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12 stories in the last 7d

Building a Linux GPU Driver for the M4 Mac Mini in One Month

Two developers built a fully OpenGL ES 3.0 compliant Linux GPU driver for the M4 Mac Mini in one month via clean-room reverse engineering.

Niklas and the author reverse engineered Apple's AGX GPU firmware ABI and user-space components in about a month, a process that normally takes years, producing an OpenGL ES 3.0 conformant driver fast enough to run Minecraft at 200fps on an M4 Mac Mini. The work was done transparently using hypervisor traces without examining Apple binaries, following clean-room practices, and included a custom shader compiler, command stream builder, and a full Linux kernel driver for the firmware ABI. The A18 Pro firmware ABI proved significantly more complex than the M1's, with 1.5x as many structs and twice as many pointers. All experiments and provenance evidence were published in public agx-re repositories.

How much of F-Droid is LLM generated?

A FOSS maintainer manually graded 102 F-Droid apps from the September 12, 2026 update batch, finding many show signs of LLM-generated code.

A student and FOSS app maintainer reviewed 102 apps pushed to F-Droid on September 12, 2026, assigning each a three-tier rating for likelihood of LLM-authored code (mostly AI >50%, hard to say/mostly human, no signs of AI). The heuristic relies on commit aesthetics, README and branding style, and the presence of agentic infrastructure like Claude Code or Codex, which automatically places an app in the 'mostly AI' tier. Example ratings include Amber (Nostr event signer) as mostly AI, and Aria for Misskey as showing no AI signs. The author stresses reliable detection of LLM-generated code from text alone is impossible, so ratings are approximate.

CISOs Race to Control AI Agents Without Destroying Their Value

Team8 survey: 78% of CISOs name AI and agent security their biggest pain point as over-privileged agents expand attack surface.

Team8's annual CISO Village survey reports that 78% of security leaders cite AI and agent security as their biggest pain point, twice the second-ranked concern (39%), while 71% are experimenting with or augmenting security tools using AI agents. Team8 CISO Tim Brown warns that employee-built agents created with tools like Claude Code, Cursor and Codex can take unintended harmful actions, such as poking around production systems, because prompt imprecision combines with non-deterministic model behavior. Brown recommends building guardrails into the agent development process to limit where agents can go and what they can do, without destroying business utility. He also urges greater transparency and experience sharing among security leaders facing the same agent security problems.

SecurityWeek · 2d agoAI safety & security

Top 10 Best Cloud Workload Protection (CWPP) Solutions in 2026

Editorial scorecard ranks 2026 cloud workload protection platforms, placing Prisma Cloud first, Sysdig second, and CrowdStrike third on detection quality.

A research-based scorecard rates ten cloud workload protection (CWPP) platforms across runtime depth, container/Kubernetes support, coverage breadth, cloud context, and value. Palo Alto Prisma Cloud leads at 8.9, followed by Sysdig at 8.8 and CrowdStrike at 8.6, with Aqua Security tying CrowdStrike at 8.6. The piece argues agent-versus-agentless is a false choice, with leaders now pairing agentless visibility with eBPF-based runtime sensors. Scores are editorial assessments, not lab benchmarks.

Cyber Security News · 5d agoIndustry1

Linux Detection Engineering - Local Privilege Escalation

Elastic details a layered detection framework for Linux local privilege escalation, covering 2026's copy-on-write bug wave and LLM-assisted discovery.

Elastic Security Labs describes how most Linux local privilege escalations share a common host flow — an unprivileged process launched from a writable path becoming root — and proposes layered detections combining general outcome-based rules with per-technique rules in Elastic Defend and Auditd. It tracks 13 recent LPE disclosures, seven of which share a copy-on-write/zero-copy bug class, including Copy Fail, DirtyFrag, Fragnesia, DirtyDecrypt, DirtyClone, pedit COW, and RefluXFS. Qualys attributes RefluXFS to an LLM-assisted research effort with Anthropic using Claude Mythos Preview, and another bug is credited to an LLM-assisted workflow. Detection and endpoint rules are published in Elastic's detection-rules and protections-artifacts repositories.

Elastic Security Labs · 5d agoResearch

A Graph-Based Approach for Mapping Kernel-Level Telemetry to MITRE ATT&CK

Trace2ATT&CK maps eBPF kernel telemetry to MITRE ATT&CK via provenance graphs and RAG with local open-weights LLMs, validated on 347 Atomic Red Team tests.

