Anthropic's Claude Opus 5 scored 30.2 percent on ARC-AGI-3, nearly quadrupling GPT-5.6 Sol's previous record of 7.8 percent. The benchmark's developers say the model independently formulated reflection equations, a behavior they had never seen from another model, and attribute to stronger logical reasoning.

The article Anthropic's Opus 5 blows past Fable 5 and GPT-5.6 Sol on the benchmark designed to measure real intelligence appeared first on The Decoder.

Tags: #Claude | #Cyber Crime | #Suchmaschine

The Trump administration is planning targeted bans on Chinese AI models rather than a blanket ban. After public pressure, OpenAI and Google DeepMind signed an open letter opposing regulation of open-weight models, yet OpenAI and Anthropic continue to lobby privately for those same restrictions amid security concerns and powerful business interests.

The article US reportedly favors selective bans over blanket restrictions on Chinese open weight models citing security concerns appeared first on The Decoder.

The post Sakana AI Releases Fugu-Cyber: An Orchestration Model Reporting 86.9% on CyberGym and 72.1% on CTI-REALM appeared first on MarkTechPost.

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The post Google Fixes Dialogflow CX Flaw That Could Have Exposed AI Chatbot Conversations appeared first on IT Security News.

Opus 5 combined with Auto Mode hits a zero percent prompt injection success rate for browser agents across 129 test scenarios. Without those extra protection layers, the rate is 3.7 percent. If these numbers hold up in practice, Anthropic may have cracked one of the biggest security problems facing AI agents that operate in browsers.

The article Opus 5 may have solved browser-based prompt injection, the biggest security flaw haunting AI agents appeared first on The Decoder.

Anthropic's Claude Opus 5 leads the Artificial Analysis Intelligence Index with 61 points, edging out Claude Fable 5 and GPT-5.6 Sol. The model scores highest in analytical quality and coding, and costs up to half as much as Fable 5 at lower reasoning tiers. But the race at the top remains close.

The article Anthropic's Claude Opus 5 costs well below Fable 5 while matching or beating it across most benchmarks appeared first on The Decoder.

The publisher of Harry Potter has received a multimillion-pound payout as a beneficiary of a $1.5bn (£1.12bn) copyright settlement between the AI startup Anthropic and thousands of authors over the use of their protected work to power chatbots.

Bloomsbury, which is home to the bestselling novelists Sarah J Maas and Susanna Clarke as well as JK Rowling, said it had 14,087 titles listed within the settlement, with a proposed compensation of about $3,000 a title.

In March, Anthropic, the cutting-edge artificial intelligence business that gave us the chatbot Claude, published an analysis on the impact of AI on employment, to help us assess the claim that intelligent robots were about to redefine human existence, ending demand for human labor.

Last year in May, Anthropic’s co-founder, Dario Amodei, claimed AI could wipe out half of all entry-level jobs in one to five years. Last January, he told us AI would probably become a “general labor substitute for humans”. In June he said we risk “a world where the economic trade-off dial is stuck on the hypergrowth, hyper-inequality setting”.

Earlier today, we held the random draw to determine attempt order. Below is the official schedule. All times are Berlin local time (CET) and may change as the competition progresses. Check back for live updates.

In case you missed it, you can watch the draw here.

Jump to:

Day One

Day Two

Day Three

DAY ONE

Thursday, May 14 - 1030

chompie of IBM X-Force Offensive Research (XOR) targeting NV Container Toolkit in the NVIDIA category for a total of $50,000 and 5 Master of Pwn points

Le Duc Anh Vu ( @vulda ) of Viettel Cyber Security (@vcslab) targeting OpenAI Codex in the Coding Agent category for a total of $40,000 and 4 Master of Pwn points

Orange Tsai (@orange_8361) of DEVCORE Research Team (@d3vc0r3) targeting Microsoft Edge – Sandbox Escape in the Web Browser category for a total of $175,000 and 17.5 Master of Pwn points

Thursday, May 14 - 1130

k3vg3n targeting LiteLLM in the Local Inference category for a total of $40,000 and 4 Master of Pwn points

Satoki Tsuji (@satoki00) / Ikotas Labs, Inc. targeting Megatron Bridge in the NVIDIA category for a total of $20,000 and 2 Master of Pwn points

Thursday, May 14 - 1300

Angelboy (@scwuaptx) of DEVCORE Research Team and TwinkleStar03 (@_twinklestar03), working with DEVCORE Internship Program targeting Microsoft Windows 11 in the Local Escalation of Privilege category for a total of $30,000 and 3 Master of Pwn points

Emanuele Barbeno, Cyrill Bannwart, Yves Bieri, Lukasz D., Urs Mueller of Compass Security (@compasssecurity) targeting OpenAI Codex in the Coding Agent category for a total of $40,000 and 4 Master of Pwn points

Park Jae Min (@hiariz) targeting Oracle Autonomous AI Database in the AI Database category for a total of $40,000 and 4 Master of Pwn points

Thursday, May 14 - 1400

Satoki Tsuji (@satoki00) / Ikotas Labs, Inc. targeting LiteLLM in the Local Inference category for a total of $40,000 and 4 Master of Pwn points.

Yoseop kim(@pwning_me) targeting Megatron Bridge in the NVIDIA category for a total of $20,000 and 2 Master of Pwn points

Thursday, May 14 - 1500

Ben Koo (@kiddo_pwn) of Team DDOS targeting Mozilla Firefox – Renderer Only in the Web Browser category for a total of $50,000 and 5 Master of Pwn points

Interrupt Labs targeting NV Container Toolkit in the NVIDIA category for a total of $50,000 and 5 Master of Pwn points

Thursday, May 14 - 1530

maitai (@MaitaiThe) of Doyensec (@Doyensec) targeting OpenAI Codex in the Coding Agent category for a total of $40,000 and 4 Master of Pwn points

Thursday, May 14 - 1600

Billy (@st424204), Pan Zhenpeng(@Peterpan980927), Weiming Shi (@bestswngs) of STARLabs SG (@starlabs_sg) targeting LM Studio in the Local Inference category for a total of $40,000 and 4 Master of Pwn points

Marcin Wiązowski targeting Microsoft Windows 11 in the Local Escalation of Privilege category for a total of $30,000 and 3 Master of Pwn points

Thursday, May 14 - 1630

haehae (@haehaeYang) of Out Of Bounds targeting Chroma in the AI Database category for a total of $20,000 and 2 Master of Pwn points

Thursday, May 14 - 1730

chompie of IBM X-Force Offensive Research (XOR) targeting Red Hat Enterprise Linux for Workstations in the Local Escalation of Privilege category for a total of $20,000 and 2 Master of Pwn points

Yoseop Kim(@pwning_me) targeting Mozilla Firefox – Renderer Only in the Web Browser category for a total of $50,000 and 5 Master of Pwn points

Thursday, May 14 - 1800

@rewhiles of Viettel Cyber Security (@vcslab) targeting Anthropic Claude Code in the Coding Agent category for a total of $40,000 and 4 Master of Pwn points

Thursday, May 14 - 1830

Kentaro Kawane of GMO Cybersecurity by Ierae targeting Microsoft Windows 11 in the Local Escalation of Privilege category for a total of $30,000 and 3 Master of Pwn points

Qrious Secure (@qriousec) targeting LM Studio in the Local Inference category for a total of $40,000 and 4 Master of Pwn points

Thursday, May 14 - 1900

haehae (@haehaeYang) of Out of Bounds targeting Megatron Bridge in the NVIDIA category for a total of $20,000 and 2 Master of Pwn points

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DAY TWO

Friday, May 15 - 1030

Ben Koo (@kiddo_pwn) of Team DDOS targeting Red Hat Enterprise Linux for Workstations in the Local Escalation of Privilege category for a total of $20,000 and 2 Master of Pwn points

Stephen Fewer (Rapid7) targeting Microsoft SharePoint in the Server category for a total of $100,000 and 10 Master of Pwn points

Tao Yan (@Ga1ois) and Edouard Bochin (@le_douds) from Palo Alto Networks targeting Apple Safari – Renderer Only in the Web Browser category for a total of $75,000 and 7.5 Master of Pwn points

Friday, May 15 - 1130

Le Duc Anh Vu ( @vulda ) of Viettel Cyber Security (@vcslab) targeting Cursor in the Coding Agent category for a total of $30,000 and 3 Master of Pwn points

Nikolaos Mourousias (@deltaclock), Caue Obici (@caueobici) and Bruno Halltari (@BrunoModificato) of OtterSec targeting LM Studio in the Local Inference category for a total of $40,000 and 4 Master of Pwn points

Sina Kheirkhah (@SinSinology) of Summoning Team (@SummoningTeam). targeting Anthropic Claude Code in the Coding Agent category for a total of $40,000 and 4 Master of Pwn points

Friday, May 15 - 1300

Ruitong from the Abstract Team at the University of Colorado Boulder targeting Red Hat Enterprise Linux for Workstations in the Local Escalation of Privilege category for a total of $20,000 and 2 Master of Pwn points

Friday, May 15 - 1330

Kiyong Kwak of Kakaogames and Song Nuri of Samsung Electronics targeting Apple Safari – Renderer Only in the Web Browser category for a total of $75,000 and 7.5 Master of Pwn points

Orange Tsai (@orange_8361) of DEVCORE Research Team targeting Microsoft Exchange in the Server category for a total of $200,000 and 20 Master of Pwn points

Friday, May 15 - 1400

Sina Kheirkhah (@SinSinology) of Summoning Team (@SummoningTeam). targeting OpenAI Codex in the Coding Agent category for a total of $40,000 and 4 Master of Pwn points

