Search: “python”

7 stories

Searchkeyword smart Sortlatest hot importance relevance

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

CategoryAll Exploit / PoC 181 Vulnerability 123 Ransomware 104 Malware 53 Data breach 48 Threat actor 42 AI safety & security 15 Phishing & fraud 7 Model release 4 Advisory 2 AI industry 1 Policy & legal 1 Research 1

SeverityAll critical 454 high 104 medium 17 low 0 info 7

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 modelsupdated Sep 11, 00:33 UTC · 5d agofirst Sep 10, 02:17 UTC · 6d agoModel release 3 sources1

nex-agi/Nex-N2.5-Pro — new model trending #30 on Hugging Face

Nex-AGI launches Nex-N2.5 agentic model family (mini/Pro/Max), with Max built on a 1.6-trillion-parameter MoE foundation.

Nex-AGI introduced Nex-N2.5, a next-generation family of agentic models in three sizes (mini, Pro, Max) focused on long-horizon agentic tasks including computer use, web browsing, and autonomous program execution. Nex-N2.5-Max is built on a 1.6-trillion-parameter text-only Mixture-of-Experts foundation, marking the company's first complete post-training effort at trillion-parameter scale. Weights will be released open-source on Hugging Face and ModelScope, with hosted access via OpenRouter. Benchmark comparisons against Claude Opus 5, GPT-5.6 Sol, Kimi-K3, GLM-5.3, DeepSeek-V4-Pro-0813, and Qwen3.8-Max show competitive scores on Terminal-Bench 2.1 and SWE-Bench Pro, though weights were listed as "coming soon" at publication.

Hugging Face trending modelsSep 8, 11:01 UTC · 7d agoModel release1

[AINews] Muse Spark 1.3 matches GPT-5.6-Sol, confirming Meta Superintelligence as the newest Frontier Lab, >90% discount for training

Meta's Muse Spark 1.3 reportedly ranks as the world's #3 model, matching frontier models from OpenAI and Anthropic with planned open weights.

The Latent Space AI News roundup leads with Muse Spark 1.3, promised in Zuckerberg's letter, which ranks #3 worldwide per AAII, is slated for open weights, and uses a pricing model over 90% cheaper when users opt in to training. The issue also covers the rumored Gemini 3.8 Flash launch and analysis arguing OpenAI's rumored looped-transformer 'Astra' architecture is a modest tweak rather than a breakthrough. Additional coverage includes ByteDance Seed's HarnessDev harness-evaluation benchmark, a retrieval-invoked actual-use evaluation method, Stanford's revamped agent engineering curricula, and Photon 2.1 adding TTS models and NVIDIA B200 support.

Latent SpaceSep 3, 04:38 UTC · 13d agoModel release1

deepseek-ai/DeepSeek-V4-Flash-Vision-Exp — new model trending #10 on Hugging Face

DeepSeek released DeepSeek-V4-Flash-Vision-Exp, its first experimental multimodal model, with large multimodal agent benchmark gains over V4-Flash-0731.

DeepSeek AI published DeepSeek-V4-Flash-Vision-Exp, an experimental multimodal model built on the DeepSeek-V4-Flash architecture with added visual modules and continued training. It scores 83.9 on Terminal Bench 2.1 and 36.5 on ApexBench Pass@1 versus 26.2 for DeepSeek-V4-Flash-0731, while remaining comparable to Opus-4.8 on several benchmarks. The MIT-licensed repository ships a tokenizer, OpenAI-style and TXT prompt encoding, and a minimal PyTorch inference implementation, with vLLM and SGLang deployment recipes.

Hugging Face trending modelsAug 31, 06:16 UTC · 15d agoModel release1

Australia charges two men for TeamPCP supply

Australia charged two Perth men over TeamPCP supply-chain attacks compromising 1,000+ organizations and exposing 500,000+ credentials.

The AFP charged two Perth-based men with a combined 14 offences for their alleged roles in TeamPCP, with payments in cryptocurrency for data intrusion, identity crime, and money laundering. The group's supply-chain attacks targeted developer tools including TanStack, Trivy, and LiteLLM, with downstream victims including the European Commission and GitHub. Investigators estimate the campaign compromised over 1,000 organizations, exposed more than 500,000 credentials, and led to theft of at least 300 GB of data, with remediation costs in the hundreds of millions of dollars. The men could face a combined 82 years if given maximum sentences, though sentences are typically served concurrently.

The RecordAug 27, 13:00 UTC · 19d agoPolicy & legal

[AINews] Hot Chips: OpenAI’s Jalapeño, Cerebras CS-5, Groq 3 LPX, Apple M6

OpenAI unveiled Jalapeno custom inference chip claiming 1.5-1.9x better perf-per-watt than NVIDIA GB200/GB300, deploying in-house by year-end.

At the 37th Hot Chips conference, OpenAI published first benchmark details for its custom Jalapeno inference chip, claiming 1.5-1.9x more work per watt, 1.7-3.6x lower end-to-end latency, and 2.1-4.1x higher interactive-workload performance versus NVIDIA GB200/GB300, with the 700W-rated part staying at or below 550W in tests. Deployment into OpenAI's own infrastructure begins by year-end, with Gen 2 deep in development and Gen 3 underway. OpenAI also said GPT-Astra and Codex helped write low-level kernels, reportedly 1.5-1.8x faster than human-expert code for selected attention and MoE blocks. Cerebras CS-5, Groq 3 LPX and Apple M6 were also featured at the conference.

Latent SpaceAug 27, 01:31 UTC · 20d agoAI industry

Code review used to be the only way to catch these bugs

Palo Alto Networks' Unit 42 says its NOVA system found 14,090 vulnerabilities in 3,915 open-source projects, mostly non-crashing bugs like access control flaws.

Unit 42's NOVA system analyzed 3,915 open-source projects over two months and reported 14,090 validated vulnerabilities, only 85 of which matched previously documented findings. 92% of findings fell outside fuzzing-friendly categories, clustering instead in access control, path traversal, injection, prototype pollution, and SSRF; language ecosystems showed distinct weakness profiles. Of 5,421 supply-chain findings, 1,280 were flaws in dependencies while 4,141 were downstream exposures, 2,776 validated with working proof-of-concepts. Unit 42 warned that faster discovery combined with an average 55-day patch deployment window has collapsed the patch-to-exploit gap.

Help Net SecurityAug 25, 18:03 UTC · 21d agoResearch