Trace2ATT&CK collects kernel-level events via eBPF, correlates attacker commands into a provenance graph, and derives compact graph representations suitable for LLM-based reasoning, mapping behavior to MITRE ATT&CK techniques with ranked candidates and rationales. Mapping uses both pure LLM prompting and retrieval-augmented generation grounded in the ATT&CK knowledge base. It was evaluated on 347 Linux Atomic Red Team tests using locally deployed open-weights LLMs. RAG consistently improved ATT&CK mapping over pure prompting, and provenance graphs substantially outperformed raw telemetry, without compromising data confidentiality.

arXiv cs.CR · 5d agoResearch

12 Best Server Security Solutions Compared (2026): Features & Pricing

GBHackers ranks 12 server security solutions for 2026, naming CrowdStrike and SentinelOne as server EDR leaders and Trend Micro Deep Security top for virtual patching.

The guide scores 12 server security platforms across five weighted criteria, with detection and response depth and Linux parity weighted 25% each. CrowdStrike and SentinelOne lead server EDR, Trend Micro Deep Security is highlighted for virtual patching of unpatchable estates, and Microsoft Defender for Servers is noted for Azure and hybrid economics. It is an editorial assessment comparing features and pricing models rather than a lab test.

GBHackersupdated · 9h agofirst · 5d agoIndustry 14 sources1

Why I'm still bearish on LLMs after Navier-Stokes

Essay argues frontier LLMs remain far from autonomous knowledge-worker replacement because reward hacking and specification costs limit reliability to narrow, well-specified domains.

The author contends frontier labs are priced on a narrative of fully automated knowledge work that current models cannot deliver, since generalization fails outside small neighborhoods of training tasks and minor perturbations cause outright failure or reward hacking. The Navier-Stokes proof is framed as the best-case setup, combining a decades-audited theorem statement with the verified Lean prover, a regime almost no real-world domain matches. Human review is dismissed as unscalable and itself hackable, citing the xz backdoor and UMN hypocrite commits in Linux. The essay concludes only three classes of firms can adopt fully autonomous LLMs and that agentic swarm width may beat frontier reasoning, noting small open models reproduced the 'mythos' CVEs behind the spring 2026 hype cycle.

Weekly Cybersecurity Newsletter – Top 50 Biggest Cybersecurity Stories of the Week

GBHackers weekly digest rounds up 50 stories including Microsoft's 973-CVE patch drop, exploited Cisco FMC flaws, and Claude agent attacks.

GBHackers' September 7-12, 2026 newsletter summarizes the 50 biggest cybersecurity stories of the week. Highlights include Microsoft patching a record 973 CVEs with two exploited zero-days, active exploitation of Cisco FMC, Check Point VPN and Ivanti flaws, China-linked crews chaining Chrome and Windows zero-days, AI agents mass-exploiting PaperCut to compromise 440 servers, and the emergence of Panzer cross-platform ransomware. It also covers Anthropic and OpenAI agentic AI incidents and CrowdStrike's SafeMind launch.

GBHackers · 1d agoIndustry in the wild

Retrospectively Reverse-Engineering Apple's Neural Engine

A developer reverse-engineers Apple's M1 Neural Engine architecture, mapping compute cores, MAC datapaths, and schedulers to explain the NPU's decline as transformers displaced CNN workloads.

A developer who previously maintained a reverse-engineered Linux driver for Apple's Neural Engine (ANE) published a retrospective deep dive mapping the M1 ANE's full internal architecture: compute, datapath, scheduler, memory, and execution model. The M1 ANE has 16 compute cores with 128 FP16 (or 256 INT8) MAC lanes each, totaling 2048 parallel MAC lanes, using 32-bit Q16.16 fixed-point accumulation with FP16 readout and an accumulator that saturates at 2^15. The author argues the ANE's dataflow was architected around the predictable reuse patterns of 2017-era CNN workloads (dating to the A11 Bionic), which autoregressive transformer decode broke, limiting its usefulness for general ML. With Apple's M5 folding ANE cores into GPU cores to tout LLM performance, the post frames this as the beginning of the end for the standalone NPU.

[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign

xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.

The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.

Latent Space · 1d agoAI safety & security

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 · 4d agofirst · 6d agoModel release 4 sources1