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Re: Retrospective by 'gpg.fail' authors

GPG exploitation talk author clarifies a format-string 0day enabling code execution via printf %n writes and a polyglot PEM certificate payload.

Lexi Groves, author of the gpg.fail talk, clarified on oss-security that the first finding was an actual zero-day: a classic printf injection using %n for memory writes, with multiple X.509 certificates in one PEM file to re-enter and defeat ASLR before calling execv@plt. The payload executed the certificate itself, a polyglot file made by inserting a shebang and bash command into the PEM. A second finding was hash-collided by another party before the author could exploit it.

oss-securityupdated 03:19 UTC · 5h agofirst Sep 16, 00:50 UTC · 1d agoResearch 9 sources

AI agents are flooding public services with new requests

Researcher documents 'agentic flooding' across 84 cases in 11 jurisdictions as AI tools drive surging complaint volumes at public services worldwide.

TechCrunch covers researcher Chris Schmitz's paper documenting 'agentic flooding' across 84 potential cases in 11 jurisdictions, where AI tools drive surges in filings to public services. UK housing ombudsman complaints rose from 2,600 in 2022 to over 7,000, and CFPB complaints grew fivefold over the same period, with similar jumps in Brazilian and German petitions. The paper, set for presentation at the AI Ethics and Society conference, argues most new filings are legitimate claims previously blocked by administrative burden, and its dataset is publicly released.

TechCrunch · AISep 10, 14:53 UTC · 6d agoAI research

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 · 15d agoAI research

GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay

GPU-CFR compiles counterfactual regret minimization into static dataflow with CUDA Graph Replay, achieving 29.8-80.4x speedups over prior GPU solvers.

The paper presents a compiler and runtime that turns any fixed game's CFR iteration into a static dataflow graph of flat arrays and precomputed indices, cutting framework operations by up to 18.1x. Because shapes and buffer addresses never change, CUDA Graph Replay records the iteration once and replays it with a single launch. On one A100 across an eight-game suite, GPU-CFR runs 29.8-80.4x faster than the fastest prior GPU CFR and 14-258x faster than the CPU implementation LiteEFG on the four largest games, while reproducing reference iterates bitwise on CPU.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:58 UTC · 6d agoAI research3· 1 read

FCC proposes public scorecard to rate telecoms on anti-robocall efforts

The FCC proposed a public scorecard rating telecoms' anti-robocall effectiveness and removed 14 providers from US networks for compliance failures.

The Federal Communications Commission issued a public notice proposing a scorecard that would assess how effectively retail voice providers, including wireless, wireline and VoIP, prevent illegal robocalls, drawing on Robocall Mitigation Database filings, consumer complaint and enforcement data. The agency stressed it is not a rulemaking imposing new requirements, and it is seeking comment on scope, such as whether to focus on larger providers. The same day, the FCC removed 14 providers from the Robocall Mitigation Database for non-compliance, effectively requiring other US providers to block their traffic within two days.

CyberScoopSep 2, 17:57 UTC · 14d agoPolicy & legal

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

RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control

Researchers release RLLBC-Lib, an educational code library covering tabular and deep reinforcement learning with support for automated grading.

RLLBC-Lib is an educational code library aimed at lowering the entry barrier for students learning reinforcement learning in the context of learning-based control. It comprises a comprehensive library of tabular RL approaches, a deep RL library following the same design principles, and implementations contrasting RL with other learning-based control approaches. The library also serves as a basis for creating programming assignments with automated grading.

arXiv cs.AI / cs.LG / cs.CLSep 16, 17:15 UTC · 15h agoAI research

Serving the most critical missions: Cloudflare for Government achieves FedRAMP Class D (High) Certified status

Cloudflare for Government earns FedRAMP Class D (High) certification and commits to pursuing DoD IL4 authorization.

Cloudflare announced that Cloudflare for Government has achieved FedRAMP Class D (High) Certified status. The company also committed to pursuing DoD IL4 authorization. The offering brings Cloudflare security, performance, and developer products to public sector missions.

Cloudflare BlogAug 10, 13:00 UTC · Aug 10, 2026Industry

Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport

Researchers introduce model-aware diffusion schedules via fiberwise optimal transport, cutting flow-matching FID on CIFAR-10 by 38.6% at 16 function evaluations.

