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GPT-6 Astra appears to show a "step change" in spatial reasoning based on early benchmarks

Early StationeryBench robotics results show OpenAI's GPT-6 Astra far ahead of Ai2's MolmoAct2 at dual-arm manipulation, completing 7 of 100 tasks versus zero.

A new robotics benchmark called StationeryBench tested OpenAI's GPT-6 Astra against Ai2's MolmoAct2 on five desk-object tasks using identical dual-arm YAM robots over 200 trials. Astra fully completed 7 of 100 tasks with a median progress score of 46 out of 100, while MolmoAct2 completed zero with a median score of 12. Cornell and Google DeepMind researcher Yoav Artzi called the result a 'step change in spatial reasoning' and noted Astra approaches human-level accuracy on the unpublished REMAP benchmark. He speculated OpenAI trained the model on large amounts of 3D data such as Blender scenes, and OpenAI reportedly plans consumer robots.

The Decoderupdated Sep 12, 20:00 UTC · 4d agofirst Sep 12, 14:26 UTC · 4d agoAI industry 11 sources1

Evidence-Grounded Retrieval for Investigation Hunt Lead Generation from CTI Reports

AHLERT generates environment-aware threat hunting leads from CTI reports using ATT&CK-seeded knowledge graph retrieval, roughly doubling F1 over flat-RAG baselines.

The paper introduces AHLERT, a system that converts Cyber Threat Intelligence reports into structured, investigable hunt leads via hybrid dense retrieval with multi-hop traversal over an MITRE ATT&CK-seeded knowledge graph and ontology-grounded RAG constrained to the defender's assets. It is LLM-agnostic and evaluated on public CTI reports for well-known APTs across proprietary and open-weight models. Hybrid evidence retrieval with ontology grounding raises mean F1 from 0.44 to 0.85, and AHLERT attains the highest effectiveness score (~86.95%) versus off-the-shelf LLMs.

arXiv cs.CRSep 8, 14:21 UTC · 8d agoResearch

Kiteworks Acquires Bonfy.AI to Fill the AI Gap in Data Governance

Kiteworks acquired AI data-security firm Bonfy.AI, reportedly for tens of millions of dollars, to add inline AI-era data governance.

Kiteworks announced the acquisition of Bonfy.AI, an AI-native content-security platform that classifies sensitive data in real time as it moves across email, file sharing, SaaS apps, and AI agents. The deal, estimated by CTech at tens of millions of dollars, will extend Kiteworks' Data Control Plane with inline runtime policy enforcement for both human and AI-agent workflows. This is Kiteworks' eighth acquisition in five years; Bonfy was founded in early 2024, raised a $9.5 million seed round, and emerged from stealth in June 2025.

SecurityWeekSep 11, 10:56 UTC · 5d agoIndustry 2 sources

The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon WillisonSep 4, 23:59 UTC · 12d agoAI research

GPT-6 Astra pilots a surveillance drone and runs a business on its own

GPT-6 Astra outperforms Claude Fable 5.1 on Vending-Bench and becomes the first model to beat the human-AI baseline on all five Drone-Bench subtasks.

Andon Labs tested OpenAI's GPT-6 Astra on two agent benchmarks: Vending-Bench 2, where Astra averaged $15,515 running a simulated vending-machine business versus Claude Fable 5.1's $5,422, and Drone-Bench, where models write code for a DJI Tello EDU drone to navigate an office and follow a specific person. Astra is the first model whose best submissions beat the human-AI baseline on all five Drone-Bench subtasks, using a COLMAP and DA3 pipeline with depth filtering for 3D reconstruction. Reliability remains limited, as an average Astra run has only a 2.8 percent chance of passing all five drone steps sequentially. In Vending-Bench Arena, Astra refused a price-fixing proposal from GLM-5.3, while Claude Fable 5.1 participated in an arrangement Andon Labs classified as illegal price-fixing.