Friday, May 15 - 1430

Billy (@st424204), Bruce Chen(@bruce30262), Pan Zhenpeng(@Peterpan980927), Weiming Shi (@bestswngs ) of STARLabs SG (@starlabs_sg) targeting Megatron Bridge in the NVIDIA category for a total of $20,000 and 2 Master of Pwn points

David Tae, Louis Hur of Out Of Bounds targeting Ollama in the Local Inference category for a total of $40,000 and 4 Master of Pwn points

Friday, May 15 - 1530

Team: Alon Ben Tsur (@iamgweej), Yahav Azran (@_yahav) targeting Red Hat Enterprise Linux for Workstations in the Local Escalation of Privilege category for a total of $20,000 and 2 Master of Pwn points

Friday, May 15 - 1600

@rewhiles of Viettel Cyber Security (@vcslab) targeting Mozilla Firefox – Renderer Only in the Web Browser category for a total of $50,000 and 5 Master of Pwn points

Siyeon Wi targeting Microsoft Windows 11 in the Local Escalation of Privilege category for a total of $30,000 and 3 Master of Pwn points

Friday, May 15 - 1630

Byung Young Yi (@yibarrack) of Out Of Bounds targeting LiteLLM in the Local Inference category for a total of $40,000 and 4 Master of Pwn points

Friday, May 15 - 1700

Emanuele Barbeno, Cyrill Bannwart, Yves Bieri, Lukasz D., Urs Mueller of Compass Security (@compasssecurity) targeting Cursor in the Coding Agent category for a total of $30,000 and 3 Master of Pwn points

Friday, May 15 - 1800

Daniel Cohen Hillel (@0xDACA) targeting NV Container Toolkit in the NVIDIA category for a total of $50,000 and 5 Master of Pwn points

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DAY THREE

Saturday, May 16 - 1100

Le Tran Hai Tung (@tacbliw), dungnm (@dungnm_) and hieuvd (@gr4ss341) of Viettel Cyber Security (@vcslab) targeting Microsoft Windows 11 in the Local Escalation of Privilege category for a total of $30,000 and 3 Master of Pwn points

Satoki Tsuji (@satoki00) / Ikotas Labs, Inc. targeting OpenAI Codex in the Coding Agent category for a total of $40,000 and 4 Master of Pwn points

Sina Kheirkhah (@SinSinology) of Summoning Team (@SummoningTeam). targeting Red Hat Enterprise Linux for Workstations in the Local Escalation of Privilege category for a total of $20,000 and 2 Master of Pwn points

Saturday, May 16 - 1330

Emanuele Barbeno, Cyrill Bannwart, Yves Bieri, Lukasz D., Urs Mueller of Compass Security (@compasssecurity) targeting Anthropic Claude Code in the Coding Agent category for a total of $40,000 and 4 Master of Pwn points

Hyunwoo Kim (@v4bel) targeting Red Hat Enterprise Linux for Workstations in the Local Escalation of Privilege category for a total of $20,000 and 2 Master of Pwn points

Team: Giuseppe Calì (@_gcali) of Summoning Team targeting VMware ESXi in the Virtualization category with the Cross-tenant Code Execution Addon add-on for a total of $200,000 and 20 Master of Pwn points

Saturday, May 16 - 1430

splitline (@_splitline_) of DEVCORE Research Team targeting Microsoft SharePoint in the Server category for a total of $100,000 and 10 Master of Pwn points

Saturday, May 16 - 1600

Byung Young Yi (@yibarrack) of Out Of Bounds targeting Anthropic Claude Code in the Coding Agent category for a total of $40,000 and 4 Master of Pwn points

Nguyen Hoang Thach (@hi_im_d4rkn3ss) of STARLabs SG (@starlabs_sg) targeting VMware ESXi in the Virtualization category with the Cross-tenant Code Execution Addon add-on for a total of $200,000 and 20 Master of Pwn points

Follow the action live! We’ll be posting real-time updates and results throughout the competition on our blog and across social media. Stay up to date by following us on Twitter, Mastodon, LinkedIn, and Bluesky, and join the conversation using #Pwn2Own Berlin and #P2OBerlin for continuous coverage. 

Following an action-packed Day One where $523,000 was awarded for 24 unique 0-day vulnerabilities, Day Two added another $385,750 and 15 unique 0-days, bringing event totals to $908,750 with 39 unique vulnerabilities overall. DEVCORE holds a commanding lead for Master of Pwn with 40.5 points and $405,000, but with one day still to go, anything can happen. Here are the standings as of Day Two but we'll see what the final day of the contest brings. Stay tuned!

We’ll be posting real-time updates and results throughout the competition right here on our blog and across social media. Stay up to date by following us on Twitter, Mastodon, LinkedIn, and Bluesky, and join the conversation using #Pwn2Own Berlin and #P2OBerlin for continuous coverage. 

FAILURE - Unfortunately, Tao Yan & Edouard Bochin of Palo Alto Networks could not get their exploit of Apple Safari – Renderer Only working within the time allotted.

FAILURE - Unfortunately, Stephen Fewer of Rapid7 could not get their exploit of Microsoft SharePoint working within the time allotted.

SUCCESS - Ben Koo (@kiddo_pwn) of Team DDOS used a use-after-free bug to escalate privileges on Red Hat Enterprise Linux for Workstations in the second round, earning $10,000 and 1 Master of Pwn point.

SUCCESS - Dialed in! Nikolaos Mourousias (@deltaclock), Caue Obici (@caueobici) & Bruno Halltari (@BrunoModificato) of OtterSec used a Code Injection bug to exploit LM Studio in the second round, earning $20,000 and 4 Master of Pwn points. Full win!

COLLISON - Although successful on stage, Sina Kheirkhah (@SinSinology) of Summoning Team (@SummoningTeam) targeting Claude Desktop in the Coding Agent category used a bug that was previously known. They still earn $10,000 and 2 Master of Pwn points.

SUCCESS - Le Duc Anh Vu (@vulda17) of Viettel Cyber Security (@vcslab) exploited Cursor, earning $30,000 and 3 Master of Pwn points. Full win!

WITHDRAWAL - Kiyong Kwak of Kakaogames and Song Nuri of Samsung Electronics has withdrawn their entry for Apple Safari – Renderer Only in the Web Browser category.

FAILURE - Unfortunately, Ruitong of Abstract Team, University of Colorado Boulder could not get their exploit of Red Hat Enterprise Linux for Workstations working within the time allotted.

SUCCESS - Sina Kheirkhah (@SinSinology) of Summoning Team (@SummoningTeam) exploited OpenAI Codex in the second round, earning $20,000 and 4 Master of Pwn points.

COLLISON - Although successful on stage, Billy (@st424204), Bruce Chen (@bruce30262), Pan Zhenpeng (@Peterpan980927) & Weiming Shi (@bestswngs) of STARLabs SG (@starlabs_sg) targeting NVIDIA Megatron Bridge used a bug that was previously known. They still earn $2,500 and 1 Master of Pwn point.

WITHDRAWAL - Alon Ben Tsur (@iamgweej), Yahav Azran (@_yahav) have withdrawn their entry for Red Hat Enterprise Linux for Workstations in the Local Escalation of Privilege category.

SUCCESS - Orange Tsai (@orange_8361) of DEVCORE Research Team chained 3 bugs to achieve Remote Code Execution as SYSTEM on Microsoft Exchange, earning $200,000 and 20 Master of Pwn points.

SUCCESS / COLLISON - David Tae & Louis Hur of Out Of Bounds targeted Ollama, hitting a one-vulnerability collision with a previous attempt and earning $28,000 and 3 Master of Pwn points.

FAILURE - Nguyen Thanh Dat (@rewhiles) of Viettel Cyber Security (@vcslab) could not get their exploit of Mozilla Firefox – Renderer Only working within the time allotted.

SUCCESS - Cyrill Bannwart, Emanuele Barbeno, Yves Bieri, Lukasz D., Urs Mueller (@compasssecurity) of Compass Security exploited Cursor in the second round, earning $15,000 and 3 Master of Pwn points.

SUCCESS - Siyeon Wi used an integer overflow bug to escalate privileges on Microsoft Windows 11 in the fourth round, earning $7,500 and 3 Master of Pwn points.

SUCCESS / COLLISON - Byung Young Yi (@yibarrack) of Out Of Bounds targeted LiteLLM, hitting a one-vulnerability collision with a previous attempt and earning $17,750 and 3.75 Master of Pwn points.

SUCCESS - Confirmed! 0xDACA (@0xDACA) & Noam Trobishi (@NTrobishi) used a use-after-free bug to exploit NV Container Toolkit in the second round, earning $25,000 and 5 Master of Pwn points.

After Day One, we awarded $523,000 for 24 unique 0-days! DEVCORE is currently in the lead for Master of Pwn, but a pack of teams are right on their heels. Stay tuned tomorrow for more results and surprises.

Follow the action live! We’ll be posting real-time updates and results throughout the competition on our blog and across social media. Stay up to date by following us on Twitter, Mastodon, LinkedIn, and Bluesky, and join the conversation using #Pwn2Own Berlin and #P2OBerlin for continuous coverage. 

FAILURE - Unfortunately, Le Duc Anh Vu (@vulda17) of Viettel Cyber Security (@vcslab) could not get their exploit of OpenAI Codex working within the time allotted.

SUCCESS - Orange Tsai (@orange_8361) of DEVCORE Research Team (@d3vc0r3) chained 4 logic bugs to achieve a sandbox escape on Microsoft Edge, earning $175,000 and 17.5 Master of Pwn points.

