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PHAT: PHotonic Accelerator for TFHE

PHAT photonic accelerator using optically addressed phase-change memory speeds TFHE bootstrapping FFTs 2.14x-5.10x over the prior ASIC accelerator.

Researchers propose PHAT, an electro-photonic accelerator for TFHE fully homomorphic encryption built on optically addressed phase-change memory (OPCM) processing-in-memory. The architecture adds OPCM-based FFT units, a twiddle-stationary dataflow, and a scheduling mechanism to maximize FFT utilization for TFHE bootstrapping. It achieves 2.14x-5.10x speedups across four real-world TFHE workloads versus the state-of-the-art ASIC accelerator, improving feasibility of privacy-preserving computation in the cloud.

arXiv cs.CRSep 10, 14:30 UTC · 6d agoResearch

Real-Time Intelligence with IBM Time Series Models on Confluent

IBM Research post on the Hugging Face blog describes running IBM time series models on Confluent for real-time intelligence.

Hugging Face's blog published an IBM Research post titled 'Real-Time Intelligence with IBM Time Series Models on Confluent.' No article body was available for classification, so details are limited to the title. The title indicates guidance on deploying IBM time series models alongside Confluent streaming infrastructure for real-time analytics.

Hugging Face BlogSep 2, 13:49 UTC · 14d agoAI tools & infra

ROSETTA: Efficient and Accurate Privacy-Preserving LLM Decoding via Hybrid CKKS/TFHE Evaluation

ROSETTA is a hybrid CKKS/TFHE homomorphic encryption framework for privacy-preserving LLM decoding, achieving up to 4.8x Softmax and 2.1x end-to-end speedups.

The paper proposes ROSETTA, a hybrid CKKS/TFHE fully homomorphic encryption framework for private inference on generative LLMs, targeting the nonlinear operations that dominate autoregressive decoding cost. It introduces an adaptive segmented lookup-table protocol based on TFHE and a scheme-aware operator-selection framework that assigns each nonlinear operator to CKKS or TFHE to minimize latency. Experiments show up to 4.8x Softmax speedup and 1.5-2.1x end-to-end decoding speedup over the state-of-the-art CacheMir framework.

arXiv cs.CRSep 15, 09:51 UTC · 1d agoResearch

Automatic Key Exchange: faster, post-quantum secure origin handshakes for 45 billion daily connections (and counting)

Cloudflare's Automatic Key Exchange probes origins to lead with preferred key exchange, cutting HelloRetryRequests from 52% to 3.7% and enabling automatic post-quantum origin connections.

Cloudflare announced Automatic Key Exchange, an extension of Automatic SSL/TLS that probes each origin's supported key agreement algorithms and leads with the preferred one, favoring the post-quantum hybrid X25519MLKEM768. Rollover across roughly 45 billion daily origin connections cut HelloRetryRequests from about 52% to 3.7%, saving more than 150 ms of p90 handshake latency. Hundreds of thousands of domains now receive post-quantum origin connections without manual configuration, supporting Cloudflare's goal of quantum-safe encryption by 2029 to counter harvest-now-decrypt-later attacks.

Cloudflare BlogSep 8, 13:10 UTC · 8d agoResearch

Shuffling is Not Enough: Breaking Permutation-Based Model Confidentiality in Hybrid FHE Inference

Attack breaks permutation-based model confidentiality in hybrid FHE inference, recovering all ResNet-20 linear layers exactly with d+1 queries per layer.

The paper shows output-permutation plus noise fails to protect model confidentiality in hybrid FHE inference: d+1 admissible queries recover an exact permutation-invariant summary of a d-input linear layer, and shuffle-model DP amplification premises cannot hold under correctness-bounded noise. The authors recovered all linear layers of a Safhire-style ResNet-20 end-to-end from TFHE transcripts with zero error, using 5,712 total queries. Exact per-layer recovery was also confirmed on pretrained ImageNet-scale CNNs and ViT-B/16. Leaked layer spectra enable model fingerprinting, lineage attribution, and improved logit-based extraction, while suppressing them destroys inference utility.

arXiv cs.CRSep 11, 14:37 UTC · 5d agoResearch1

XHToken/Spark-X2.5-4B-GGUF — new model trending #30 on Hugging Face

XHToken released GGUF weights of Spark-X2.5-4B, a compact model with 1M-token context and 200+ language support, under Apache 2.0.

