Mid-Training

Controlled mid-training experiments on Qwen3-8B-Base find each domain has a 10-40% coverage optimum and domain gaps survive alignment SFT.

Using Qwen3-8B-Base (with a 4B replication) across five semantically rule-disjoint KOR-Bench domains, the authors train 30 data allocations spanning the five-domain simplex at five seeds each. All five domains show interior optima in the moderate 10-40% coverage band, and domain gaps persist after a fixed-budget compensatory SFT pass, which raises 116/120 cells yet bridges 0/240 pairs at a 5% threshold. Zero coverage collapses mid-training-only accuracy, though a FineWeb-Edu-only control shows the collapse is partly generic drift. The results argue mid-training data composition requires principled design rather than reliance on later alignment.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research1

PIDS-Bench: Evaluating Prompt-Injection Detectors Under Over-Defense, Obfuscation, and Distribution Shift

PIDS-Bench shows prompt-injection detectors scoring F1 above 0.98 still misclassify about one-third of external benign security-adjacent prompts, revealing provenance-sensitive over-defense.

PIDS-Bench is a frozen multi-axis benchmark that jointly evaluates prompt-injection detectors on attack detection and benign false-positive behavior at fixed thresholds, spanning in-distribution inputs, hard-benign prompts, obfuscated attacks, and domain/structural distribution shifts. It evaluates seven detectors plus a rule-based lower-bound reference. A detector exceeding F1 = 0.98 on held-out data still misclassifies roughly one-third of an externally-sourced benign security-adjacent subset, and no internal detector reaches F1 >= 0.95 with hard-benign FPR <= 0.10 on the stress distribution. Hard-negative augmentation nearly eliminates over-defense on curated stress inputs but leaves it intact on externally-sourced prompts, a pattern termed provenance-sensitive over-defense.

arXiv cs.CR · 2d agoAI safety & security