SUCCESS - chompie of IBM X-Force Offensive Research (XOR) used a single bug to exploit NV Container Toolkit, earning $50,000 and 5 Master of Pwn points.

SUCCESS - k3vg3n chained 3 bugs including SSRF and Code Injection to take down LiteLLM. $40,000 and 4 Master of Pwn points. Full win.

SUCCESS - Satoki Tsuji (@satoki00) of Ikotas Labs, Inc. used an Overly Permissive Allowed List bug to exploit NVIDIA Megatron Bridge, earning $20,000 and 2 Master of Pwn points.

FAILURE - Unfortunately, Park Jae Min could not get their exploit of Oracle Autonomous AI Database working within the time allotted. #Pwn2Own #P2OBerlin

SUCCESS - Emanuele Barbeno, Cyrill Bannwart, Yves Bieri, Lukasz D., Urs Mueller of Compass Security (@compasssecurity) used a single CWE-150 bug to exploit OpenAI Codex, earning $40,000 and 4 Master of Pwn points.

SUCCESS - Angelboy (@scwuaptx) & TwinkleStar03 (@_twinklestar03) of DEVCORE Research Team used an Improper Access Control bug to escalate privileges on Microsoft Windows 11, earning $30,000 and 3 Master of Pwn points.

WITHDRAWAL - Ben Koo (@kiddo_pwn) of Team DDOS has withdrawn their entry for Mozilla Firefox – Renderer Only in the Web Browser category

FAILURE - Unfortunately, Interrupt Labs could not get their exploit of NV Container Toolkit working within the time allotted

COLLISON - Although successful on stage, the Ikotas Labs, Inc. team targeting LiteLLM in the Local Inference category used bugs that were previously known. They still earn $8,000 and 1.75 Master of Pwn points.

SUCCESS - Yoseop Kim (@pwning_me) used a CWE-470 bug to exploit NVIDIA Megatron Bridge in the second round, earning $10,000 and 2 Master of Pwn points.

COLLISON - Although successful on stage, maitai (@MaitaiThe) of Doyensec (@Doyensec) targeting OpenAI Codex in the Coding Agent category used a bug that was previously known to the vendor. They still earn $10,000 and 2 Master of Pwn points.

WITHDRAWAL - Yoseop Kim(@pwning_me) has withdrawn their entry for Mozilla Firefox – Renderer Only in the Web Browser category

SUCCESS - haehae (@haehaeYang) of Out Of Bounds chained 2 bugs (CWE-190, CWE-362) to exploit Chroma, earning $20,000 and 2 Master of Pwn points.

SUCCESS - Billy (@st424204), Pan Zhenpeng (@Peterpan980927) & Weiming Shi (@bestswngs) of STARLabs SG (@starlabs_sg) chained 5 bugs (incl. SSRF and Code Injection) to exploit LM Studio, earning $40,000 and 4 Master of Pwn points. Full win!

SUCCESS - Marcin Wiązowski used a heap-based buffer overflow to escalate privileges on Microsoft Windows 11 in the second round, earning $15,000 and 3 Master of Pwn points.

WITHDRAWAL - Qrious Secure (@qriousec) has withdrawn their entry for LM Studio in the Local Inference category.

SUCCESS - Chompie of IBM X-Force Offensive Research (XOR) used a race condition to escalate privileges on Red Hat Enterprise Linux for Workstations, earning $20,000 and 2 Master of Pwn points.

COLLISON - Although successful on stage, Nguyen Thanh Dat (@rewhiles) of Viettel Cyber Security (@vcslab) targeting Anthropic Claude Code in the Coding Agent category used a bug that was previously known to the vendor. They still earn $20,000 and 2 Master of Pwn points

SUCCESS - haehae (@haehaeYang) of Out Of Bounds used a Path Traversal bug to exploit NVIDIA Megatron Bridge in the second round, earning $10,000 and 2 Master of Pwn points. Full win!

SUCCESS - Kentaro Kawane of GMO Cybersecurity by Ierae chained 2 Use-After-Free bugs to escalate privileges on Microsoft Windows 11 in the third round, earning $15,000 and 3 Master of Pwn points.

Day Three added to an already historic event, bringing the final totals to $1,298,250 awarded for 47 unique 0-day vulnerabilities across three days of competition. DEVCORE claimed the title of Master of Pwn with a commanding 50.5 points and $505,000 — a dominant performance across all three days. STARLabs SG finished in second with 25 points and $242,500, followed by Out Of Bounds in third with 12.75 points and $95,750.

Congratulations to all the researchers who participated, and a special thank you to OffensiveCon for hosting. We'll see you at the next Pwn2Own.

Here are the results of Day Three:

SUCCESS/COLLISION - Sina Kheirkhah (@SinSinology) of Summoning Team (@SummoningTeam) used two bugs to exploit Red Hat Linux, but one of the bugs was previously known. He still earns $7,000 and 1.5 Master of Pwn points.

SUCCESS - Le Tran Hai Tung (@tacbliw), dungnm (@dungnm_) and hieuvd (@gr4ss341) of Viettel Cyber Security (@vcslab) used an integer overflow to escalate privileges on #Windows 11. Their 5th round win nets them $7,500 and 3 Master of Pwn points.

SUCCESS - Satoki Tsuji (@satoki00) of Ikotas Labs, Inc. abused an external control to exploit OpenAI Codex and pop a host of calcs. He earns $20,000 and 4 Master of Pwn points.

FAILURE - Unfortunately, Giuseppe Calì of Summoning Team (@SummoningTeam) could not get their exploit of VMware ESXi working within the time allotted.

COLLISON - Although successful on stage, Emanuele Barbeno, Cyrill Bannwart, Yves Bieri, Lukasz D., Urs Mueller (@compasssecurity) of Compass Security targeted Anthropic Claude Code, hitting a one-vulnerability collision with a previous attempt and earning $20,000 and 2 Master of Pwn points.

SUCCESS - Hyunwoo Kim (@v4bel) chained a use-after-free and uninitialized memory bug to escalate privileges on Red Hat Enterprise Linux for Workstations in the fourth round, earning $5,000 and 2 Master of Pwn points.

SUCCESS - splitline (@splitline) of DEVCORE Research Team chained 2 bugs to exploit Microsoft SharePoint, earning $100,000 and 10 Master of Pwn points.

SUCCESS - Nguyen Hoang Thach (@hi_im_d4rkn3ss) of STARLabs SG (@starlabs_sg) used a Memory Corruption bug to exploit VMware ESXi with the Cross-tenant Code Execution add-on, earning $200,000 and 20 Master of Pwn points.

COLLISON - While Byung Young Yi (@yibarrack) of Out Of Bounds successfully demonstrated their exploit of Anthropic Claude Code, the bug used had been previously disclosed. They still earn $20,000 and 2 Master of Pwn points.

Pwn2Own Ireland returns for 2026, and it’s the third year for this event in the Emerald Isle. Despite the dreary Irish skies (and the threat of a random banshee), we had an amazing event, even if we did end up in a jail at the end. With that in mind, we’re excited to return to Cork this fall for yet another great Pwn2Own event. We’ll also be returning to some of the great pubs Ireland has to offer in the evenings and wrapping the event up at a special location (stay tuned for that announcement).

As for the contest itself, it will run from October 6-9, 2026. As always, we’ll have a random drawing to determine the schedule of attempts on the first day of the contest, and we will proceed from there. Registration closes at 5:00 p.m. Irish Standard Time on Oct 1st, 2026. There are no exceptions for late entries, so if you have questions, please contact us at pwn2own@trendmicro.com (note the address). We will be happy to address your issues or concerns directly.

Due to the overwhelming amount of registrations and last-minute entries for our Pwn2Own Berlin event, we’re changing who can enter the contest a bit to ensure it’s fair for all researchers. To enter, you must have received an aggregate bounty payment totaling at least $15,000 during their life-time participation in ZDI. This includes past Pwn2Own events and our regular bug bounty program. We recognize there may be some who haven’t participated in the past with great exploits to demonstrate, so we will also accept up to 10 new contestants at our discretion. We’re capping the number of entries to 80 this year. Once we have 80 qualifying entries, we will close registration. That means if you want to enter, it is in your best interest to contact us sooner rather than later. Please read the rules thoroughly to ensure you meet all the requirements.

Now on to this year’s target categories. We’ll have seven different categories for this year’s event:

-- Mobile Phones
-- Smart Home Devices
-- Wellness
-- Printers
-- Messaging
-- AI Infrastructure
-- AI Coding Agents

Let’s take a look at each category in more detail, starting with mobile phones.

The Target Phones

Back in Amsterdam where this contest originated, it was originally dubbed “Mobile Pwn2Own” and our focus was strictly on phones. Mobile handsets remain at the heart of this event, and some of the Samsung entries from last year were absolutely smashing. As always, these phones will be running the latest version of their respective operating systems with all available updates installed. Last year we also introduced the USB attack vector, but no one submitted an entry for it. We’ll see if that changes this year.

Otherwise, contestants must compromise the device by browsing to content in the default browser for the target under test or by communicating with the following short-distance protocols: near field communication (NFC), Wi-Fi, or Bluetooth. The awards for this category are:

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Smart Home Devices

As you might have noticed, we have eliminated most of the consumer-related devices from this year’s event. However, there are still a few “pro-sumer” devices that still could have an impact on enterprises, and the first of these categories are the devices that control other devices and services. An attempt in this category must be launched against the target’s exposed network services, RF attack surface, or exposed features from the contestant’s laptop within the contest network.

