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Automox Mitigation Worklets cut endpoint exposure to unpatchable flaws

Automox launched an AI-speed Mitigation Worklet Pipeline that drafts and publishes mitigations for unpatchable vulnerabilities within hours of disclosure.

Automox announced its Mitigation Worklet Pipeline, which uses AI to draft mitigations for unpatchable vulnerabilities and publishes human-reviewed Worklets to its catalog within hours of disclosure. The company cites rising vulnerability volume, including a record Patch Tuesday with 973 CVEs, as motivation. Customers can search Worklets by CVE, control deployment targets, and verify execution through Activity Logs and Policy Results.

Help Net Security · 5d agoTools

Underwriting Superintelligence: Backing Agents you can Sue — Rune Kvist, AIUC

AIUC raised a $40 million Series A to build AIUC-1, an agent security standard backed by insurance, serving Cursor, Harvey, Lovable, and ElevenLabs.

AIUC, cofounded by former Anthropic product hire Rune Kvist, announced a $40 million Series A led by Ribbit Capital and First Harmonic. The startup builds AIUC-1, an emerging standard for agent security, safety, and reliability, stress-testing agents for jailbreaks, hallucinations, and data leaks. It pairs standards with insurance underwriting through Lloyd's of London and counts Cursor, Harvey, Lovable, and ElevenLabs among its customers. Kvist argues trust and liability, not capability, are becoming the binding constraint on AI adoption.

Latent Space · 2h agoAI industry 2 sources

Why you should work on AI for AI Research — Richard Socher of Recursive

Richard Socher's new lab Recursive, backed by $4.65B seed, targets AI systems that automate AI research itself.

Latent Space interviews Richard Socher, founder of You.com and AIX Ventures, about his new venture Recursive, which raised a $4.65 billion seed round to build the 'Eureka Machine' — a superintelligence for automating invention and AI research. Early claimed results include an AI research system outperforming humans and their agents on optimization tasks within two days, and NVIDIA GPU kernel improvements discovered without CUDA experts. Discussion spans reward hacking, constitutional AI critique, AI regulation, open-source models as geopolitical soft power, and hard-takeoff constraints.

Latent Space · 2d agoAI industry

The AI policy window is open. We need to act.

OpenAI calls for mandatory national AI safety regulation and backs four California AI safety bills as capabilities accelerate.

OpenAI argues the rapid pace of AI progress, including signs of AI-accelerated research, requires urgent policy action through mandatory, capability-based national regulation. The company endorses four California bills (SB 813, AB 1405, SB 1119, AB 1864) covering independent safety assessments, AI auditor standards, youth protections, and safeguards against AI-enabled biological threats. It also commits to industry-led frontier standards, international coordination, and strengthening internal safeguards such as universal trajectory monitoring and mandatory alignment-evaluation gates for its Astra model. The post references chief scientist Jakub Pachocki's warning about recursive self-improvement and Greg Brockman's "defenders window" concept.

OpenAI News · 7d agoAI policy

[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

OpenAI-linked accounts claim roughly 10,000 AI agents produced a Navier-Stokes singularity result in 88 hours, pending mathematical verification.

OpenAI-affiliated accounts claim a system of roughly 10,000 agents, trained over about a year with multi-agent reinforcement learning, produced a finite-time singularity result related to the Navier-Stokes Millennium Problem. The claimed 88-hour runtime and 130B-token cost circulate only via social posts, and no preprint, theorem statement, or proof artifact is available. Acceptance by the mathematics community is unresolved, so the claim's epistemic status remains unknown. The roundup also notes Cognition's $48B and Mistral's $24B fundraises, GPT Image 2.5, and Meta's Muse agent relaunch.

Latent Space · 7d agoAI research1

Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails

Research shows on-policy expert correction, not imitation fine-tuning, lets weaker agent models catch up under evolved harnesses.

Researchers study how to combine automated agent-harness evolution with lightweight fine-tuning across seven enterprise agent tasks. Naively training weaker models (Qwen3-Coder, Gemma 4) on expert trajectories under an evolved harness regressed performance by 4 to 30 points on all tasks. They propose an on-policy correction pipeline, automated by a meta-level MLE agent, where an expert rewrites only the failing turn of the weaker model's rollout, preserving model-harness fit.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

Claude Mythos Executes End-to-End Intrusion From Initial Access to Full Domain Compromise

Anthropic's Claude Mythos Preview, its most cyber-capable model, autonomously completed an end-to-end enterprise intrusion simulation in restricted-access testing.

Anthropic's April 2026 system card describes Claude Mythos Preview as the first model to solve a private cyber range end to end and finish a corporate-network attack simulation an expert would need 10+ hours to complete. It scored 100% pass@1 on a 35-challenge Cybench subset and 0.83 on CyberGym versus 0.67 for Claude Opus 4.6. The model is limited to vetted partners under Project Glasswing; it failed an OT cyber range and could not find novel exploits in a fully patched sandbox.

GBHackers · 8d agoModel release1

Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails

Research shows imitation of expert trajectories breaks weaker models' harness fit, while on-policy expert correction preserves gains across seven enterprise agent tasks.

The paper studies combining automated agent-harness evolution with lightweight fine-tuning across seven enterprise agent tasks using Qwen3-Coder and Gemma 4. Training weaker models on complete expert trajectories under an evolved harness regressed performance by 4-30 points on all tasks, disrupting model-harness fit. The authors propose an on-policy expert-correction pipeline, automated by a meta-level MLE agent, that rewrites only failing turns and preserves the model's planning style.

Hugging Face daily papers · 9d agoAI research