Search: “MLflow”

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DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll Vulnerability 6 Exploit / PoC 3 Malware 1 Model release 1 Policy & legal 1 Research 1 Threat actor 1 Phishing & fraud 1 Industry 1

SeverityAll critical 7 high 5 medium 0 low 2 info 2

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U.S. CISA adds a MLflow flaw to its Known Exploited Vulnerabilities catalog

CISA added actively exploited MLflow SSRF flaw CVE-2026-64849 (CVSS 9.3) to its KEV catalog; attackers are stealing cloud credentials from exposed instances.

CISA added CVE-2026-64849, a critical unauthenticated server-side request forgery in MLflow, to its Known Exploited Vulnerabilities catalog. The flaw affects MLflow versions before 3.15.0 and allows unauthenticated attackers to reach internal services including cloud metadata endpoints, exposing temporary cloud credentials. watchTowr observed in-the-wild exploitation exfiltrating credentials and secrets, plus widespread scanning of exposed MLflow instances within hours of the CVE's assignment on August 17, 2026. MLflow is an open-source platform for managing the machine learning and AI development lifecycle with over 60 million monthly downloads.

Security AffairsAug 20, 08:58 UTC · 27d agoExploit / PoC in the wildCVE-2026-64849

Attackers Exploit MLflow SSRF Flaw to Steal Cloud Credentials and Secrets

Attackers exploit MLflow SSRF CVE-2026-64849 (CVSS 9.3) to steal cloud credentials; CISA added it to KEV; FUXA flaw CVE-2026-25895 is being scanned.

watchTowr observed exploitation of MLflow CVE-2026-64849, an unauthenticated SSRF (CVSS 9.3) affecting versions below 3.15.0, within hours of CVE assignment on August 17, 2026, with attackers abusing model-registry webhooks to reach cloud metadata endpoints and exfiltrate credentials and secrets. CISA added the flaw to its Known Exploited Vulnerabilities catalog on August 19, 2026, with a September 2 patch deadline for federal civilian agencies. VulnCheck reported scanning of FUXA CVE-2026-25895 (missing authentication plus path traversal, CVSS 9.5, versions through 1.2.9) beginning August 18; about 60 FUXA instances are exposed and no RCE payloads have been dropped yet.

The Hacker NewsAug 20, 05:48 UTC · 27d agoExploit / PoC in the wildCVE-2026-64849 CVE-2026-25895 CVE-2026-25939+1 CVEs

Risky Bulletin: Academics find source code overlaps between Geedge and China's Great Firewall

Academics linked Chinese vendor Geedge Networks' Tiangou Secure Gateway source code to one of the Great Firewall's three traffic filtering capabilities.

US researchers presenting at USENIX Security reconstructed Geedge Networks' Tiangou Secure Gateway firmware from over 100,000 leaked files, including Git repositories with commit history, and matched its filtering behavior to sections of China's Great Firewall. They found only 1 of 3 characterized DNS injectors matched Geedge code, noted the system relies on memory-unsafe C components and copied third-party code, and said its bugs could aid future circumvention tools. Geedge also exports censorship tools to Kazakhstan, Ethiopia, Pakistan, and Myanmar. The newsletter additionally rounds up multiple breaches.

Risky Business NewsAug 21, 02:55 UTC · 26d agoResearch2

FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation

FreeFlow is a bias-free hierarchical transformer achieving state-of-the-art optical flow results on Sintel, KITTI-2015, and Spring benchmarks.

FreeFlow replaces task-specific inductive biases like correlation volumes and iterative warping with a single feed-forward encoder-decoder combining window, shifted-window, and reduced-resolution global attention. It reaches 0.68/1.48 EPE on Sintel Clean/Final, 3.23 Fl-all on KITTI-2015, and 3.192 1px on Spring. The architecture scales consistently from small to large variants and remains memory efficient at 1080p inference.

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

August 2026 CVE Landscape

Insikt Group catalogs 73 high-impact August 2026 CVEs (43 Very Critical), including PaperCut, Zimbra, and Metabase flaws actively exploited or weaponized.