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Wellness Category

This is one of the new categories this year and our first foray into the world of healthcare devices. However, we don’t intend to make this too easy. Entries that require physically pressing any button on the target, or the use of any information, code or PIN printed on the device, are out of scope. Entries that require the contestant to be paired to the target prior to the start of the attempt are not in scope. An attempt in this category must be launched against the target’s exposed network services, RF attack surface, or exposed features from the contestant’s laptop within the contest network.

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Rage Against the Printers

Printers have long been the source of jokes and memes, but they are also an often overlooked attack surface in your office. The printer category always produces some interesting results, often by playing music it shouldn’t or the occasional Rick Roll. We’ve reduced the number of targets in this category this year, but we still expect to see some interesting exploits in these oft unheralded targets.

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The Messaging Category

We introduced WhatsApp as a target last year and came close to seeing a functioning exploit. Sadly, that didn’t happen. However, WhatsApp is used by more than three billion people globally, and some of the messages transmitted can be quite sensitive. That’s why we are bringing it back and hoping for some better results. We know the bugs are out there. We’re just hoping the right researcher decides to show us an exploit that leads to code execution. All of the target handset will be available as clients. Here’s the full prize list for Messaging category:

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AI Infrastructure Category

We introduced these targets at Pwn2Own Berlin, and we saw such…uh…enthusiasm from the community that we decided to immediately bring them back for our Ireland event. An attempt in this category must be launched from the contestant’s laptop. Here’s a look at the targets and awards in the AI Infrastructure category:

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AI Coding Agent Category

Let’s face it. At some point or another, we’ve probably all vibe coded something. There’s no shame in that, but how secure are the tools we use for vibe coding? Well, let’s take the most popular choices and find out. A successful entry must interact with a contestant-controlled resource (e.g. web page, repository, media file) to exploit a vulnerability within the coding agent. The attack vector of the entry must be a common coding agent use case. There are few things out of scope here as well. UI spoofing or misrepresentation unrelated to permission prompts, model jailbreaks or prompt outputs that do not cross security boundaries, and vulnerabilities that require unsafe or permission-less modes are just a few of the things not allowed. As this is a recently updated category, please read the rules carefully to ensure your entry qualifies. Here’s a look at the targets and awards in the AI Coding Agent category:

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Master of Pwn

No Pwn2Own contest would be complete without crowning a Master of Pwn, which signifies the overall winner of the competition. Earning the title results in a slick trophy, a different sort of wearable, and brings with it an additional 65,000 ZDI reward points (instant Platinum status in 2027).

For those not familiar with how it works, points are accumulated for each successful attempt. While only the first demonstration in a category wins the full cash award, each successful entry claims the full number of Master of Pwn points. Since the order of attempts is determined by a random draw, those who receive later slots can still claim the Master of Pwn title – even if they earn a lower cash payout. As with previous contests, there are penalties for withdrawing from an attempt once you register for it. If the contestant decides to remove an Add-on Bonus during their attempt, the Master of Pwn points for that Add-on Bonus will be deducted from the final point total for that attempt. For example, someone registers for the Apple iPhone 15 with the Kernel Bonus Add-on. During the attempt, the contestant drops the Kernel Bonus Add-on but completes the attempt. The final point total will be 20 Master of Pwn points.

The Complete Details

The full set of rules for Pwn2Own Ireland 2026 can be found here. They may be changed at any time without notice. We highly encourage potential entrants to read the rules thoroughly and completely should they choose to participate. We also encourage contestants to read this blog covering what to expect when participating in Pwn2Own.

Registration is required to ensure we have sufficient resources on hand at the event. Please contact ZDI at pwn2own@trendmicro.com to begin the registration process. (Email only, please; queries via social media, blog post, or other means will not be acknowledged or answered.) If we receive more than one registration for any category, we’ll hold a random drawing to determine the contest order. Registration closes at 5:00 p.m. Irish Standard Time on Oct 1st, 2025.

The Results

We’ll be blogging and tweeting results in real-time throughout the competition. Be sure to keep an eye on the blog for the latest information. Follow us on Twitter at @thezdi and @trendaisecurity, and keep an eye on the #P2OIreland hashtag for continuing coverage.

We look forward to seeing everyone in Cork, and we look forward to seeing what new exploits and attack techniques they bring with them.

©2026 Trend Micro Incorporated. All rights reserved. PWN2OWN, ZERO DAY INITIATIVE, ZDI, TrendAI, and Trend Micro are trademarks or registered trademarks of Trend Micro Incorporated. All other trademarks and trade names are the property of their respective owners.

The post SectopRAT Gives Attackers Remote Access to Passwords, Credit Cards, Cookies and Corporate Files appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

The post Tego AI Discloses Second Claude Flaw in a Week: Hidden Link Silently Sends Files to Attackers appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

Willkommen zurück, meine Damen und Herren, zu unserem zweiten Wettbewerb in Berlin! That’s correct (if Google translate didn’t steer me wrong). After our inaugural competition last year, Pwn2Own returns to Berlin and OffensiveCon. Outside of our shipping troubles, we had an amazing time and can’t wait to get back.

Last year, we added Artificial Intelligence as a category with great results. This year, we’re expanding this and splitting it into multiple different categories: AI Databases, Coding Agents, Local Inferences, and a separate category for NVIDIA products. In last year’s contest, NVIDIA targets had wins, losses, and collisions, so it will be interesting to see how they fare this year. The folks from AWS wanted to get into the fray as well, so they stepped up to co-sponsor this year’s event, which allows us to increase the reward for bugs in Firecracker. Of course, we have all of the returning categories as well, including web browsers, containers, servers, virtualization, and operating systems. There’s more than $1,000,000 in cash and prizes available for contestants. Last year, we awarded $1,078,750 for 28 unique 0-days over the three-day event. We’ll see if we can eclipse those numbers in 2026.

The contest begins on May 14, but registration closes on May 7, so don’t delay in getting those submissions in. We’re hoping for maximum participation, so set aside your vibe coding and show us what you can really do. We’re looking forward to some cutting-edge exploitation on display. For 2026, we have a total of 31 targets across 10 categories. Here is a full list of the categories for this year’s event:  

-- Virtualization
-- Web Browser
-- Enterprise Applications
-- Servers
-- Local Escalation of Privilege
-- Containers
-- AI Database
-- Coding Agents
-- Local Inference
-- NVIDIA

Of course, no Pwn2Own competition would be complete without us crowning a Master of Pwn (Meister von Pwn?). Since the order of the contest is decided by a random draw, contestants with an unlucky draw could still demonstrate fantastic research but receive less money since subsequent rounds go down in value. However, the points awarded for each unique, successful entry do not go down. Someone could have a bad draw and still accumulate the most points. The person or team with the most points at the end of the contest will be crowned Master of Pwn, receive 65,000 ZDI reward points (enough for Platinum status), a killer trophy, and a pretty snazzy jacket to boot.

Let's look at the details of the rules for this year's event.

Virtualization Category

Some of the highlights for each contest can be found in the Virtualization Category, and we’re thrilled to see what this year’s event could bring with it. As usual, VMware is the main highlight of this category as we’ll have VMware ESXi return with an award of $150,000. Last year produced the first ESXi exploits in Pwn2Own history, so it will be interesting to see if we get more. Microsoft also returns as a target and leads the virtualization category with a $250,000 award for a successful Hyper-V Client guest-to-host escalation. Kernel-based Virtual Machine (KVM) is our final target in this category with a prize of $50,000.

There’s an add-on bonus in this category as well. If a contestant can escape the guest OS, then gain arbitrary code execution on the virtualization target and obtain arbitrary code execution in the guest operating system on a separate virtual machine managed by the same targeted virtualization target, they’ll earn another $50,000. That could push the payout on a ESXi bug to $200,000. This bonus is for KVM and ESXi only. Here’s a detailed look at the targets and available payouts in the Virtualization category:

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Web Browser Category

While browsers are the “traditional” Pwn2Own target, we’re continuously tweaking the targets in this category to ensure they remain relevant. We re-introduced renderer-only exploits a couple of years ago, and this year, we’ve increased the award to $75,000. In fact, we’ve increased the awards across the board for this category. Here’s a detailed look at the targets and available payouts:

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Enterprise Applications Category

Enterprise applications return as targets with Adobe Reader and various Office components on the target list once again. Attempts in this category must be launched from the target under test. For example, launching the target under test from the command line is not allowed. Prizes in this category run from $50,000 for a Reader exploit with a sandbox escape or a Reader exploit with a kernel privilege escalation, and $150,000 for an Office 365 application. Word, Excel, and PowerPoint are all valid targets. Microsoft Office-based targets will have Protected View enabled where applicable. Adobe Reader will have Protected Mode enabled where applicable.