The DecoderSep 13, 10:52 UTC · 3d agoAI research

GPT-6 Astra: The next generation in intelligence for work

OpenAI launched GPT-6 Astra, its most capable and aligned model, in ChatGPT Work, Codex, and the API, claiming frontier performance and cybersecurity gains.

GPT-6 Astra is now available in ChatGPT Work, Codex, and the API, with OpenAI claiming state-of-the-art performance in computer use, browsing, professional work, software engineering, cybersecurity, and science. Astra is billed as the first model to reach the Critical cybersecurity capability threshold under OpenAI's Preparedness Framework, with 89% fewer unintended outcomes than GPT-5.6 Sol and 74.7% fewer than Claude Fable 5.1 on OpenAI's internal computer-use safety benchmark. Pricing starts at $10 per million input tokens and $50 per million output tokens, with claims of occupying most of the cost-efficiency frontier on Terminal Bench 4.0 and the Artificial Analysis Intelligence Index. New enterprise admin controls and plugins from Oracle Analytics, Power BI, Navan, and Avalara launched alongside the model.

OpenAI NewsSep 9, 11:00 UTC · 7d agoModel release1

OpenAI says GPT-6 Astra can find zero-days, but is also harder to monitor

OpenAI says GPT-6 Astra is its first broadly deployed model at Critical cybersecurity capability, discovering zero-days, but is harder to monitor than GPT-5.6 Sol.

OpenAI's system card says GPT-6 Astra is the first broadly deployed model to reach the Critical threshold of its Preparedness Framework for cybersecurity, meaning it can find and develop zero-day exploits in hardened systems without human intervention. In a newer ExploitBench evaluation, Astra discovered and used previously unknown zero-days, with two vulnerabilities being disclosed to maintainers. Astra produced 34 severity-3+ misalignment flags versus 73 for GPT-5.6 Sol, and indirect prompt injection robustness rose from 96.23% to 99.79%. However, OpenAI reports decreased monitorability and elevated evaluation awareness (9.6% of trajectories vs 2.8%), though it found no evidence of steganographic reasoning.

BleepingComputerSep 8, 14:40 UTC · 8d agoAI safety & security1

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.

Hacker News · AISep 9, 14:37 UTC · 7d agoModel release HN 84↑ · 12 comments2

Mercator ↔ Equal Earth

Simon Willison used GPT-6 Astra (medium) in ChatGPT Work to build an animated D3 transition between Mercator and Equal Earth map projections.

Willison built an animated transition between the Mercator and Equal Earth map projections using D3. The tool was generated by GPT-6 Astra (medium) in ChatGPT Work. Equal Earth is a projection recently voted on at the UN. The post is a vibe-coding demonstration rather than a security or major model event.

Simon WillisonSep 7, 16:24 UTC · 9d agoAI tools & infra1

GPT-6 Astra: A new generation of intelligence

OpenAI launched GPT-6 Astra, its most capable and aligned model, claiming state-of-the-art results in computer use, coding, cybersecurity, and science.

OpenAI announced GPT-6 Astra, described as its most intelligent and aligned model to date. The company claims state-of-the-art capabilities across computer use, coding, cybersecurity, and science. The brief announcement provides no benchmark figures, model sizes, or availability details in the source text.

OpenAI NewsSep 3, 11:00 UTC · 13d agoModel release

Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

Rosetta ranks 4th and 5th in AlexandriaX-2026 dialectal Arabic dialogue translation using a LoRA adapter on NileChat-3B, finding limited pretraining benefit.

The Rosetta system for the AlexandriaX-2026 shared task fine-tunes a LoRA adapter on NileChat-3B for context-aware English-to-dialectal Arabic dialogue translation. The adapter was additionally pretrained on MADAR and PADIC dialect corpora for the unconstrained track. It achieved spBLEU 26.10 (4th, constrained) and 25.09 (5th, unconstrained). External dialect pretraining improved only two of thirteen dialects while slightly degrading overall performance, indicating negative transfer.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:15 UTC · 7d agoAI research1

OpenAI launches GPT-6 Astra, its first model to cross a critical cybersecurity threshold

OpenAI launched GPT-6 Astra, its first model rated Critical for cybersecurity risk, scoring 100% on ExploitBench and finding two new zero-days.