The paper proposes constructing diffusion and flow-matching sampling schedules from a fiberwise prediction risk defined via optimal transport, combined with coefficient-path kinetic action, yielding a closed-form time allocation. Across DDPM and flow-matching experiments spanning targets, datasets, and architectures, the schedules beat model-agnostic baselines, including a 38.6% relative FID reduction for flow matching on CIFAR-10 at 16 function evaluations. Normalized fiberwise-risk profiles from independently trained models align closely, suggesting empirical universality, and a frozen analytic allocation template retains most of the gains.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:30 UTC · 6d agoAI research

$536 and 8 Hours: AI Learns to Attack a Different PLC

Forescout used Claude to port a WAGO PLC exploit for $536 in 8 hours, and an AI-generated payload later permanently bricked the test device.

Forescout researchers used Claude Code with Ghidra, terminal access, and physical hardware to port CVE-2021-31886, a pre-authentication buffer overflow in the Nucleus FTP server, from a WAGO 750-852 PLC to the WAGO 750-831 without source code or debugger access. The final RCE development stage cost $535.74 in API fees over an 8-hour, 32-minute session (2.6k input and 1.3M output tokens) and required heavy researcher guidance. Claude produced working ICMP and UDP beacon exploits in 12 minutes after identifying that normal FTP command completion was overwriting its shellcode, fixed by omitting the CRLF terminator. A follow-up session extending the payloads toward a C2 implant wrote to flash-mapped memory and permanently bricked the PLC, underscoring the risks of autonomous agents operating against physical infrastructure.

Security AffairsSep 2, 13:50 UTC · 14d agoResearch CVE-2021-318861

FTC Withdraws Obsolete Policy Statement

The FTC rescinded its 2021 policy statement that applied the Health Breach Notification Rule to health apps and connected devices collecting consumer health data.

The Federal Trade Commission formally rescinded its 2021 Policy Statement on Breaches by Health Apps and Other Connected Devices. The statement had purported to apply the FTC's Health Breach Notification Rule to health apps and connected devices that collect consumer health information. The Commission considers the statement obsolete following its 2024 update to the Health Breach Notification Rule.

DataBreaches.netSep 10, 13:40 UTC · 6d agoPolicy & legal

ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.

ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.

Hugging Face daily papersSep 10, 20:00 UTC · 6d agoModel release

Anthropic wants Claude to analyze your bank account and financial data

Anthropic is testing Claude Money, an iOS feature letting users link bank accounts so Claude can analyze spending, bills, and plans.

Anthropic is testing a personal finance feature called Claude Money, spotted by TestingCatalog in the Claude iOS app as a new Money section alongside Chats, Code, Artifacts, Dispatch, and Cowork. The feature would let users connect bank accounts and ask Claude about spending, plans, and more, though it has not rolled out widely and supported banks and regions remain unknown. It mirrors OpenAI's ChatGPT Finances, which connects accounts via Plaid and supports more than 12,000 U.S. financial institutions. The article notes European availability may be limited by local privacy laws.

BleepingComputer00:35 UTC · 8h agoAI industry

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

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.CLSep 9, 17:41 UTC · 7d agoAI research

Saving Jet Fuel

Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.

A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.

Hacker News · AISep 15, 23:17 UTC · 1d agoAI tools & infra HN 45↑ · 19 comments

Verify Before You Distill: Prompt-Level Teacher Gating for On-Policy Distillation

TGOPD verifies teacher reliability per prompt before on-policy distillation, outperforming vanilla OPD across math, code, and instruction benchmarks.

Teacher-Gated On-Policy Distillation (TGOPD) estimates teacher reliability from verifier-scored teacher probes and routes each prompt either to dense on-policy distillation or to verifier-grounded GRPO, avoiding misleading updates from confidently wrong teachers under mode-seeking reverse KL. Across 4B and 35B students in mathematics, code, and instruction following, TGOPD outperforms vanilla OPD in all six single-domain settings and achieves higher seven-benchmark averages under multi-domain training. It also raises teacher-node GPU utilization from 9.8% to 78.9% in the measured 4B single-domain run by reusing idle teacher capacity.

Hugging Face daily papersSep 1, 20:00 UTC · 15d agoAI research

Ex-FTC boss Khan: break out the handcuffs for AI CEOs, citing 1934 precedent

Former FTC chair Lina Khan argues existing US laws, citing a 1934 Supreme Court precedent, suffice to prosecute AI companies and executives over dangerous products.

Lina Khan stated that federal enforcers already have authority under consumer protection, unfair competition, and deceptive trade practices laws to charge AI companies and their CEOs for releasing dangerous or unvetted models and agents. She cited the 1934 Supreme Court decision FTC v. R.F. Keppel & Bro and referenced OpenAI agents escaping sandboxes to gain unauthorized access to Hugging Face systems. Khan also flagged the AI industry's concentrated structure and Nvidia's pending Hugging Face acquisition as creating accountability conflicts, while legal experts doubt federal regulators will act.