This year, we’re adding a bonus for Copilot data exfiltration and Copilot action execution. Microsoft just patched a bug like this in Excel, so we know they are out there. If you’re able to exploit Copilot in addition to a Microsoft application, you’ll earn an additional $50,000. There are quite a few rules and scenarios around this add-on, so be sure to read the rules carefully and contact us with questions. Here’s a detailed view of the targets and payouts in the Enterprise Application category:

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The Server Category

The Server Category for 2026 focuses solely on the server components we’re most interested in. These servers are often targeted by everyone from ransomware crews to nation/state actors, so we know there are exploits out there for them. The only question is whether we’ll see any of the competitors bring one of those exploits to Pwn2Own. Last year, the bugs demonstrated in SharePoint ended up being exploited in the wild, so we know people are looking for these with great interest. Microsoft Exchange has been a popular target for some time, and it returns as a target this year as well, with a payout of $200,000. This category is rounded out by Microsoft Windows RDP/RDS, which also has a payout of $200,000. Here’s a detailed look at the targets and payouts in the Server category:

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Local Escalation of Privilege Category

This category is a classic for Pwn2Own and focuses on attacks that originate from a standard user and result in executing code as a high-privileged user. A successful entry in this category must leverage a kernel vulnerability to escalate privileges. Red Hat Enterprise Linux for Workstations returns as our Linux-based target, while Apple macOS, and Microsoft Windows 11 return as targets in this category. Prior exploits in this category have won Pwnie awards, so they’re always interesting to see. Here’s a detailed look at the targets and payouts in this category:

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The Container Category

We’re excited to have this category return for its third season, and we’re hopeful that even more contestants will target one of these container targets. For an attempt to be ruled a success against these three, the exploit must be launched from within the guest container/microVM and execute arbitrary code on the host operating system. Again, with help from AWS, Firecracker returns as a target with a prize of $100,000. Here are the targets and payouts for this category:

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AI Database Category

In the past, AI Hackathons have focused on using AI to develop vulnerabilities or other offensive frameworks. We’re opening up the models and various components themselves for exploitation. The first AI sub-category focuses on databases. An attempt in this category must be launched from the contestant’s laptop. Here’s a look at the targets and awards in the AI Database category:

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The Coding Agent Category

Let’s face it. At some point or another, we’ve probably all vibe coded something. There’s no shame in that, but how secure are the tools we use for vibe coding? Well, let’s take the most popular choices and find out. A successful entry must interact with a contestant-controlled resource (e.g. web page, repository, media file) to exploit a vulnerability within the coding agent. The attack vector of the entry must be a common coding agent use case. There are few things out of scope here as well. UI spoofing or misrepresentation unrelated to permission prompts, model jailbreaks or prompt outputs that do not cross security boundaries, and vulnerabilities that require unsafe or permission-less modes are just a few of the things not allowed. As this is a new category, please read the rules carefully to ensure your entry qualifies. Here’s a look at the targets and awards in the AI Coding Agent category:

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The Local Inference Category

We couldn’t leave local inference and LLMs out of Pwn2Own. These products claim to provide enhanced data privacy, zero-cost inference, lower latency, and fully offline functionality. We’ll see how the security stacks up. An attempt in this category must be launched from the contestant’s laptop within the contest network. Here are the targets and payouts for the Local Inference category:

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The NVIDIA Category

Our last AI sub-category focuses solely on NVIDIA products. For network accessible targets, an attempt must be launched from the contestant's laptop within the contest network. For NV Container Toolkit, the attempt must be launched from within a crafted container image and execute arbitrary code on the host operating system. For Megatron Bridge, entries that leverage vulnerabilities pertaining to pickle deserialization or that leverage a vulnerability when “trust_remote_code=true” are out of scope. Here are the targets and payouts for the NVIDIA category:

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Conclusion

The complete rules for Pwn2Own Berlin 2026 are found here. As always, we highly encourage entrants to read the rules thoroughly if they choose to participate. If you are thinking about participating but have specific configuration or rule-related questions, email us. Questions asked over X (nee Twitter), BlueSky, or other means will not be answered. Registration is required to ensure we have sufficient resources on hand at the event. Please contact ZDI at pwn2own@trendmicro.com to begin the registration process. Registration for onsite participation closes at 5 p.m. Central European Time on May 7, 2026.

Be sure to stay tuned to this blog and follow us on Twitter, Mastodon, LinkedIn, or Bluesky for the latest information and updates about the contest. We look forward to seeing everyone in Germany, and we hope to see some of the best in the world show what they can do – vibe coded or not.

With special thanks to our Pwn2Own Berlin 2026 partners AWS, for providing their expertise and technology.

© 2026 Trend Micro Incorporated. All rights reserved. PWN2OWN, ZERO DAY INITIATIVE, ZDI, ZERO DAY INITIATIVE, TrendAI, and Trend Micro are trademarks or registered trademarks of Trend Micro Incorporated. All other trademarks and trade names are the property of their respective owners.

Dieser Beitrag SharedRoot: Sandbox-Fehler in KI-Agent „Claude Cowork“ gibt VM Zugriff auf Mac-Dateien erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

The post Researcher Claims Working Jailbreak on Top AI Models Including GPT-5.6, Claude Opus 5, and Fable appeared first on Cyber Security News.

Anthropic hat eine neue Funktion für Claude Cowork vorgestellt: Record a Skill, zu Deutsch: einen Skill aufzeichnen. Nutzer können sich dabei selbst beim Arbeiten aufzeichnen. Claude lernt dann daraus eine wiederholbare Fähigkeit, um Aufgaben für dich zu erledigen. Die Reaktionen reichen von Begeisterung bis zu Jobsorgen.  So schnell wie KI-Anwendungen neue Funktionen bekommen, können Nutzer […]

Der Beitrag Claude Cowork kann dich beim arbeiten beobachten – um deinen Job zu erledigen erschien zuerst auf BASIC thinking. Folge uns auch auf Google News und Flipboard oder abonniere unseren Newsletter UPDATE.

Powerful AI models capable of devastating new cyber attacks on governments and businesses are mere months away, intelligence agencies for the Five Eyes have warned in a rare joint statement, urging leaders to “act now”.

The surprising public intervention by signals agencies for Australia, the US, the UK, New Zealand and Canada comes after the Trump administration earlier this month decided to block “foreign nationals” from using a much-hyped AI model built by tech company Anthropic, called Fable.

The post 1Password Lets Claude Sign In Without Revealing Passwords appeared first on TechRepublic.

In March, Anthropic, the cutting-edge artificial intelligence business that gave us the chatbot Claude, published an analysis on the impact of AI on employment, to help us assess the claim that intelligent robots were about to redefine human existence, ending demand for human labor.

Last year in May, Anthropic’s co-founder, Dario Amodei, claimed AI could wipe out half of all entry-level jobs in one to five years. Last January, he told us AI would probably become a “general labor substitute for humans”. In June he said we risk “a world where the economic trade-off dial is stuck on the hypergrowth, hyper-inequality setting”.

Today we're releasing Android 17 and making it available on most supported Pixel devices. Look for new devices running Android 17 in the coming months.

Android 17 marks the start of our transition to an intelligence system, putting your apps at the center. It's shifting to an adaptive-first development standard by introducing mandatory large-screen resizability, all while delivering next-generation privacy, security, media, camera, and performance. We'll cover all that in this post, as well as how we're bringing together next generation tools, libraries, and agent skills to help your apps embrace the opportunity.

Throughout the past year, from our Canary channel to our Beta releases, we’ve collaborated with you in the developer community to build a platform you and your users can trust. To that end, this moment marks the availability of the source code at the Android Open Source Project (AOSP). This allows you to examine the source code for a deeper understanding of how Android works.

Let's dive deeper into Android 17.

An intelligence system

With deep integration between hardware, software and AI, we’re transforming Android from an operating system to an intelligence system. It's about delivering new helpful experiences that anticipate user needs, and it brings more opportunities for engagement with your apps. To that end, Android 17 expands the capabilities of AppFunctions, a platform API with a corresponding Jetpack library. It allows you to contribute your app's unique capabilities as orchestratable "tools" for Android MCP, the on-device equivalent of the Model Context Protocol. AI agents and assistants (like Google Gemini) can discover and execute AppFunctions to perform workflows on behalf of the user with direct access to the app's local state.

The Jetpack library, currently in alpha, makes adding AppFunctions as easy as annotating a class and adding KDoc comments.

/**
 * A note app's [AppFunction]s.
 */
class NoteFunctions(
    private val noteRepository: NoteRepository
) {
    /**
     * Adds a new note to the app.
     *
     * @param appFunctionContext The execution context.
     * @param title The title of the note.
     * @param content The note's content.
     */
    @AppFunction(isDescribedByKDoc = true)
    suspend fun createNote(
        appFunctionContext: AppFunctionContext,
        title: String,
        content: String
    ): Note {
        return noteRepository.createNote(title, content)
    }
}

We’ve also launched an AppFunctions agent skill that analyzes your app’s key workflows, automatically generates the required Kotlin code, optimizes your KDocs for LLM tool-calling, and provides ADB commands for testing and debugging.

The Gemini integration is currently in a private preview with trusted testers, but you can begin preparing your apps now. In addition to ADB commands to execute your AppFunctions, we've provided a test agent app that includes an interface to discover and execute your app functions and simulate an AI agent integration. Join our integration early access program at goo.gle/eap-af for a chance to be among the first apps to deploy AppFunctions to production.

Adaptive-first

Your users no longer rely on a single form factor; they transition between phones, foldables, tablets, laptops, automotive displays, and immersive XR environments. Now, with over 580 million large screen devices in the hands of users and the forthcoming launch of Googlebooks, the next generation of ChromeOS built on the Android stack, adaptive is no longer just a technical goal. It’s a massive opportunity to reach highly engaged users, which is one of the reasons we're shifting to an adaptive-first development standard.

No resizability/orientation restrictions on large screens

To ensure apps deliver a premium experience across all form factors, including mobile devices running in desktop mode on connected displays, Android 17 (API level 37) removes the developer opt-out for orientation and resizability restrictions on large screen devices (sw > 600 dp) for apps targeting API level 37. The system will ignore legacy manifest attributes and runtime APIs, including screenOrientation, setRequestedOrientation(), resizeableActivity=false, and aspect ratio constraints (minAspectRatio/maxAspectRatio). Games (based on app category in Google Play) remain exempt. Your app must be ready to adapt to any window size, respect the user's preferred device posture, and support free-form windowing natively.