Recorded Future's Insikt Group identified 73 high-impact vulnerabilities in August 2026, 43 rated Very Critical, spanning 45 vendors with Microsoft accounting for roughly 11%. 31 vulnerabilities surfaced via CISA's KEV catalog, with others validated via open sources, vendor telemetry, and honeypot data. New Nuclei detection templates were released for CVE-2025-62593 (Ray), CVE-2026-72898 (Metabase), and CVE-2026-9198 (IBM Langflow). The report also highlights two AI-assisted operations: UAT-10147 exploited Zimbra, AjaxPro, Nacos, and Telerik servers before using DeepAudit and PentestGPT post-compromise, while a separate Chinese-speaking actor weaponized Hermes Agent and DeepSeek in a failed attempt.

Recorded FutureSep 8, 00:00 UTC · 8d agoVulnerability in the wildCVE-2025-62593 CVE-2026-72898 CVE-2026-9198+4 CVEs

MIT creates method to force AI to comply with safety rules

MIT researchers published HardFlow, a method enforcing hard safety constraints on flow-matching generative models' final outputs without retraining.

MIT researchers led by Zeyang Li and Navid Azizan developed HardFlow, a trajectory-optimization method that enforces strict, non-negotiable constraints on flow-matching generative models by checking rule satisfaction only at the final generation step. Published in IEEE TPAMI, it outperformed six rival projection and guidance methods on four simulated benchmarks including D3IL robotic manipulation, Maze2D, physical process control, and image editing. All results are simulation-only, with no independent reproduction yet reported.

Hacker News · securitySep 14, 16:18 UTC · 1d agoAI research HN 24↑ · 25 comments

⚡ Weekly Recap: AI-Powered PLC Attacks, GitLab Attacks, Stripe Key Leaks and More

US agencies warn of AI-assisted attacks on exposed Siemens PLCs; the week also saw GitLab CVE-2026-19478 exploited and trojanized npm packages found.

The weekly recap leads with a US government warning that threat actors use AI-generated scripts and Censys/ZoomEye scanning to attack internet-exposed Siemens S7 PLCs in water, energy and manufacturing, calling it an active threat. Other stories include active exploitation of GitLab CVE-2026-19478 (CVSS 9.4, unauthenticated project rewriting), 14 trojanized npm packages delivering the RedC2 4.0 Linux backdoor, and the Zombie Card attack that revives expired Visa cards for contactless payment fraud. It also covers suspected Russian clusters UNC6293, UNC7005 and UNC5976 phishing campaigns, a faster Cloudflare Workers Spectre JWT leak, and a bespoke Cl0p JSP web shell deployed after exploiting PTC Windchill flaws.

The Hacker NewsAug 28, 08:05 UTC · 19d agoThreat actor in the wildCVE-2026-194781

IBM security advisory (AV26-922)

Canadian Cyber Centre relays IBM advisory for Langflow, MQ, and Sterling File Gateway flaws including MQ remote code execution (CVE-2026-13293).

Canadian Cyber Centre advisory AV26-922 relays IBM fixes for Langflow OSS (versions through 1.11.5 across release lines), IBM MQ (10.0.0.0 and 9.x LTS/CD through 9.4.5.1), and Sterling File Gateway (through 6.2.2.1). CVE-2026-13293 is a remote code execution flaw in IBM MQ Java messaging caused by an incomplete security scanner blocklist enabling network-based code execution. CVE-2026-19290 is an improper access control vulnerability in IBM Sterling File Gateway. Administrators are urged to review and apply the necessary updates.

Canadian Centre for Cyber SecuritySep 15, 14:29 UTC · 20h agoAdvisory CVE-2026-13293 CVE-2026-19290

[webapps] Langflow 1.10.0 - RCE

A public proof-of-concept demonstrates remote code execution in Langflow 1.10.0, the open-source AI workflow builder.

Exploit-DB lists a remote code execution proof of concept targeting Langflow version 1.10.0. Langflow is a widely used open-source tool for building AI agent and LLM workflows. The listing does not include a CVE id or evidence of in-the-wild exploitation, but RCE in AI development tooling is relevant to teams running it in production.