The Hugging Face repository provides BF16 GGUF conversions of Spark-X2.5-4B, a compact general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. The model uses a hybrid attention architecture, supports a native context length up to 1M tokens, and covers more than 200 languages. Local inference is supported through Ollama and LM Studio via an XHToken llama.cpp fork, with a --think=false flag to disable thinking mode for faster responses. Released under Apache License 2.0; it was trending #30 on Hugging Face at publication.

Hugging Face trending modelsAug 28, 11:26 UTC · 19d agoModel release

TPMSpy: Validation of Measured Boot Systems by Low-Level Tracing of TPM Usage

Researchers present TPMSpy, a platform-agnostic method validating TPM Measured Boot via low-level tracing, finding inconsistent Linux systemd measurements that break remote attestation and LUKS decryption.

An arXiv paper (2609.05011) introduces TPMSpy, a method that analyzes virtualized system–TPM interactions to independently reconstruct and validate TPM Event Logs without relying on the quoting mechanism, applicable to open and closed systems and demonstrated on Linux and Windows. A longitudinal analysis of Linux systems running systemd versions 245–258 (2020–2025) found wide divergence in Measured Boot usage, undocumented behavioral changes, and no common usage pattern. The authors report inconsistent measurement of user-space systemd services, which prevents reliable remote attestation and LUKS disk decryption on affected systems.

arXiv cs.CRSep 4, 11:23 UTC · 12d agoResearch

DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF — new model trending #8 on Hugging Face

A new Qwen3.8-27B GGUF fine-tune claims ARC-C 735 at 8-bit with thinking tokens cut 2x-10x versus the base model.

Independent creator DavidAU released a GGUF fine-tune of Qwen3.8-27B built with Unsloth, claiming ARC-C of 735 at 8-bit and 719 at 4-bit, trending #8 on Hugging Face. The 'TURBO' variant cuts thinking tokens by one half to as much as one tenth while retaining output quality and detail. The repo ships both regular and MTP quants and claims gains over the base model across seven benchmarks, using 'Cold Fusion (GAIN + Unsloth)' and 'Fable Fusion 711' training methods.

Hugging Face trending modelsSep 1, 07:01 UTC · 15d agoModel release

Causal Foundation Models

A paper introduces causal foundation models (CFMs): pretrained networks that estimate treatment effects on new datasets via in-context learning without fine-tuning.

Causal foundation models (CFMs) apply the foundation-model paradigm to causal inference, replacing bespoke per-problem estimator pipelines with networks pretrained once at scale. CFMs estimate causal quantities such as the average treatment effect on entirely new datasets through in-context learning, without model updates. The work serves as a practical introduction to the emerging area, covering background in causal inference and machine learning and including example code and Jupyter notebooks.

Hugging Face daily papersSep 1, 20:00 UTC · 14d agoAI research

Signing TLS handshakes inside a TPM

Blog post demonstrates signing TLS handshakes in Go with a TPM-held private key; full text was unavailable behind an Anubis challenge.

The title indicates a technique write-up on using a TPM-protected private key to sign TLS handshakes from Go. The full article text was not retrievable because the site serves an Anubis Hashcash-style proof-of-work challenge to automated fetchers, so no additional technical details (code, library, or threat model) could be extracted at collection time.

Lobsters · securitySep 7, 14:54 UTC · 9d agoResearch

Adaptive Gated Deepfake Detection for Low-Resolution and Resource-Constrained Environments

AdaGate-DF routes deepfake detection by image quality through dual multi-exit gates, hitting 0.9370 AUC on Celeb-DF with low inference latency.