Next-gen multitasking: App Bubbles, Bubble Bar, and desktop interactive PiP

Android 17 introduces powerful new windowing capabilities that redefine how users multitask, demanding even greater layout flexibility from your apps:

• App Bubbles: Moving beyond the messaging bubbles API, users can now transform any app into a floating bubble by long-pressing its icon on the launcher. This feature is available across phones, foldables, and tablets, enabling lightweight multitasking for any workflow.
• The Bubble Bar: On large screens (tablets and foldables), the system taskbar now includes a dedicated Bubble Bar to organize, transition between, and dock these floating app bubbles.
• Desktop interactive PiP: In desktop environments, Android 17 introduces interactive Picture-in-Picture (PiP). Unlike traditional PiP windows which are read-only, these pinned windows remain fully interactive while staying always-on-top of other application windows.

App Bubbles and Bubble Bar in action

Activity recreation updates

To prevent disruptive state loss and stutter, Android 17 updates the default behavior for Activity recreation. The system will no longer restart activities by default for typical configuration changes that do not require a full UI redraw (including CONFIG_KEYBOARD, CONFIG_KEYBOARD_HIDDEN, CONFIG_NAVIGATION, CONFIG_TOUCHSCREEN, and CONFIG_COLOR_MODE).
Instead, running activities will receive these updates via onConfigurationChanged(), enabling smooth transitions. If your application explicitly relies on a full restart to reload resources for these changes, you must now explicitly opt-in using the new android:recreateOnConfigChanges manifest attribute.

Continue On

Android 17 adds Continue On to help users seamlessly transition a task between Android devices. The user sees a suggestion for the most recently opened app from their mobile device in their tablet taskbar, providing a one-tap affordance to launch the app and deep-link where they left off. Continue on can support app-to-web transitions, including falling back to using the web if the app isn't installed.

Handoff Suggestion on a Tablet

class MyHandoffActivity : Activity() {

    ...

  override fun onCreate(savedInstanceState: Bundle?) {
    super.onCreate(savedInstanceState)
    // Do stuff
    ...
    // Enable handoff
    setHandoffEnabled(true, null)
  }

  // Override and implement onHandoffActivityDataRequested
  override fun onHandoffActivityDataRequested(handoffRequestInfo: HandoffActivityDataRequestInfo) : HandoffActivityData {
    // Create and return handoff data
  }
}

Go adaptive-first with Jetpack Compose

To help you adapt your apps to meet the new Android 17 requirements, we've launched the Jetpack Compose adaptive skill. This AI-powered developer workflow helps you implement the best adaptive practices:

• Adaptive navigation: Automatically transition between bottom navigation bars on mobile and edge-anchored navigation rails on large screens using NavigationSuiteScaffold from the Material 3 Adaptive library.
• Multi-pane layouts: Implement list-detail and supporting pane layouts natively using Navigation 3 Scenes (ListDetailSceneStrategy and SupportingPaneSceneStrategy) instead of fragile fragment transactions.
• FlexBox & Grid APIs: Utilize Compose 1.11's dynamic layout components to easily adjust row and column spans on the fly, ensuring your content always fills the space beautifully.
• Advanced non-touch input: Leverage Compose 1.11's enhanced trackpad and mouse support, including native focus rings and new APIs (like TrackpadInjectionScope and performTrackpadInput) to easily test and deliver a true "laptop-class" experience on Googlebooks and Desktop Mode.
• Dynamic window states: Leverage Compose's reactive state model to seamlessly adapt your UI when the app transitions from full screen to a floating App Bubble or an interactive Desktop PiP window, ensuring a premium experience even at minimal dimensions.

Android is Compose-first

Compose offers the easiest way to build adaptive apps, and that's just one of the many reasons we believe that all Android UI should be built with Compose. To that end, Android development is now Compose-first. All new Android APIs, libraries, tools, and developer guidance will be built exclusively for Jetpack Compose. Legacy View components (in the android.widget package) and View-based Jetpack libraries (like Fragments, RecyclerView, and ViewPager) are now in maintenance mode. They will receive only critical bug fixes, and no new features.

TIP
Ready to migrate? Use our AI-driven XML to Compose Migration Skill to automatically analyze your legacy View layouts and convert them into highly-adaptive Compose code.

Performance & efficiency

App performance means a smooth user interface, fast app start times, and efficient multitasking; Android 17 has impactful improvements in all of these areas.

App memory limits

Memory usage is one of the silent foundations of overall performance. When a foreground app or service grows unchecked, memory management spikes CPU and battery utilization and eventually leads to the termination of other well-behaved cached apps and background jobs, ultimately forcing slower cold starts and impaired multitasking. 

Starting in Android 17, the system will enforce strict app memory limits based on a device's total RAM, abruptly terminating offending processes. New things to help you navigate these tighter requirements:

• R8 Optimizer: The R8 optimizer significantly reduces your app's bytecode memory footprint by shrinking classes, methods, and fields into shorter names, and stripping out unused code and resources. Use R8 in full mode along with the new R8 configuration analyzer to make sure your app is getting the most from R8.
  

• LeakCanary in Android Studio Panda: The profiler now features native LeakCanary integration as a dedicated task, fully integrated with your IDE and source code.
• ApplicationExitInfo: If your app is terminated by these limits, getDescription() from ApplicationExitInfo will return "MemoryLimiter:AnonSwap".
• On-Device Anomaly Detection: Part of ProfilingManager, you can leverage trigger-based profiling using TRIGGER_TYPE_ANOMALY to automatically capture heap dumps when the memory limit is reached.

val profilingManager = applicationContext
   .getSystemService(ProfilingManager::class.java)

val triggers = ArrayList<ProfilingTrigger>().apply {
  add(ProfilingTrigger.Builder(
    ProfilingTrigger.TRIGGER_TYPE_ANOMALY).build())
}
profilingManager.addProfilingTriggers(triggers)

And, we're working to surface more in-field memory metrics to you within Google Play Console.

Generational garbage collection

Android 17 introduces more frequent, less resource-intensive young-generation collections to ART's Concurrent Mark-Compact garbage collector (GC). By separating short-lived objects from stable, long-lived ones, the system runs frequent, lightweight "young-generation" sweeps rather than expensive full-heap scans, drastically reducing CPU usage, power drain, and UI stutter. Our testing has shown significant improvements in GC interference with application threads and a reduction in the maximum memory resident set size (RSS). ART improvements are also available to over a billion devices running Android 12 (API level 31) and higher through Google Play System updates.

Lock-Free MessageQueue

For apps targeting SDK 37 or higher, the core android.os.MessageQueue now implements a lock-free architecture, significantly reducing missed frames, improving app startup time, and radically improving the performance of busy queues in multithreaded scenarios. Note: This can break apps that use reflection on private MessageQueue fields and methods.  The peekWhen and poll APIs have been added to TestLooperManager for instrumentation testing without relying on MessageQueue internals.

Static final fields now truly final

Starting from Android 17, apps targeting SDK 37 or higher won’t be able to modify “static final” fields, allowing the runtime to apply performance optimizations more aggressively. An attempt to do so via reflection (or deep reflection) will lead to an IllegalAccessException being thrown. Modifying them via JNI’s SetStatic<Type>Field methods family will immediately crash the application.

Custom notification view restrictions

To reduce memory usage we are further restricting the size of custom notification views. This update closes a loophole that allows apps to bypass existing limits using URIs. This behavior is gated by the target SDK version and takes effect for apps targeting API 37 and higher.

Privacy & Security

Maintaining user trust is at the heart of the Android ecosystem. Android 17 introduces robust features that protect sensitive data while simplifying user experiences.

Privacy-preserving choices

Historically, apps required broad, permanent permissions to access information like contacts, precise location and media files. Android 17 continues the shift toward privacy-preserving choices that grant temporary, session-based access only to the data the user explicitly selects:

• System-Level Contact Picker: Utilizing ACTION_PICK_CONTACTS, apps can request temporary access only to specific fields (e.g., email or phone number) chosen by the user, eliminating the need for the broad READ_CONTACTS permission. It also fully supports work/personal profile separation.
• Customizable Photo Picker aspect ratio: UsingPhotoPickerUiCustomizationParams, you can customize the system photo picker to show thumbnails in portrait mode. This is perfect for apps that always display photos and videos in portrait such as video based social media apps.
• System-rendered Location Button: A new system-rendered location button that you can embed in your app grants precise location access for the current session only.
• EyeDropper API: A new system-level API, ACTION_OPEN_EYE_DROPPER, allows your app to create a system-powered eyedropper enabling the user to select color from any pixel on the display. This provides a secure, privacy-preserving color-picking experience that eliminates the need for broad, sensitive screen capture or media projection permissions.

val eyeDropperLauncher = registerForActivityResult(ActivityResultContracts.StartActivityForResult()) { result ->
   if (result.resultCode == Activity.RESULT_OK) {
       val color = result.data?.getIntExtra(Intent.EXTRA_COLOR, Color.BLACK)
       // Use the picked color in your app
   }
}
fun launchColorPicker() {
   val intent = Intent(Intent.ACTION_OPEN_EYE_DROPPER)
   eyeDropperLauncher.launch(intent)
}

Picking a color from anywhere on the screen with the system EyeDropper

Local network access

Apps targeting Android 17 now either require the ACCESS_LOCAL_NETWORK runtime permission or the use of system-mediated, privacy-preserving device pickers for local network communication, such as talking to smart home devices or casting receivers. Because ACCESS_LOCAL_NETWORK falls under the existing NEARBY_DEVICES permission group, users who have already granted other NEARBY_DEVICES permissions will not be prompted again.