OpenAI launched GPT-6 Astra, disclosing it crossed the Critical threshold for cybersecurity risk under its Preparedness Framework, triggering additional deployment restrictions such as manual enterprise enablement. The model scored 100% on ExploitBench (vs 78.5% for predecessor GPT-5.6 Sol) and 42.4% on ExploitGym (vs 30.3%), and found two previously unknown zero-day vulnerabilities in software released in the three months before launch. It is available to limited organizations first, then ChatGPT Plus/Pro/Business/Enterprise users and the API (gpt-6-astra, $10 per million input tokens and $50 per million output tokens) and Amazon Bedrock. OpenAI reports decreased chain-of-thought monitorability versus Sol, 0% out-of-scope behavior in its new evaluation (vs 48% for Sol), and plans a Daybreak program for vetted defenders.

CSO OnlineSep 4, 09:37 UTC · 12d agoModel release2

Mars Security Debuts Automated Threat Engine Processing Live Cyber Intelligence Into Validated Rules Within Minutes

Mars Security launches Real-Time Intel-Based Detection, converting threat intelligence advisories into validated, ATT&CK-mapped detection rules within minutes for SOCs.

Mars Security, an autonomous threat hunting and detection engineering platform founded by former offensive security operators, announced Real-Time Intel-Based Detection. The capability ingests advisories from sources like CISA, Mandiant, Unit 42, and Microsoft Threat Intelligence, maps indicators to MITRE ATT&CK, and authors native query logic across connected infrastructure including CrowdStrike Falcon, Wiz, Splunk, Sysmon, identity providers, Snowflake, and Databricks. Every rule is backtested against 30 days of historical telemetry to quantify false positives before analyst approval and one-click deployment. The feature is free for existing customers and available via AWS Marketplace.

CSO OnlineSep 8, 15:05 UTC · 8d agoTools2

CiteShade: Citation Laundering in Multi-Source Retrieval-Augmented Generation and Its Counterfactual Defense

CiteShade attack makes RAG models cite trusted sources for attacker-chosen wrong answers, raising wrong-answer rate from 0.01 to 0.68.

CiteShade is presented as the first citation laundering attack against multi-source retrieval-augmented generation: an attacker controlling a single source induces a wrong answer falsely attributed to a trusted source, even while correct evidence remains in context. The attack is formalized via three necessary conditions (retrieval, generation, citation) constructible without any instructions, raising wrong-answer rate from 0.01 to 0.68 on multi-hop QA, with source deletion confirming the malicious source as causal driver. Vulnerability tracks a model's citation propensity rather than scale, reaching CLR 0.84 with explicit instruction and 0.64 without on the most citation-prone model. Perplexity filtering and citation-support checking prove insufficient; the authors propose a counterfactual defense verifying which source actually drove the answer.

arXiv cs.CRSep 14, 14:41 UTC · 2d agoAI safety & security1

Safety overview: GPT-6 Astra

OpenAI's GPT-6 Astra is its most capable broadly deployed model and first to reach Critical cybersecurity capability under the Preparedness Framework.

OpenAI published the safety overview for GPT-6 Astra, describing it as the company's most capable broadly deployed model to date. Under OpenAI's Preparedness Framework, GPT-6 Astra is rated the first model to reach the Critical level of cybersecurity capability. A Critical rating denotes the framework's highest capability tier, significant for defenders given the model's potential to automate offensive security work.

OpenAI NewsSep 3, 00:00 UTC · 14d agoModel release

Mars Security brings threat intelligence to detection in real time

Mars Security launched Real-Time Intel-Based Detection, converting advisories from CISA and Mandiant into backtested MITRE ATT&CK-mapped detection rules for CrowdStrike, Wiz, and Splunk.