Exploit-DBSep 2, 00:00 UTC · 14d agoExploit / PoC1

Embedded Graph Flows for Categorical Graph Generation

Researchers propose Embedded Graph Flows, a generative model with learned categorical embeddings that beats DiGress and GruM on molecular graph benchmarks.

Embedded Graph Flows (EGF) learns continuous embeddings for node and unordered-edge categories and transports Gaussian noise toward these endpoints using a permutation-equivariant graph transformer. On QM9 it achieves the best result on all four reported metrics, with a Fréchet ChemNet Distance of 0.150 versus 0.717 for DiGress and 0.812 for GruM. On ZINC250k it retains the lowest NSPDK MMD, indicating close agreement with local substructures of reference molecules. Code is released on GitHub.

arXiv cs.AI / cs.LG / cs.CLSep 4, 16:26 UTC · 11d agoAI research1

Model ML completes finance work more efficiently with GPT-5.6 Sol

OpenAI customer Model ML uses GPT-5.6 Sol to turn finance research into editable, traceable decks and workbooks.

OpenAI published a customer story describing how Model ML uses GPT-5.6 Sol for finance work. The model carries tasks from research and analysis through to editable, traceable PowerPoint decks and Excel workbooks. This is a product adoption case rather than a new model release.

OpenAI NewsAug 10, 12:00 UTC · Aug 10, 2026AI industry

JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management

JustFit MLX runtime serves 200K-token contexts for Qwen3.8-27B on a 24 GiB MacBook via just-in-time state management.

JustFit is an MLX-based inference runtime combining KVExec for compressed KV execution, PhaseSwap for component residency, and StateTrans for state-preserving serving transitions, independent of weight quantization. On a 24 GiB M4 Pro MacBook running Qwen3.8-27B MXFP4, it completed 196,608 input and 16,384 output tokens, raising single-request context from the mlx-vlm baseline's 30,720 positions to 212,992 (6.93x). Performance tests show 19.11 tokens/s on a 32K-input probe with a 16,374 MiB median peak footprint, and the runtime answered 29 of 30 AIME 2026 problems correctly.

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

Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference

Hands-on tutorial implements NVIDIA cuML and RAPIDS to GPU-accelerate scikit-learn-style ML workflows with benchmarking, clustering, and inference.

The tutorial demonstrates NVIDIA cuML as a GPU-accelerated machine learning framework, using cuml.accel to speed up unmodified scikit-learn scripts with zero code changes and the native cuML API for CuPy/cuDF interoperability. It benchmarks CPU versus GPU implementations of PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN on datasets up to 200,000 samples with 64 features. It also builds GPU pipelines with UMAP, t-SNE, and HDBSCAN, validates GPU-generated SHAP explanations, uses the FIL library for forest inference, and covers model serialization and GPU/CPU portability.

MarkTechPostSep 13, 01:42 UTC · 3d agoAI tools & infra

HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning

HybridFLow uses SDN topology visibility to partition federated-learning clients into sync/async groups, reaching 80% accuracy 33-40% faster than SmartFLow.

HybridFLow is a closed-loop, SDN-driven orchestration framework for hybrid federated learning that integrates network-layer intelligence into cross-silo training. It leverages the SDN controller's global topology view to generate calibrated per-client communication-time estimates, partitioning clients into synchronous and asynchronous groups while balancing round latency and update staleness, with measured times fed back after each round. Across multiple network topologies it reaches 80% target accuracy 33-40% faster than SmartFLow and cuts average round duration by 30-40 seconds, while FedAsync fails to reach target accuracy under non-IID data.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:23 UTC · 6d agoAI research

Likelihood-free inference with nuisance parameters through normalizing flows

Researchers decompose normalizing flows to derive near-pivotal statistics for likelihood-free inference with nuisance parameters, recovering the t-test and beating Welch limits.

