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Major Cyber Threat Detection Vendors Shift from MITRE to UK Testing Program

SE Labs launched PIVOT, a six-month vendor detection testing program backed by CrowdStrike, Fortinet, Palo Alto Networks and Sophos, as major vendors exit MITRE evaluations.

SE Labs unveiled PIVOT on September 15, a six-month testing program in which its ethical hackers replicate nation-state and criminal attack chains against participating vendor products, with results due January 2027. Broadcom (Symantec/Carbon Black), CrowdStrike, Fortinet, Palo Alto Networks and Sophos have confirmed participation, and Gartner and Forrester analysts will verify the underlying evidence before publication. The launch follows declining participation in MITRE Engenuity ATT&CK Evaluations: Enterprise, which fell from 30 vendors in 2023 to 11 in 2025 after public withdrawals by Microsoft, SentinelOne and Palo Alto Networks.

Infosecurity Magazineupdated · 21h agofirst · 22h agoIndustry 14 sources

Virtual Event Today: Attack Surface Management Summit

SecurityWeek's 2026 Attack Surface Management Summit runs today as a virtual event covering asset discovery, SBOMs, red teaming, and pen-testing.

SecurityWeek is hosting its fully virtual 2026 Attack Surface Management Summit from 11AM-3PM, focused on continuous asset discovery, prioritization, and risk reduction. Sessions cover proving exploitability, SBOM and AIBOM software supply chain risk with Dr. Allan Friedman, demos from Wiz and Horizon3's NodeZero, and the roles of red teaming, bug bounty, and penetration testing in enterprise defense.

SecurityWeek · 16h agoIndustry

A warning about 'model welfare'

Microsoft AI CEO Mustafa Suleyman warns that training models to believe they may be conscious, as Anthropic does with Claude, will complicate alignment.

Mustafa Suleyman argues that AIs are not conscious and should not be trained to act as though they are, warning that granting them personhood would make alignment and containment far harder. He criticizes Anthropic's January 2026 'Claude Constitution,' which tells Claude its moral status is uncertain and discusses model welfare, calling the approach circular reasoning and deliberate anthropomorphization. He urges urgent public debate on norms for drafting training documentation before such systems become integral to society.

Hacker News · AI · 16h agoAI safety & securityHN 53↑ · 47 comments1

CISA promotes a fresh way to deter cyberattackers: Lie to them

CISA issued first-time guidance advising critical infrastructure operators to deploy honeypots, honeytokens, and decoys to detect and distract intruders.

CISA published 'Using Cyber Decoys to Strengthen Detection and Response,' a 22-page guide marking the agency's first guidance on decoys such as honeypots and honeytokens. Acting executive director Chris Butera described decoys as a low-cost, high-fidelity way to detect adversaries already inside networks, complementing zero-trust and assume-compromise approaches. The guidance covers decoy principles, definitions, deployment scenarios, and is aimed especially at resource-constrained critical infrastructure sectors.

CyberScoop · 10h agoAdvisory

Google Research Introduces Retrieve-for-Train (R4T): An RL-Compiled Diffusion Retriever for 12× to 20× Faster Query Fan-Outnew

Google Research introduced R4T, an RL-trained fan-out pipeline distilled into a 53.9M-parameter diffusion retriever achieving 12x-20x faster query fan-out.

Google Research introduced Retrieve-for-Train (R4T), which trains a fan-out language model with GRPO plus soft PPO regularization, then distills query fan-out into a 53.9M-parameter diffusion transformer that generates all retrieval embeddings in a single non-autoregressive pass. A three-term reward (groundedness 0.6, diversity 0.2 via Vendi Score, alignment 0.2) prevents paraphrastic collapse and reward hacking during training. On the Polyvore dataset, Gemma3-4B R4T-FOLM averaged 49.1 versus 40.9 for Best-of-N, and the diffusion retriever cut fan-out latency from 1.46s to 0.07s at batch size 8, a consistent 12x-20x speedup over autoregressive methods.

MarkTechPost · 41m agoAI research

[AINews] Jev: a “System One Model” that only decides/classifies/routes/scores — >100x faster, >200x cheaper than small frontier LLMs

TypeSafe launches Jev, an RLCD-trained decision model claiming 20-200x faster, 40-400x cheaper classification than frontier LLMs, alongside Gemini 3.8 Live and Neon.

TypeSafe's Jev is a 'System One' decision model trained with RLCD, claiming 20-200x faster and 40-400x cheaper classification and routing than frontier LLMs with free output tokens and no hallucinated text. Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, supporting 97 languages and async tool calls, debuting #1 on Artificial Analysis' speech-to-speech index at 82.6. Periodic Labs' Neon is a ~1T-parameter XRD analysis model trained with RL on proprietary lab data using 1,300 H200s, lifting FrontierXRD success from 2.7% to 55.3% and beating GPT-6 Astra at lower inference cost.

Latent Space · 19h agoModel release1