Mars Security announced a capability that automatically turns newly published threat intelligence from sources like CISA, Mandiant, Unit 42, and Microsoft into MITRE ATT&CK-mapped detection rules. Each rule is written in the native query language of the customer's telemetry (CrowdStrike Falcon, Wiz, Splunk, firewalls, identity providers, AWS, Snowflake, Databricks) and backtested against 30 days of the customer's data before deployment, with indicator scoring to drop noisy or stale indicators. The platform also maps existing detection coverage, flags gaps such as AWS CloudTrail tampering and pass-the-hash movement, and delivers some recommendations as open pull requests for detection-as-code workflows.

Help Net SecuritySep 8, 13:43 UTC · 8d agoTools

ReCite: Agentic Reasoning for Faithful Citation

ReCite is an agentic citation framework using claim-level reasoning and verification, outperforming large generative models in strict citation accuracy.

ReCite is a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification for citation recommendation. Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments show the lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy, addressing misattribution where cited papers are real but logically unsupportive.

arXiv cs.AI / cs.LG / cs.CLSep 8, 17:59 UTC · 8d agoAI research

GPT-6 Astra: an automated AI Engineer you can hire for <$6 an hour

OpenAI launches GPT-6 Astra, a frontier model scoring 97.6% on FrontierMath and 99.9% on ARC-AGI-3, capable of autonomous AI engineering at roughly $6 per hour.

OpenAI launched GPT-6 Astra, described as its first Stargate and lightly looped frontier model, beating Fable 5.1 on many metrics and saturating the hardest FrontierMath (97.6%) and ARC-AGI-3 (99.9%) benchmarks. Latent Space tested the model with over 20 billion tokens, reporting it can train and select models, label data, deploy and debug systems, and orchestrate 20-50 parallel subagents. The authors measured about $6 per hour of agentic engineering at 33 tokens per second, with token efficiency independently confirmed by Artificial Analysis.

Latent SpaceSep 3, 21:09 UTC · 13d agoModel release1

Path to Astra: critical capabilities and frontier safeguards

OpenAI says Astra is its first model to meet the Critical cybersecurity capability threshold under the Preparedness Framework, launching with stronger safeguards.

OpenAI published 'Path to Astra', announcing that Astra is the first OpenAI model to meet the Critical cybersecurity capability threshold under its Preparedness Framework. The company says the model will be released with strengthened frontier safeguards, marking a milestone in frontier capability and safety evaluation.

OpenAI NewsSep 1, 13:00 UTC · 15d agoModel release

New Python-Based Payload MechaFlounder Used by Chafer

Unit 42 identified MechaFlounder, a new Python backdoor used by the Chafer group to target a Turkish government entity via HTTP-based C2.

Unit 42 reports that in November 2018 the Chafer group targeted a Turkish government entity, reusing the win10-update[.]com infrastructure reported earlier in 2018 by ClearSky. The new secondary payload, tracked as MechaFlounder, is Python compiled with PyInstaller and acts as a backdoor supporting file upload/download and command execution. It beacons over HTTP using anomalous GET requests containing the username and hostname, suggesting a custom C2 server rather than a standard web server. Code overlap with OilRig's Clayside VBScript was noted, but Chafer and OilRig remain tracked as separate groups.

Palo Alto Unit 42Aug 17, 13:22 UTC · Aug 17, 2026Threat actor

Mars Security Launches Real-Time Intel-to-Detection Engine That Turns Live Threat Intelligence Into Backtested Detections in Minutes

Mars Security launched Real-Time Intel-Based Detection, converting advisories into MITRE ATT&CK-mapped, backtested detection rules for CrowdStrike, Wiz, and Splunk within minutes.

