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[AINews] Claude Fable/Mythos 5.1: new SOTA model, 75% cache price cut but 70% more output tokens

Anthropic launched Claude Fable 5.1 and Mythos 5.1, claiming new SOTA benchmarks, with 75% cache-read price cut and 1M-token context.

Anthropic released Claude Fable 5.1 and Mythos 5.1 as flagship models for coding and knowledge work, with a 1M-token context window and pricing of $10/$50 per million input/output tokens and cache reads cut 75% to $0.25. Artificial Analysis Intelligence Index scored Fable 5.1 at 66 versus 63 for Claude Opus 5, with HLE at 59.1% and Terminal-Bench v2.1 at 91.4%, though per-task cost rose ~20% due to 1.7x output token usage. Community analysis suggested Fable and Mythos may share underlying weights with different safety/routing behavior, and release notes highlighted Enterprise Frontier Safeguards and zero-data-retention support.

Latent Space · 15d agoModel release2

DeepSeek-v4.1 Flash: Pushing the Limits of KV Cache Compression

DeepSeek-V4.1 Flash is a 552B-parameter multimodal MoE model with 1M-token context achieving 4x KV cache compression for long-horizon agent workloads.

A detailed analysis of the DeepSeek-V4.1 Flash technical report describes a 552B-parameter multimodal mixture-of-experts model supporting contexts up to 1 million tokens. Its Causal Encoder-Decoder (CED) architecture activates 8B parameters during prefill and 16B during decode, and reportedly delivers about 420 tokens/s. Joint optimization of architecture (CSA2 cross-layer compression), FP4 KV cache precision, and deployment strategy cuts runtime KV cache to roughly 1/4 and persistent KV cache to about 1/8 of DeepSeek-V4-Flash at the same sequence length, targeting storage and bandwidth bottlenecks in long-horizon agent serving. The author notes all DeepSeek-V4 Pro models were taken offline following the release.

DeepSeek AI Released DeepSeek-V4.1-Flash with 1M Context, FP4 KV Cache, and Cross-Layer Attention Reuse

DeepSeek released open-weight V4.1-Flash, a 552B MoE model with 1M context and FP4 KV cache, beating Opus-5 and GPT-5.6 Sol on agent benchmarks.

DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with a 552B-parameter backbone plus 196B Engram parameters, activating 8B parameters at prefill and 16B at decode, with a 1M-token context window. It introduces a causal encoder-decoder design, Compressed Sparse Attention 2, and FP4 (E2M1) KV cache quantization, cutting global KV cache to 890 bytes per token, about 1/4 of V4-Flash and 437x smaller than V1. Pre-training covered 45T multimodal tokens; the MIT-licensed weights ship on Hugging Face with vLLM and SGLang support. It scores 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1, ahead of Opus-5 and GPT-5.6 Sol.

MarkTechPost · 7d agoModel release1

The skb that wasn't freed - the Fragnesia primitive via Open vSwitch

Doyensec details CVE-2026-90049 in Open vSwitch, enabling deterministic Linux kernel local privilege escalation on default major distribution installs.

Doyensec reports that the Open vSwitch datapath strips the SKBFL_SHARED_FRAG flag from packets it is still forwarding, allowing an in-place decrypt to write attacker-chosen bytes into root-owned page cache — a Dirty COW-class primitive that re-opens the Fragnesia bug family. The issues are tracked as CVE-2026-90049, CVE-2026-89487, and CVE-2026-80977, and were reported to the kernel security team with fixes coordinated alongside OVS maintainers. A deterministic local privilege escalation works on default installs of Arch, Fedora, Debian, Amazon Linux, and RHEL where unprivileged user namespaces and openvswitch auto-loading are enabled; the fix landed in mainline and shipped in stable on 09/04/2026. The write technique builds on the earlier Fragnesia and Dirty Frag bugs, including CVE-2026-43284 and CVE-2026-43500.

GPT-6 Astra, Looped Transformers, and Hidden Reasoning

OpenAI released GPT-6 Astra, its strongest model to date, with standout 3D rendering and computer-use performance and 99.9% on ARC-AGI-3.

Sebastian Raschka reviews OpenAI's GPT-6 Astra, calling it the best model he has used, with disproportionate gains in 3D rendering, animation, and computer use through the Codex/ChatGPT harness. The model scores 99.9% on ARC-AGI-3 versus 7.8% for GPT-5.6 Sol and leads the Artificial Analysis Coding Agent Index, though gains on independent aggregate indices are more incremental. The article also explains looped transformer/recurrent depth architecture rumors, speculation that Astra hides its chain-of-thought reasoning, and recent research insights on the topic.

StyleSmuggler: Magento and Adobe Commerce 0-day RCE (CVE-2026-75650) under active attack

Sansec details actively exploited StyleSmuggler 0-day (CVE-2026-75650, CVSS 10.0) unauthenticated RCE in Magento and Adobe Commerce, patched by Adobe hotfix APSB26-146.

Sansec is investigating StyleSmuggler, an actively exploited unauthenticated remote code execution chain in Magento Open Source and Adobe Commerce, now tracked as CVE-2026-75650 with CVSS 10.0. Adobe released hotfix VULN-39341 via APSB26-146 (priority 1) on September 7 for versions 2.4.4 through 2.4.9, but stores were being exploited for roughly three days before the fix existed. The implant is a Rust backdoor that disguises itself as kworker, fc-cache, or chronyd processes and exfiltrates host data in MessagePack records sent as fake NTP replies over UDP port 123. Adobe advises rotating the encryption key and every credential it protected, and Sansec stresses patching does not clean already-compromised stores.

deepseek-ai/DeepSeek-V4.1-Flash — new model trending #28 on Hugging Face

DeepSeek releases DeepSeek-V4.1-Flash, a 552B-parameter multimodal MoE model with 1M-token context and KV cache cut to 890 bytes per token.

DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with a 552B-parameter backbone that activates 8B parameters per token during prefill and 16B during decode. It uses a Causal Encoder-Decoder architecture, Compressed Sparse Attention 2, and FP4 KV caching to reduce the global KV cache footprint to 890 bytes per token, roughly one quarter of DeepSeek-V4-Flash. The model was trained from scratch on 45T tokens with context extended to 1M tokens, includes an Engram conditional-memory module (196B parameters), and is released under the MIT license. Post-training uses SFT, RL, and on-policy distillation with large-scale automated synthesis of agentic tasks and a controllable reasoning effort setting from 1 to 100.

Hugging Face trending models · 7d agoModel release1

Six new dnsmasq vulnerabilities open the door to DNS cache poisoning, local root

Six dnsmasq flaws enable DNS cache poisoning, DoS, memory leaks, and local root code execution; fixes ship in version 2.92rel2.

Researchers disclosed six dnsmasq vulnerabilities spanning memory safety and input validation, including heap buffer overflows in extract_name() and extract_addresses(), DNSSEC infinite-loop and out-of-bounds read flaws, and a DHCPv6 out-of-bounds write allowing local root code execution. Exploitation paths include DNS cache poisoning, bypassing security controls, remote denial of service, and local privilege escalation. Maintainers released version 2.92rel2 with fixes, and a stable 2.93 release is expected within weeks.

New Deepseek model V4.1-Flash cuts memory needs for AI agents

DeepSeek released V4.1-Flash, a 552B-parameter open-weight model cutting KV cache needs to a quarter of its predecessor for cheaper million-token AI agents.

DeepSeek released V4.1-Flash, a multimodal model with 552 billion total parameters and 1 million-token context, trained from scratch on 45 trillion tokens of text and images. The model reduces KV cache footprint to about a quarter of DeepSeek-V4-Flash in fast GPU memory and one-eighth offloaded, and 437x smaller per token than DeepSeek-V1, via an encoder/decoder split, 8-16B active parameters per token, and FP4 cache storage. It scores 74.2% on DeepSWE v1.1, narrowly beating Anthropic Opus 5 and OpenAI GPT-5.6 Sol, with gains attributed to data and RL scaling rather than new algorithms. Weights are on Hugging Face under MIT license, also served via API at V4-Flash prices.

The Decoderupdated · 5d agofirst · 6d agoModel release 2 sources1

StyleSmuggler: The Magento Zero-Day Behind New Store Attacks

Sansec reports actively exploited Magento/Adobe Commerce zero-day StyleSmuggler enabling unauthenticated RCE and Rust backdoor installation on fully patched stores since September 4.

Sansec discovered StyleSmuggler, an unpatched zero-day in Magento Open Source and Adobe Commerce, affecting all current versions including 2.4.7, 2.4.8 and 2.4.9, with attacks observed since September 4. The two-stage attack poisons Magento's template system via the styles property and executes the injected PHP during 'Payment Transaction Failed Reminder' email rendering, working even when email delivery fails and when sessions are moved to Redis. Successful compromise installs a lightweight Rust backdoor disguised as fc-cache or chronyd that beacons every 60 seconds with 48-byte UDP packets to NTP port 123 at ntp.timesync.to. A second attacker deployed a PHP web shell in product-image cache directories, hidden behind 404 responses unless a correct X-Cache-Token header is present.

Security Affairs · 9d agoExploit / PoC in the wild

China-Aligned Hackers Hide PeckBirdy Malware C2 Inside Casino and Adult Websites

Infoblox found China-aligned actors hiding PeckBirdy malware C2 inside fake Chinese-language casino and adult websites, evading security scans via service workers and WebSockets.

Infoblox reported that China-aligned actors behind the PeckBirdy JScript C2 framework conceal command-and-control inside low-quality Chinese-language casino and adult websites, extending Trend Micro's earlier findings that tied the framework to backdoors including MKDOOR and HOLODONUT. One decoy, vip311[.]cc, embedded JavaScript linked to cache-mcp[.]com and registered a service worker connecting to mcp-source[.]online over WebSocket; at publication mcp-source[.]online had zero VirusTotal detections, showing how the layered design evades conventional scanning. The campaign has been active since at least 2023, and just over 3% of Infoblox enterprise customers resolved at least one PeckBirdy C2 domain, with education, IT, banking and government among observed sectors.

GBHackersupdated · 23h agofirst · 1d agoThreat actor in the wild 2 sources

[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.

DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.

Latent Space · 5d agoModel release 2 sources1

wp2shell: incident response guide (CVE-2026-63030 + CVE-2026-60137)

Eye Security published forensic IR tooling for wp2shell (CVE-2026-63030/CVE-2026-60137), the unauthenticated WordPress core RCE chain, after WordPress.org forced auto-updates.

wp2shell chains a REST API batch-endpoint route-confusion bug (CVE-2026-63030) with an SQL injection in WP_Query's author__not_in parameter (CVE-2026-60137), giving unauthenticated attackers rogue admin and code execution on default installs. Adam Kues of Searchlight Cyber discovered the flaw, a public PoC exists on GitHub, and WordPress.org forced automatic updates across an estimated 200M+ sites. Eye Security released a compromise-scanner WordPress plugin and browser extension and notes database artifacts (oEmbed cache rows, changesets) are the primary evidence since the attack is log-blind; fixed versions are 6.8.6, 6.9.5, and 7.0.2.