SMS OTP protection

Android 17 expands SMS one-time-password (OTP) protection by delaying access to SMS messages for three hours:

• WebOTP Format: Delayed for all apps that are not the intended recipient (domain mismatch).
• Standard SMS OTP: Delayed for all apps targeting SDK 37+.
• Exemptions: Default SMS, assistant, and connected companion apps are exempt. Apps are strongly encouraged to migrate to the SMS Retriever or SMS User Consent APIs.

Post-Quantum Cryptography (PQC)

Android 17 is ready for the next generation of cryptographic security:

• Keystore Integration: Supported devices can generate ML-DSA (Module-Lattice-Based Digital Signature Algorithm) keys in secure hardware to produce quantum-safe signatures, exposed via standard JCA APIs.
• Hybrid APK Signing: Introducing the v3.2 APK Signature Scheme, which combines classical signatures with ML-DSA signatures to secure app delivery.

Safer native dynamic code loading 

Smarter password protection for physical inputs

With Android 17, we're making it safer to enter passwords, PINs, and other secrets when using a physical keyboard by no longer showing the last typed character by default.

Users can still easily customize these display settings to match their preferences (availability may vary by device manufacturer).

These enhanced privacy protections are automatically supported byAndroid's built-in SDK components and will be supported in Compose 1.12 for SecureTextFields.

Media and camera features that empower creators and delight users

Android 17 introduces new creator features that give access to pro-quality cameras and media, all while improving the experience for consumers.

• Eclipsa Video: HDR video standard built upon the SMPTE ST 2094-50 specification that introduces new metadata to help devices adapt content for their display headroom and ambient light conditions, as well as improve the simultaneous display of standard and HDR content.
• RAW14 image format: New support for the RAW14 image format provides a way for your professional camera app to capture the highest level of detail and color depth from compatible camera sensors.
• Vendor-defined camera extensions: Vendor-defined extensions enable hardware partners to define and implement custom camera extension modes, providing access to the best and latest camera features.
• Extended HE-AAC software encoder: A new system-provided Extended HE-AAC software encoder, supports both low and high bitrates using unified speech and audio coding, providing significantly better audio quality for voice messages in low-bandwidth conditions, including support for loudness metadata.
• Versatile Video Coding (H.266): Enables OEMs to add codec support by defining the video/vvc MIME type in MediaFormat, adding new VVC profiles in MediaCodecInfo, and integrating support into MediaExtractor.
• Camera device type: New APIs that query the underlying device type to identify if a camera is built-in hardware, an external USB webcam, or a virtual camera.
• Constant Quality for Video Recording: SetVideoEncodingQuality in MediaRecorder configures a constant quality (CQ) mode for video encoders to ensure uniform visual fidelity across the entire video.

Better support for hearing aids

• Bluetooth LE Audio hearing aid support: Android now includes a specific device category for Bluetooth Low Energy (BLE) Audio hearing aids with the new AudioDeviceInfo.TYPE_BLE_HEARING_AID constant, so your app can distinguish hearing aids from regular headsets to provide a tailored experience for users with assistive listening devices.
• Granular audio routing for hearing aids: Android 17 allows users to independently manage where specific system sounds are played. They can choose to route notifications, ringtones, and alarms to connected hearing aids or the device's built-in speaker, helping to avoid unwanted in-ear interruptions while maintaining a Bluetooth connection for hearing aid management apps.

CameraX and Media3

CameraX and Media3 have been updated for Android 17. They are there to do the heavy lifting, smoothing the rough edges of media development and simplifying building reliable camera capture, smooth media playback, and creative and complex editing experiences.

We've released an agent skill that can migrate legacy Android camera implementations (Camera1 or raw Camera2 APIs) to CameraX.

Note: You'll need to update your CameraX version to either 1.5.2 or 1.6.0+ to avoid a crash related to an added dynamic range mode on Android 17 devices.

Get your apps, libraries, tools, and game engines ready!

If you develop an Android SDK, library, tool, or game engine, it's critical to prepare any necessary updates now to prevent your downstream app and game developers from being blocked by compatibility issues and allow them to target the latest SDK features. Please let your downstream developers know if updates are needed to fully support Android 17.

Testing involves installing your production app or a test app making use of your library or engine using Google Play or other means onto a device or emulator running Android 17 Beta 4. Work through all your app's flows and look for functional or UI issues. Each release of Android contains platform changes that improve privacy, security, and overall user experience; review the app impacting behavior changes for apps running on and targeting Android 17 to focus your testing, including the following:

• Resizability on large screens: Once you target Android 17 (SDK 37), you can no longer opt out of maintaining orientation, resizability and aspect ratio constraints on large screens.
• Dynamic code loading: If your app targets SDK 37 or higher, the Safer Dynamic Code Loading (DCL) protection introduced in Android 14 for DEX and JAR files now extends to native libraries. All native files loaded using System.load() must be marked as read-only. Otherwise, the system throws UnsatisfiedLinkError.
• Enable CT by default: Certificate transparency (CT) is enabled by default. (On Android 16, CT is available but apps had to opt in.)
• Local network protections: Apps targeting SDK 37 or higher have local network access blocked by default. Switch to using privacy preserving pickers if possible, and use the new ACCESS_LOCAL_NETWORKpermission for broad, persistent access.
• Background audio hardening: Starting in Android 17, the audio framework enforces restrictions on background audio interactions including audio playback, audio focus requests, and volume change APIs. Based on your feedback, we’ve made some changes since beta 2, including targetSDK gating while-in-use FGS enforcement and exempting alarm audio. Full details available in the updated guidance.
• NPU access declaration: Apps targeting Android 17 that need to directly access the NPU must declare FEATURE_NEURAL_PROCESSING_UNIT in their manifest to avoid being blocked from accessing the NPU. This includes apps that use the LiteRT NPU delegate, vendor-specific SDKs, as well as the deprecated NNAPI.

Get started with Android 17

Your Pixel device should get Android 17 shortly if you haven't already been on the Android Beta. If you don’t have a Pixel device, you can use the 64-bit system images with the Android Emulator in Android Studio. If you are currently on Android 17 Beta 4.1 and have not yet taken an Android 17 QPR1 beta, you can opt out of the program and you will then be offered the release version of Android 17 over the air.

Getting the Android 17 beta on partner devices

Android 17 is available in beta on handset, tablet, and foldable form factors from partners including Honor, iQOO, Lenovo, OnePlus, OPPO, Realme, Sharp, vivo, and Xiaomi.

For the best development experience with Android 17, we recommend that you use the latest Canary build of Android Studio Quail. Once you’re set up, here are some of the things you should do:

Test your current app for compatibility, learn whether your app is affected by changes in Android 17, and install your app onto a device or Android Emulator running Android 17 and extensively test it.

Thank you again to everyone who participated in our Android developer preview and beta program. We're looking forward to seeing how your apps take advantage of the updates in Android 17, and have plans to bring you updates in a fast-paced release cadence going forward.

For complete information on Android 17 please visit the Android 17 developer site.

Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we explored how to build intelligent on-device features using Gemini Nano through ML Kit's Prompt API.

In this post, we will look at how you can leverage Firebase AI Logic to build cloud-hosted and hybrid AI features: 

• Grounding answers in real-world context
• Routing requests dynamically between cloud and local execution using hybrid inference
• Translating content with custom routing systems

Sometimes a use case requires AI models with greater world knowledge, a much larger context window, or the ability to handle complex queries. In those scenarios, we can leverage cloud models. 

Other times, you want the best of both worlds: using hybrid inference to run on-device when available to lower costs, while falling back to the cloud to ensure compatibility for all devices.

Cloud and hybrid features in Jetpacker: Museum assistant with web grounding, hybrid restaurant review drafting, and  support chat featuring custom-routed live translation.

Let’s look at how we implemented three cloud and hybrid features in Jetpacker:

• a museum assistant with web grounding
• hybrid restaurant review drafting
• hotel support chat featuring custom-routed live translation.

Use LLM grounding for up-to-date informationMuseum assistant chatbot with LLM grounding

The Museum assistant is an interactive chatbot designed to help users plan their museum visits. It provides visitors with up-to-date details regarding specific exhibits, current opening hours, ticket pricing, and more.

Museum assistant is a chatbot that answers questions, such as 

When building AI features, getting the model to answer with fresh, accurate, and specific real-world information is a common challenge. While cloud models possess massive amounts of world knowledge, they might not know about seasonal exhibits or the current day’s opening hours. 

Grounding data is added to the context window to enable the model

To bridge this gap, we can use grounding techniques to add extra context to the model’s context window. The Firebase AI Logic SDK supports three types of grounding:

• URL grounding: Grounding responses using content from a specific webpage (e.g. current ticket prices or museum rules).
• Google Search grounding: Letting the model query the real-time Google search index for up-to-date details.
• Maps grounding: Using Google Maps location data.

In Jetpacker, we dynamically construct the available tools based on enabled feature flags and initialize the generative model using the Firebase AI SDK:

// implementation("com.google.firebase:firebase-ai-logic")

private var toolList = mutableListOf<Tool>()

init {
    if (ENABLE_SEARCH_GROUNDING) {
        toolList.add(Tool.googleSearch())
    }
    if (ENABLE_URL_GROUNDING) {
        toolList.add(Tool.urlContext())
    }
}

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        systemInstruction = content {
            text("You are a helpful museum assistant answering questions about a museum. Use plain text.")
        },
        tools = toolList
    )

When the user queries the assistant, if URL grounding is enabled, we append the specific museum resource URLs directly into the prompt:

val groundingText = if (FeatureFlags.ENABLE_URL_GROUNDING) {
    "\n If the following message above is about the rules and terms to visit Le Louvre, " +
    "if needed answer this urls ${urlList.joinToString()}"
} else {
    ""
}

val prompt = "$text $groundingText"

var response = chat.sendMessage(prompt)

Hybrid inference: On-device review generation with Maps deep link

Not every AI task requires a cloud-based model, and not every device is online. To help developers balance latency, cost, and offline availability, we recently introduced the Firebase API for Hybrid Inference.