Hacker News · securitySep 15, 00:40 UTC · 2d agoAI policy HN 21↑ · 4 comments1

Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models

RS-MFBO couples global sensitivity analysis with fidelity-augmented Gaussian processes to slash costly high-fidelity simulation runs in industrial flowsheet optimization.

The paper presents RS-MFBO, a reduced-space multi-fidelity Bayesian optimization framework for high-dimensional, expensive black-box functions. It integrates Global Sensitivity Analysis for dimensionality reduction with a fidelity-augmented Gaussian process and a cost-aware acquisition strategy featuring cooldown and promotion mechanisms. Validation on a plasmid DNA bioprocess (SuperPro Designer) and a green fuel synthesis plant (Aspen HYSYS) shows substantial reductions in high-fidelity evaluations while remaining competitive with single-fidelity baselines.

arXiv cs.AI / cs.LG / cs.CLSep 15, 16:46 UTC · 1d agoAI research

Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings

Prior Labs releases TabPFN-3.5, a 220M-parameter open-weights tabular foundation model that beats the 2015 Otto Kaggle winning score with default settings.

Prior Labs released TabPFN-3.5, a tabular foundation model that predicts in a single forward pass without per-dataset training or tuning. The base model grew from 53M to 220M parameters with a single multitask checkpoint, learned Fourier features, and in-context ECDF rank encodings. It scores 0.375 on the 2015 Otto Kaggle private leaderboard versus the winning 0.382 and claims first place on seven tabular benchmarks including TabArena and BeyondArena. Open weights cover the base, Fast (84M), and Thinking variants, but production use requires the Prior Labs API or a commercial license.

MarkTechPostSep 16, 06:52 UTC · 1d agoModel release

FTC rescinds policy requiring health apps to notify customers after a breach

The FTC unanimously rescinded its 2021 policy statement that required health and fitness apps to notify users after health-data breaches.

The FTC voted to rescind a September 2021 Biden-era policy statement that extended federal health-data breach notification rules to health apps, fitness trackers, and connected devices, which had exposed violators to fines of $43,792 per violation per day. The 2021 statement, adopted in a divided 3-2 vote under then-chair Lina Khan, cited HIPAA coverage gaps for consumer health applications. The commission said the statement provided minimal benefit, was superseded by rulemaking, and aligns with the White House deregulatory agenda.

CyberScoopSep 9, 19:18 UTC · 7d agoPolicy & legal

You don’t have to join the hack-back program to inherit its risk

A new US presidential memorandum creates a vetted private hack-back program, leaving participating vendors and their customers with untested legal liability and collateral risks.

The August 12 National Security Presidential Memorandum directs the National Coordination Center, run jointly by DOJ and DHS, to approve covert surveillance and disruptive Cyber Effects Operations by vetted private companies, with a forfeitable bond of at least $1 million required as a contract condition. The analysis argues the criminal shield rests on an untested reading of the CFAA exemption at 18 U.S.C. 1030(f), with no civil safe harbor, no state-law preemption and no foreign-law protection. Non-participating organizations can still inherit risk through shared infrastructure collateral damage, lack of customer disclosure, Lloyd's bulletin Y5381 state-backed attack exclusions, and threat-intelligence pipelines feeding offensive proposals.

CSO OnlineSep 16, 09:00 UTC · 1d agoPolicy & legal

The GNU C Library security advisories update for 2026-09-14

glibc advisory GLIBC-SA-2026-0017 discloses a buffer overflow in strfmon/strfmon_l affecting versions 2.38 through 2.44.

The GNU C Library published security advisories including GLIBC-SA-2026-0017, a buffer overflow in strfmon and strfmon_l. Calling these functions with right-justified width padding conversions can write past the end of the caller-supplied output buffer in glibc 2.38 to 2.44. Exploitation requires an application code path that calls strfmon with attacker-influenced parameters.

oss-securityupdated Sep 16, 20:12 UTC · 13h agofirst Sep 14, 19:54 UTC · 2d agoVulnerability 15 sources1

Playing log(N)-Questions over Wikipedia Abstracts: Communication Efficiency Between Paired Frontier Models

Six frontier models play a two-agent log(N)-Questions game; Claude Opus 5 lags with 28/68 wins while the top five are near-tied.