A new paper decomposes neural-network normalizing flows to uncover pivotal statistics in the presence of nuisance parameters using only a sample generator from the distribution of interest. The statistic is near-pivotal in the sense of minimum average KL-divergence of its p-values and can incorporate prior knowledge of group invariances such as translation and scale. Experiments show it recovers the one-sample t-test almost exactly, outperforms the Welch test on worst-case size over a constrained variance-ratio range, and delivers higher power and much faster runtime than profile likelihood-ratio techniques on small-to-moderate samples.

arXiv cs.AI / cs.LG / cs.CLSep 9, 17:58 UTC · 6d agoAI research

HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving

HoneyRoute detects malicious LLM serving requests and diverts them to a honeypot model, reaching F1 0.911 with 38 ms median added latency.

HoneyRoute is an inference-serving layer pairing a streaming router (a frozen 0.8B embedding backbone with per-domain MLP heads) with a dual-implementation honeypot and an analysis loop that converts trapped interactions into attacker fingerprints for router retraining. On a production trace plus a seven-domain attack corpus it matches 96% of a two-tier guard-LLM cascade's F1 at 1/385th of its latency with 0% evasion under 13 adversarial transformations. Diverting malicious traffic cuts production token consumption under GCG-suffix flooding by 97.8%, and loop training raises detection F1 to 0.933.

arXiv cs.CRSep 8, 06:26 UTC · 8d agoAI safety & security

FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience

FlowBalance is a verifier-grounded self-improvement method that beats FlowRL on Qwen3-4B and Qwen3-8B math reasoning while improving training stability.

FlowBalance calibrates dense self-guidance scores with verifier-derived group advantages: guidance is retained on positive-advantage trajectories, reversed on negative-advantage trajectories, and disabled when rollout groups show no outcome preference. The method exponentially reweights a reference policy via trajectory balance, with guarantees including within-group contrast preservation and a minimum-change reverse-KL characterization. On mathematical reasoning it outperforms FlowRL on Qwen3-4B and Qwen3-8B, trains faster and more stably, avoids direct OPSD's response-length collapse, and shows higher correct-strategy diversity on AIME24.

Hugging Face daily papersSep 2, 20:00 UTC · 13d agoAI research

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 · 13d agoAI tools & infra

Critical Langflow Vulnerability Exploited as Attacks on AI Platform Rise

Attacks exploiting CVE-2026-0768, a critical vulnerability in the Langflow low-code AI platform, are rising amid growing adversary attention this year.

CVE-2026-0768 is a critical vulnerability in Langflow, a low-code AI development platform, with exploitation attacks now rising. Dark Reading notes the platform has drawn increasing adversary attention in 2026. Organizations running exposed Langflow instances face elevated risk and should patch promptly and review instances for compromise.

Dark ReadingSep 1, 20:48 UTC · 14d agoExploit / PoC in the wildCVE-2026-07681

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

Intezer adds native response automation without separate SOAR

Intezer launched Workflows, native response automation inside its AI SOC, letting teams automate remediation without a separate SOAR platform.

Intezer announced Workflows, a native automation and response builder inside its AI SOC platform that lets security teams run post-investigation actions such as closing alerts, isolating hosts, and updating tickets without a separate SOAR. Workflows are created through natural language via MCP, inherit full investigation context, and are logged for audit, with per-tenant routing and customer communications aimed at MSSPs. The announcement cites Intezer's AI SOC Report 2026 finding that nearly 1% of real incidents trace back to lowest-severity alerts.

Help Net SecurityAug 19, 00:00 UTC · 28d agoTools

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

Researchers present SMART, an ML performance-modeling library regenerated by AI coding agents from natural-language design docs instead of code.

The paper describes SMART, a symbolic performance-modeling library whose main branch contains almost no code: the repository is a DAG of self-contained design documents, and coding sub-agents regenerate implementations from only the docs on version updates. Reliability rests on a worked-example doc style used as in-context demonstrations and a minimal operator IR with SymPy cost expressions, offering both fast analytical roll-up and fine-grained modulo-scheduling modes. Regenerated implementations reproduce hand-audited reference models, including DeepSeek-V3 serving on a TPU pod slice, to round-off precision.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:08 UTC · 11d 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 · 11d agoAI research1

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Open-source speech foundation model AuK unifies generation and editing, trained on 1.95 million hours, with distilled AuK-Flash achieving 4.5x speedup.