AdaGate-DF is an adaptive gated deepfake detection framework that uses image-quality cues to send high-quality images through earlier exits, saving compute in resource-constrained settings. On Celeb-DF it achieves an AUC of 0.9370, outperforming MaD-CoRN and DefakeHop++, and reaches 0.9708 at 384x384 resolution. On FaceForensics++ it remains effective under class imbalance while balancing uncertainty-aware prediction and computational efficiency.

arXiv cs.AI / cs.LG / cs.CLSep 4, 16:14 UTC · 12d agoAI research

DF26: We Cannot Tell Fake From Real Anymore

DF26 benchmark shows humans and state-of-the-art deepfake detectors perform near chance on videos generated by seven modern text-to-video models.

Researchers introduce DF26, a benchmark of 271 real and 2,420 fully synthetic videos created by seven modern video generation models, all depicting single-person public-speaking scenarios such as direct-to-camera recordings, official statements, and studio interviews. Human viewers and state-of-the-art deepfake detectors scored close to random chance at distinguishing fakes from real footage. The authors argue current evaluation protocols are insufficient and call for benchmarks that explicitly measure robustness to modern generative model distribution shifts.

Hugging Face daily papersSep 6, 20:00 UTC · 9d agoAI research

LexFlip: A Dissociation Diagnostic for Legal Meaning Preservation Metrics

LexFlip releases 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving tokens, exposing weaknesses in embedding-based meaning preservation metrics.

LexFlip provides 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of tokens, creating dissociation items that break monotone token-overlap metric validation. The seven embedding and BERTScore metrics tested register only 0.022-0.039 of their identical-to-unrelated range on these edits, versus 0.670 for bidirectional NLI. Against FrJudge, with a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric tested.

arXiv cs.AI / cs.LG / cs.CLSep 4, 15:49 UTC · 12d agoAI research

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:32 UTC · 12d agoAI research1

Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:43 UTC · 6d agoAI research

dealignai/GLM-5.3-CYBERSECURITY-FP8 — new model trending #13 on Hugging Face

dealignai releases GLM-5.3-CYBERSECURITY-FP8, a 753B MoE weight-modified variant cutting refusals on offensive-security prompts, trending #13.

dealignai released GLM-5.3-CYBERSECURITY-FP8 on Hugging Face, a cybersecurity-domain 'crack' of the 753B-parameter GLM-5.3 MoE model, currently trending #13. The release directly edits bf16 residual writers, keeps FP8 routed experts, and serves with stock vLLM on 8x H200 GPUs with 131k context. HarmBench-320 evaluations show 80-84% direct harm compliance and 89% cyber-offense compliance, while MMLU rose 1.07 points to 86.65%. Copyright-verbatim reproduction remains a known soft-refusal limitation, with an UNCENSORED sibling variant offered.

Hugging Face trending modelsAug 30, 10:46 UTC · 17d agoModel release

Training a coding model to paint watercolours with TRL and OpenEnv

Hugging Face tutorial trains a coding model with TRL and OpenEnv to paint watercolours through generated code.

A Hugging Face blog walkthrough uses the TRL reinforcement learning library and the OpenEnv environment framework to train a coding model. The target task is generating code that produces watercolour-style drawings, serving as a hands-on reinforcement learning training example. No article body was available in the feed, so specifics are limited to the title.

Hugging Face BlogSep 3, 00:00 UTC · 13d agoAI tools & infra1

Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization

ModerationBench shows foundation models can nearly triple Bluesky's moderation F1 (0.60 vs 0.22), with instruction- and example-driven guidance performing comparably.

Researchers built ModerationBench, a new benchmark of 4,000 manually annotated in-the-wild posts from Bluesky, to test whether foundation models can reliably operationalize content moderation policies. They systematically compare instruction-driven guidance (reasoning from policy precepts) with example-driven guidance (generalizing from precedents) for Vision-Language Models. Both paradigms achieve comparable peak effectiveness, and foundation models nearly triple the F1 of Bluesky's deployed moderation system on Random Posts (0.60 vs 0.22).

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:30 UTC · 7d agoAI research1

DeepSeek v4.1 Flash Uncensored

Hugging Face user dealignai published an uncensored FP8-quantized variant of DeepSeek v4.1 Flash, drawing moderate Hacker News attention.