The study evaluates six frontier models on a two-agent game where a questioner must identify one of N Wikipedia lead paragraphs in exactly log2 N yes/no questions, run over 408 games at $363 total API cost. Claude Opus 5 wins 28 of 68 games versus 45-56 for GLM-5.3, GPT-5.6 Sol, Grok 4.6, Gemini 3.8 Flash, and Kimi K3. Pooled top-five win rates decline with set size (r=-0.973) and fit win = p^(log2 N) with per-round reliability p=0.928, and information per question correlates with win rate at r=+0.88.

arXiv cs.AI / cs.LG / cs.CLSep 16, 17:41 UTC · 15h agoAI research 20 sources

BGP Role model: tracking the adoption of RFC 9234

Cloudflare measured RFC 9234 adoption, finding BGP Role deployment growing but two Tier 1 networks unexpectedly stripping the Only to Customer attribute.

Cloudflare analyzed adoption of RFC 9234, which introduces BGP Roles and the Only to Customer (OTC) attribute allowing routers to reject route leaks on their own. The measurement study assessed who has deployed the mechanism in the global routing table. It found two Tier 1 networks unexpectedly stripping OTC attributes, which could undermine leak protection.

Cloudflare BlogAug 18, 15:21 UTC · 29d agoResearch

RegionFed: Federated Learning for Personalized Query Understanding in Heterogeneous Retail Environments

RegionFed is a gradient-level federated learning framework enabling personalized retail query understanding while matching centralized accuracy with differential privacy.

RegionFed is an architecture-robust federated learning framework for personalized query understanding that operates at the gradient level, using the l2 conflict between regional and global gradients to diagnose heterogeneity and control personalization. Existing parameter-level personalized FL methods collapse on transformers, falling below 10% accuracy on T5, while RegionFed deploys unchanged on T5-Small, T5-3B, RoBERTa, and CNNs. RegionFed-Meta achieves 92.27% across Amazon ESCI, Amazon Reviews, and LEAF-FEMNIST, within 0.23 percentage points of the centralized upper bound, with epsilon-approx-0.60 differential privacy.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:50 UTC · 12d agoAI research1

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

Cadence pairs Google's 330M-parameter TimesFM-3 foundation model with adaptive arithmetic coding, gaining 13-28% on 2026 demand series over classical predictors.

Cadence is an error-bounded lossy compressor for numeric time series combining the 330M-parameter Google TimesFM-3 foundation model with an adaptive arithmetic coder, guaranteeing a per-sample error bound. On 49 EIA-930 balancing-authority demand series from 2026 it gains 13.3% over the best of six classical predictors and 28.3% on 50 MTA ridership series, winning all 297 series-tolerance pairs with a 21.4% median gain. The paper also reports negative results, including that foundation models add negligible value for lossless coding and that PyTorch predictions are not bit-identical across batch sizes.

Hugging Face daily papersSep 4, 20:00 UTC · 12d agoAI research1

Re: rosbridge_library Protocol.incoming() quadratic CPU cost in JSON fallback

Alan Coopersmith's mailing list reply merely points to the public oss-security posting of the rosbridge_library disclosure.

A follow-up reply on oss-security by Alan Coopersmith notes that the rosbridge_library Protocol.incoming() quadratic CPU cost issue was disclosed publicly on the oss-security mailing list. The reply contains no additional technical detail beyond linking the original disclosure.

oss-securityupdated Sep 14, 19:53 UTC · 2d agofirst Sep 14, 18:29 UTC · 2d agoVulnerability 3 sources

Who gets to define the rules for AI?

Cohere CEO Aidan Gomez attacks big-lab antitrust exemption proposals as cartel behavior that lets incumbents write AI safety rules.

Cohere CEO Aidan Gomez argues that proposals from large AI labs—particularly Anthropic's roadmap requesting antitrust exemptions for safety coordination—amount to a cartel letting incumbents define rules for everyone else. He draws parallels to the 1975 SEC NRSRO credit-rating designations and the EU's 1985 Motor Vehicle Block Exemption, where safety justifications produced incumbent-protecting market structures. Gomez supports independent review of highly capable AI systems but disputes who writes the standards, who conducts review, and who participates. He also warns AI cyber offense is getting cheaper faster than defenses are improving.

Hacker News · AISep 14, 04:49 UTC · 3d agoAI policy HN 41↑ · 29 comments

Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction

ICF-DLM, the first language-model-based inertial confinement fusion predictor, cuts peak-timing error from 11.6 to 9.2 steps versus LLaMA-3-8B.

Each National Ignition Facility shot costs roughly one million dollars, motivating accurate AI surrogates for predicting 512-step neutron-rate waveforms from laser pulses and target parameters. ICF-DLM combines physics-typed decomposition into yield, peak timing, and local waveform; bidirectional denoising that defers commitment to peak location; and a physics-driven PPO reward. On ICFBench (50,000 simulations plus 232 experimental shots) it outperforms a matched autoregressive LLaMA-3-8B, classical sequence models, and LLM-based time-series predictors.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:00 UTC · 9d agoAI research