AuK is an open-source foundational model that unifies speech generation and editing through natural-language instructions and audio context, trained on approximately 3.03 billion instruction-audio instances and 1.95 million hours of supervision across five task families including generation, content editing, and acoustic editing. It combines a multimodal LLM for semantic conditioning, a VAE jointly trained on speech, general audio, and music, and a hybrid rectified-flow Transformer using dual-stream MMDiT blocks followed by unified single-stream DiT blocks. Post-training applies human-feedback preference optimization for editing and reward-based reinforcement learning for generation, and the distilled AuK-Flash performs 4-step inference without classifier-free guidance at a 4.5x wall-clock speedup. Source code and model weights are released.

Hugging Face daily papersSep 7, 20:00 UTC · 8d agoModel release1

MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling

MCRL2 augments reinforcement learning with multi-resource cross-attention representations to improve cloud microservice scheduling and load balancing.

MCRL2 combines a multi-resource cross-attention representation learning module (MCRL) with an actor-critic architecture and maximum entropy objective for microservice scheduling. The approach captures interdependencies among nodes, resources, and microservices in data centers. Experiments on real production cluster traces show improvements in load balancing, scheduling success rate, and average completion time versus baselines.

arXiv cs.AI / cs.LG / cs.CLSep 11, 16:44 UTC · 4d agoAI research1

Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks

Survey of four harness mechanisms—context budgeting, compaction, todo-state, and memory—that keep long-horizon LLM agents on task across 200+ tool calls.

The article details how agent harnesses, not larger context windows, solve context overflow and goal loss on long-horizon tasks, citing Chroma's Context Rot report showing 18 LLMs (GPT-4.1, Claude 4, Gemini 2.5, Qwen3) degrade on long inputs. Concrete implementations include LangChain Deep Agents offloading tool responses over 20,000 tokens to the filesystem and truncating old tool calls at 85% window usage, and Claude Code capping auto memory at 25KB while re-reading the 5 most recently modified files after compaction. OpenAI's Responses API now offers server-side compaction via context_management with a standalone /responses/compact endpoint, which Codex uses for long-running coding tasks. Manus reports a roughly 100:1 input-to-output token ratio per ~50-tool-call task, motivating todo.md state recitation to prevent goal drift.

MarkTechPostSep 13, 05:56 UTC · 3d agoAI research1

Hugging Face's new ML Intern lets anyone run machine learning experiments through a simple chat

Hugging Face launched ML Intern, a chat-based agent that autonomously selects models, trains, and ships demos within user-approved compute budgets.

Hugging Face's ML Intern lets users describe a project in chat, then finds models, datasets, and tools on the Hub, GitHub, and the web before estimating compute costs and enforcing an approved budget. It autonomously creates datasets, trains models, monitors jobs, uploads results, writes reports, and builds demos, each with its own tracking dashboard. A demo training run took about six hours and cost under $0.50. The launch comes as Hugging Face is being acquired by Nvidia, whose CEO Jensen Huang has pledged to keep the platform open and hardware-neutral.

The DecoderSep 9, 10:38 UTC · 6d agoAI industry1

Unlocking Lossless Speedups in LLMs via Discrete Diffusion

Uno pairs autoregressive LLMs with lightweight diffusion weights to draw multiple tokens in parallel, delivering up to 3x lossless speedup without a draft model.

The paper introduces diffusion-augmented LLMs: autoregressive weights trained with the standard next-token objective plus lightweight diffusion weights trained via a Diffusion Distillation phase to emit multiple tokens in parallel. Psi-Spec samplers enable lossless acceleration without the separate draft model required by speculative decoding. The 8B Uno model outperforms the 26B open DiffusionGemma and proprietary Mercury 2 on agentic tool use, coding, and long-context reasoning benchmarks, with up to 3x throughput gains over the base model at all evaluated batch sizes. Code and checkpoints are released publicly.