A community-published uncensored FP8 quantization of DeepSeek v4.1 Flash appeared on Hugging Face under user dealignai. The release is a third-party upload rather than an official DeepSeek launch, and no benchmark data or license details are provided in the listing. It received limited visibility, with 43 points and 11 comments on Hacker News.

Hacker News · AIupdated 12:19 UTC · 6h agofirst Sep 11, 06:46 UTC · 5d agoModel release 3 sources HN 43↑ · 11 comments1

You Can't Prefer Emotions You Don't Sample: Intensity Undershoot in DPO-Tuned LLMs

Study quantifies DPO-tuned LLMs undershooting requested emotional intensity, tracing the gap to candidate-pool extremity rather than conditioning format.

Conditioning an instruction-tuned LLM on continuous valence-arousal targets yields gain of only 0.26 for valence and 0.13 for arousal on Llama-3.1-8B, far below faithful control of 1.0. The authors attribute undershoot to neutral-heavy preference corpora like EmoBank and candidate pools lacking extreme affect, leaving DPO without extreme exemplars. Uniform target coverage with a hotter candidate pool raises valence gain to 0.40 on Llama-3.1-8B and 0.44 on Qwen3-8B, with modest in-distribution cost; arousal gains remain unstable across seeds.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:47 UTC · 9d agoAI research

Deep-Fake CAPTCHA: Mitigating Next-Generation Social Engineering Attacks

Researchers propose DF-CAPTCHA, a challenge-response defense that verifies callers in voice and video to defeat real-time deepfake impersonation in social engineering.

The DF-CAPTCHA framework prompts call participants with simple challenge-response tasks that are easy for humans but hard for real-time deepfake systems to convincingly generate. Responses are verified on four criteria: realism, identity consistency, task completion, and response time. User studies and experiments with real-time deepfake models across audio and video modalities show substantially improved detection over passive artifact-based methods.

arXiv cs.CRSep 10, 11:35 UTC · 6d agoResearch1

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:31 UTC · 7d agoAI research

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

Hugging Face daily papersSep 8, 20:00 UTC · 7d agoAI research1

Jackrong/Qwopus3.8-27B-Flash-GGUF — new model trending #26 on Hugging Face

Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.

Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.

Hugging Face trending modelsSep 4, 12:43 UTC · 12d agoModel release1

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Hugging Face published a tutorial on training and finetuning multi-vector embedding models using the Sentence Transformers library.

Hugging Face's blog walks through training and finetuning multi-vector embedding models with Sentence Transformers. Multi-vector approaches store multiple vectors per document to support late-interaction retrieval. The post is a practical guide for developers building retrieval pipelines with the library.

Hugging Face BlogAug 26, 00:00 UTC · 21d agoAI tools & infra1

New TCG guidance gives buyers a way to test PQC-ready TPM claims

Trusted Computing Group published PTP 1.07-based requirements defining PQC-ready and PQC-upgradable TPM designations to guide buyers assessing quantum-safe hardware.

The Trusted Computing Group published requirements establishing PTP 1.07, based on TPM 2.0 Library Specification Version 1.85, as the baseline for a 'TCG PQC-ready TPM', and defined a second designation, 'TCG PQC-upgradable TPM', for chips that can be upgraded to that support. The guidance lets buyers request vendor evidence that TPMs meet end-to-end post-quantum requirements for identities, attestation, and hardware-anchored trust rather than single-algorithm compliance. TCG also announced plans to extend its certification programs to certify TPMs meeting PTP 1.07, noting that 90% of businesses still lack a formal PQC roadmap.

Help Net SecurityAug 25, 00:00 UTC · 22d agoTools

Do speech foundation models really learn words?

Researchers show via residualization that later layers of HuBERT and wav2vec 2.0 encode word identity and semantics independently of phonetic content.

The study argues that discriminative ability on words does not imply specialized word representations, since good word discrimination can be explained by phoneme encoding alone. By partialling out phoneme information using residualization, the authors show that later layers of HuBERT and wav2vec 2.0 encode words with reasonable fidelity independently of local phonetic content. Applying this disentanglement approach enhances higher-order linguistic information in word discovery tasks, informing analysis of speech foundation models used for recognition and speech tokens.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:47 UTC · 7d agoAI research

Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models

Audits of 10 classifiers on BRFSS show target leakage, not model class, drives the reported 0.89 AUROC in survey-based cardiovascular screening.