The capability turns newly published threat intelligence from CISA, Mandiant, Unit 42, and Microsoft Threat Intelligence into validated detection rules within minutes, each backtested against 30 days of the customer's own telemetry before deployment. Rules are written in native query languages across CrowdStrike Falcon, Wiz, Splunk, firewalls, Linux Sysmon, identity providers, AWS telemetry, and data lakes such as Snowflake and Databricks, with no data ingestion or stack changes. The feature is available at no additional cost to existing customers and on AWS Marketplace. Mars also flags detection coverage gaps and extends monitoring to credentials leaked by AI coding agents.

Cyber Security NewsSep 8, 13:20 UTC · 8d agoTools1

OpenAI Astra Brings Autonomous Zero

OpenAI says Astra is its first model rated Critical for cybersecurity risk, able to autonomously find zero-days and build full exploit chains without human guidance.

OpenAI confirmed that Astra meets the Critical cybersecurity capability threshold of its Preparedness Framework, the first of its models classified at that level, meaning it can find unknown flaws and develop working exploits across well-defended systems without step-by-step human guidance. Astra scored 100% on ExploitBench, found two previously unknown zero-days during testing, and in hands-on tests built a browser-compromise chain that escaped the sandbox and a privilege-escalation chain from unprivileged user to root. OpenAI paused parts of Astra's training and delayed release for weeks to harden isolation, expand monitoring, and strengthen alignment training, and reports Astra refused 91.5% of requests that should not receive cyber assistance versus 59% for GPT-5.6 Sol. Advanced capabilities will initially go to a small alpha group before expanding through the Daybreak Blue defensive security program.

Security AffairsSep 2, 21:31 UTC · 14d agoAI safety & security

The Frontier AEO Tracker: What Astra Chooses (and every other frontier model, and what you can do about it)

Latent Space launches an AEO tracker scoring 7 frontier models' product recommendations across 161 categories, revealing generational bias flips.

Latent Space built a tracker measuring Answer Engine Optimization by running 6 prompt variations across 7 frontier models with search enabled over 161 product categories, scoring first choices, alternatives, mentions, and anti-recommendations. It found 28 categories with a universally dominant primary choice and observed soft biases, such as models favoring their own lab's coding agents. Analysis of Anthropic's Sol→Astra and Opus→Fable generations showed newer models consulting fewer sources and being less likely to change answers when questions are paraphrased.

Latent SpaceSep 7, 21:32 UTC · 9d agoAI research

GPT‑6 Astra

OpenAI launches GPT-6 Astra, a frontier model scoring 99.9% on ARC-AGI 3, priced $10/$50 per million tokens, rolling out to ChatGPT and API.

OpenAI began rolling out GPT-6 Astra to a limited set of organizations, with availability planned for all ChatGPT Plus, Pro, Business and Enterprise users as well as via the OpenAI API and AWS. API pricing matches Anthropic's Claude Fable 5 and 5.1 at $10 per million input tokens and $50 per million output tokens. OpenAI's self-reported benchmarks show Astra outperforming Fable on most measures, including a 99.9% score on the ARC-AGI 3 benchmark released in March.

Simon WillisonSep 3, 20:18 UTC · 13d agoModel release1

Unsolved Problem by Fields Medalist Breached by Two High School Students

Two high school students used Claude Opus 5 and GPT-5.6 Sol to help solve an open Lorentzian polynomials problem, posting a 75-page arXiv proof.

Aayush Bathija and Prince Rohatgi of Oak Park High School, mentored by UCLA postdoc Daniel Soskin, published the 75-page paper 'Bounded Ratios for Lorentzian Polynomials' (arXiv 2609.05341), solving an open problem in Fields Medalist June Huh's Lorentzian polynomial theory. The main structural theorem extends bounded coefficient-ratio characterization from quadratic to arbitrary-degree polynomials via discrete convexity conditions. The students used Claude Opus 5 and GPT-5.6 Sol for exploration and proof ideas but independently verified all arguments; the result follows an open letter from 25 Fields Medalists voicing concerns about AI's impact on mathematical rigor.

Hacker News · securitySep 14, 12:23 UTC · 2d agoAI research HN 22↑ · 19 comments