Hugging Face daily papersSep 2, 20:00 UTC · 13d agoAI research

Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation

A distillation framework compresses LLM reasoning into a 15.5M-parameter trade-up recommendation model reaching AUC 0.941 with product-type test-time training.

The paper targets trade-up recommendation, which identifies higher-quality alternatives that preserve customer purchase intent. A retrieval-augmented few-shot LLM teacher generates labels and rationales that supervise a compact embedding-pair classifier; at inference the 15.5M-parameter student uses only two precomputed 768-dimensional embeddings with no LLM calls. On 8,352 annotated pairs, label-only training scored AUC 0.912, reasoning distillation reached 0.924, and product-type test-time training lifted it to 0.941 with average precision 0.940. The distilled student is roughly 5,000x faster and 10,000x cheaper than direct LLM inference on a 100K-pair proxy catalog.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:08 UTC · 11d agoAI research

[webapps] Langflow 1.8.4 - Path Traversal to Remote Code Execution

A path traversal to remote code execution exploit for Langflow 1.8.4, a popular LLM application builder, was published on Exploit-DB.

Exploit-DB lists a proof-of-concept exploit chaining path traversal to remote code execution in Langflow 1.8.4, an open-source tool used to build LLM applications and agents. The chain allows an attacker to write arbitrary files outside the intended directory and achieve code execution on the host. The provided text does not include a CVE identifier or reports of exploitation in the wild, but RCE in a widely deployed AI tooling product is notable for defenders.

Exploit-DBAug 31, 00:00 UTC · 16d agoExploit / PoC1

[AINews] Fal’s H3 Max Live breaks the infinite videogen barrier

Fal post-trained MiniMax H3 into a 'Max' variant with 35x-faster inference, enabling faster-than-realtime AI video generation and infinite streams.

Fal post-trained MiniMax's H3 model into a 'Max' variant and optimized it for its in-house inference engine, achieving roughly 35x the speed of the official endpoint. The optimization enables faster-than-realtime video generation, demonstrated by an infinite interactive AI-generated stream productized by levels.io. The roundup also notes Meta Muse Code's general availability with an SDK, open DeepSeek-V4-Flash-Vision-Exp weights, GLM-5.3-Flash's strong agentic cost/performance rankings, and Tencent's 770B-parameter Hy4 Preview MoE with 49B active parameters.

Latent SpaceSep 1, 04:36 UTC · 15d agoAI industry

Honeypot-Omaha and batch.py [Guest Diary], (Wed, Sep 2nd)

A SANS ISC guest diary describes batch.py, a Python tool that consolidates honeypot logs and enriches IOCs with threat intelligence data.

Written by a SANS.edu BACS intern, the diary explains analysis of the DShield Honeypot-Omaha sensor, which uses Cowrie to emulate SSH and Telnet and log attacker activity. The author's batch.py script implements a four-phase pipeline with SHA-256-generated master and guest authentication to consolidate JSON and log files, correlate data via external APIs, and produce MITRE, CVE, geolocation, threat-score and fingerprint enrichment for investigated indicators.

SANS Internet Storm CenterSep 3, 02:02 UTC · 13d agoTools1

Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models

A seven-person independent team trained open-weight agentic cyber models via a data-centric post-training framework, ranking 10th on CyberGym and first at comparable scale.

The paper presents Feyospace-v1, a data-centric post-training framework combining five systems: Choulea (hidden reasoning analysis), SkyReal (teacher-sampling cost reduction), Hongzwang (bypassing teacher API restrictions), PSBreakup (restoring capabilities weakened by model merging), and Kreator (converting expert interventions into trainable reasoning). The data engine builds resettable coding, vulnerability, CTF, kernel-history, full-exploit, firmware, and device-backed environments, retaining only execution-verified and evidence-audited trajectories, yielding 164,269 trajectories for long-context supervised fine-tuning. Three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across pooled CTF suites. As of September 1, 2026, Feyospace-s1 achieves a 63.24% verified success rate, ranks 10th on the official CyberGym leaderboard, and all three checkpoints rank 1st among models at comparable parameter scales.

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