The study benchmarks ten model classes, including glass-box and tabular foundation models, for prevalent myocardial infarction on 442,067 respondents of the 2022 BRFSS across five feature tiers of decreasing leakage risk. Removing two post-diagnostic features costs every model 0.049-0.051 AUROC and collapses performance into a 0.0045-wide band, and the explainable boosting machine matches all alternatives within 0.005 while scoring roughly 104x faster than the strongest foundation model. Frozen models transport within 0.002 AUROC to 2023 data; the authors conclude evaluation practice and feature sets, not model capacity, are the binding constraint.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:29 UTC · 6d agoAI research1

BreezeBlue/Breeze-TTS-2 — new model trending #19 on Hugging Face

BreezeBlue open-weights Breeze TTS 2, a bilingual text-to-speech model it ranks #1 among open-weight models on the Artificial Analysis TTS leaderboard.

BreezeBlue released open weights and Apache 2.0-licensed PyTorch inference code for Breeze TTS 2 on 2026-08-25. The text-to-speech model supports English and Chinese, voice cloning, reference-free voice design, voice direction, and inline vocal events like (laugh) and (sigh). Reported performance includes #1 open-weight ranking on the Artificial Analysis Elo leaderboard, under 40 ms time-to-first-audio, a 0.32 real-time factor on an NVIDIA H100, and about 7.7 GiB GPU memory for eager inference.

Hugging Face trending modelsAug 25, 16:39 UTC · 22d agoModel release

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Hugging Face details building and using multi-vector late-interaction embedding models with Sentence Transformers for retrieval workloads.

Hugging Face published a guide on multi-vector, late-interaction embedding models (ColBERT-style) supported through Sentence Transformers. The post covers how practitioners can build and use these models for retrieval and RAG pipelines. It is a developer tooling and technique write-up, not a security advisory.

Hugging Face BlogAug 18, 00:00 UTC · 29d agoAI tools & infra1

TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking

Researchers propose TFTrack, a template-free LiDAR 3D single object tracking framework cutting FLOPs ~50% while running at ~120 FPS.

TFTrack is the first template-free framework for 3D Single Object Tracking, dropping template-search pairings and complex motion modeling in favor of the prior bounding box center plus geometric alignment. It ships in three variants (TFTrack-Voxel, TFTrack-Pillar, TFTrack-Point) covering sparse and dense 3D representations. On KITTI and nuScenes it is competitive with leading template-based trackers while reducing FLOPs by about 50% and running near 120 FPS. Code is released, targeting real-time deployment in embedded robotics such as autonomous vehicles.

arXiv cs.AI / cs.LG / cs.CLSep 7, 16:43 UTC · 9d agoAI research

Next.js 16.4.0-canary.13 Image Optimizer DNS Rebinding TOCTOU SSRF Still Exists

Next.js 16.4.0-canary.13 Image Optimizer retains a DNS rebinding TOCTOU SSRF in fetchExternalImage() allowing requests to private network resources.

Next.js 16.4.0-canary.13 contains a DNS rebinding time-of-check-to-time-of-use Server-Side Request Forgery vulnerability in the Image Optimizer's fetchExternalImage() function. The code resolves the supplied hostname and checks the resulting addresses with isPrivateIp(), but a TOCTOU gap between resolution and the actual fetch allows an attacker-controlled DNS record to change and reach private network resources.

Full DisclosureSep 4, 00:13 UTC · 12d agoVulnerability

HuggingFace: Security.txt

Hugging Face published a security.txt file, prompting limited Hacker News discussion of the RFC 9116 disclosure standard.

Hugging Face's security.txt file, which lists its security contact and disclosure channels per the RFC 9116 standard, drew attention on Hacker News. The RFC 9116 standard lets organizations publish where and how security researchers should report issues, but the submission received only one comment.

Hacker News · securityupdated Sep 11, 16:04 UTC · 5d agofirst Sep 11, 14:38 UTC · 5d agoIndustry 2 sources HN 22↑ · 1 comments