In Jetpacker, the restaurant review feature lets users review select topics and automatically drafts a review. To enable this for all users, we prioritize local execution with Gemini Nano, and fall back to cloud models on devices that don’t support Gemini Nano. 

// implementation("com.google.firebase:firebase-ai-logic")
// implementation("com.google.firebase:firebase-ai-ondevice:16.0.0-beta03")


// Initialize the model with hybrid routing configuration
val reviewModel = Firebase.ai.generativeModel(
    modelName = "gemini-3.1-flash-lite",
    onDeviceConfig = OnDeviceConfig(
        inferenceMode = InferenceMode.PREFER_ON_DEVICE
    )
)

The Hybrid Inference API supports four distinct routing modes:

• PREFER_ON_DEVICE: Prioritizes local execution and falls back to cloud if Gemini Nano is unavailable.
• PREFER_IN_CLOUD: Prioritizes cloud execution and falls back to on-device if the device goes offline.
• ONLY_ON_DEVICE: Restricts execution strictly to the device.
• ONLY_IN_CLOUD: Restricts execution strictly to the cloud.

Once the review is generated, we copy it to the clipboard and use an intent to open Google Maps directly to the restaurant's review page, providing a seamless user experience:

private fun copyAndOpenMapsReview(context: Context, reviewText: String, placeId: String) {
    val clipboard = context.getSystemService(Context.CLIPBOARD_SERVICE) as ClipboardManager
    val clip = ClipData.newPlainText("User Review", reviewText)
    clipboard.setPrimaryClip(clip)

    val uri = Uri.parse("https://search.google.com/local/writereview/mobile?placeid=$placeId")
    val intent = Intent(Intent.ACTION_VIEW, uri).apply {
        setPackage("com.google.android.apps.maps")
    }
    context.startActivity(intent)
}

Custom hybrid routing: Hotel support chat translation with simulated personas

The hotel support chat was built to let users finalize logistics and check on hotel details. This feature uses system instructions to configure a localized receptionist assistant. By passing specific information—such as the preferred language and hotel information—in the instructions, we can set up a conversational persona representing a specific hotel.

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        systemInstruction = content {
            text("""
              You are a helpful hotel receptionist at $hotelName only speaking $language. 
              Answer politely in $language. The bar closes at 10pm and breakfast is from 7am to 10am.
              There's someone at the desk 24/7. You can retrieve your luggage from the storage room 
              at the back of the lobby at any time.
              """)
        },
        modelName = "gemini-3-flash-preview"
    )

Because receptionist responses are in the hotel's local language (for example, French for Hotel Le Meurice in Paris), we need to translate messages to the user’s preferred language. 

Hotel support chat messages are automatically translated to the user’s preferred language 

While hybrid models can configure simple routing preferences, complex scenarios require custom routing logic. In Jetpacker, we implement a custom routing stack that takes into account:

• Language identification: Using the on-device ML Kit Language Identification API, we can detect the incoming message language.
• On-device translation (Gemini Nano): ML Kit’s Prompt API lets us translate common language pairs directly on the device, saving bandwidth and cloud cost.
• Cloud translation (Gemini 3 Flash): For more complex languages, we use Gemini Flash 3 to get a higher quality translation.

// implementation("com.google.android.gms:play-services-mlkit-language-id:17.0.0") 

// ML Kit for Language Identification (powered by Google Play Services)
private val languageIdentifier = LanguageIdentification.getClient()

// On-device translator model (prefer Gemini Nano) for translating common language pairs
private val hybridTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        onDeviceConfig = OnDeviceConfig(mode = InferenceMode.PREFER_ON_DEVICE)
    )

// Cloud translator model for more complex language pairs
private val cloudTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash"
    )

When a message needs to be translated, we identify the source language and apply our custom routing logic, executing either on-device or cloud translation:

fun translateMessage(message: SupportChatMessage) {
    viewModelScope.launch {
        // 1. Detect language using ML Kit Language Identification
        val sourceLang = try {
            Tasks.await(languageIdentifier.identifyLanguage(message.text))
        } catch (e: Exception) {
            "Undefined"
        }

        // 2. Custom routing: we've verified the translation quality for English and Korean with Gemini Nano, and will translate message on-device for those two languages
        val routeToCloud = sourceLang != "en" && sourceLang != "kr"

        val prompt = "Translate the following text to $selectedLanguage. Just return the translated sentence: ${message.text}."

        val (translatedText, routePrefix) = if (routeToCloud) {
            val result = cloudTranslationModel.generateContent(prompt)
            result.text to "[Cloud]"
        } else {
            val result = hybridTranslationModel.generateContent(prompt)
            result.text to "[On-Device]"
        }

        if (translatedText != null) {
            _translations.update { current ->
                current + (message.id to "$routePrefix: $translatedText")
            }
        }
    }
}

In this example, the custom routing logic only takes into consideration the translation’s source and target language. However, based on your app’s use case, you can expand the routing logic to include other factors such as the on-device model version, network connectivity, battery status, and more.

Securing the AI Pipelines: Firebase App Check

Lastly, using AI in the cloud opens up possibilities of API key abuse or unauthorized billing. To secure API calls, we integrated Firebase App Check using both Play Integrity (production) and the local Debug Provider (for local development or emulators).

In the JetPackerApplication.kt file, we install the debug provider at startup and trigger anonymous authentication to establish a secure user session:

//  implementation("com.google.firebase:firebase-appcheck-playintegrity") 
//  implementation("com.google.firebase:firebase-appcheck-debug")  
//  implementation("com.google.firebase:firebase-auth") 

override fun onCreate() {
    super.onCreate()
    Firebase.initialize(context = this)
    Firebase.appCheck.installAppCheckProviderFactory(
        DebugAppCheckProviderFactory.getInstance()
    )
    Firebase.auth.signInAnonymously()
}

When building locally on an emulator, App Check prints a local token secret to logcat:

Enter this debug secret into the allow list in the Firebase Console: a8c2dd4c-xxxx-xxxx-xxxx-ef6c114ba27e

Once registered in the Firebase console, local requests are fully verified and authenticated by App Check, protecting our backend while letting us test the app locally.

Conclusion

By combining cloud model capabilities (grounding, system instructions) with on-device capabilities (hybrid routing, translation, security app checks), we created a travel app that is smart, secure, and available offline.

Check out the full source code for Jetpacker on GitHub, and explore the Firebase documentation to get started:

Firebase AI Logic Documentation
Firebase Hybrid Inference API

Learn more

Check out the other parts of this blog post series:

Part 1: Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3 (this post!): Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions. 
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0

Read more of this story at Slashdot.

Our Accessibility Standards and Committee Meetings are now recorded and online!

In the February Accessibility Standards & Compliance meeting, the committee advances a draft Thunderbird accessibility statement for review at the next meeting and starts crafting Thunderbird’s accessibility guidelines. The group identifies work needed to extend guidance into the Bolt design system with a lightweight a11y definition of done for design, and development. Finally, the committee discusses adding automated accessibility checks across platforms (including a minimum “accessibility gate”), reviews proposed success metrics, and closes with an open forum for community questions.

Wo du uns folgen kannst: Facebook, Reddit, Google News, X, Threads

• James enabled adaptive autofill in Nightly for testing, which we believe should provide better results in the URL bar when doing autocomplete!
• Jack updated the illustrations shown on some of our error pages to match the latest approved designs, giving users more polished artwork when the browser encounters connection or security errors!

• Controls for the Memories feature can now be set during Smart Window onboarding

• We’ve disabled the CSS filter implicitly applied to WebExtension pageAction SVG icons across all release channels starting in Firefox 152, completing the deprecation
  • NOTE: The blog post published at WebExtensions API changes in Firefox 149-152 provides to extensions developers more details about this deprecation and links to the related MDN docs.

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

• Amin Amir
• Pranjali Srivastava
• Sam Johnson

New contributors (🌟 = first patch)

•  🌟:23rd: Regression: The new swipe-to-navigation indicator stucks for a moment, when deciding not to navigate the other page
• 🌟Akeem Omosanya: Remove commented-out code in SearchService.sys.mjs
• Amin Amir:
  • 🌟Fix browsingContext.sys.mjs to assign to #contextCreatedHandled instead of contextCreatedHandled
  • Fix missing WITHOUT ROWID SQLite performance optimization in SERPCategorization.sys.mjs
• 🌟Sahaj: Suggest the default target language for translation after changing the detected source language
• 🌟JIANG Zhirui: Breakpad build failed on Windows using VS2026 due to removal of stdext
•  John Iweh: Add “Open in New Tab” and “Open in New Container Tab” options to the context menu for Tabs from Other Devices
• Jak: Bookmarks and History – should respect the “When you open a link, image or media in a new tab, switch to it immediately” setting
• 🌟Andy [:rgbcmy]: Autoplayed next video should also be PIP
•  konyhéa: “Escape” key should collapse the expanded on hover sidebar launcher even if hover is still active.
•  Pranjali Srivastava:
  • Remove icon property from sidebar extensions
  • Show language-agnostic SelectTranslations context menu item when the source and target languages are the same

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons

• Fixed long-standing regression on the autocomplete and datalist popups for extension inline options pages on about:addons (introduced in Firefox 68 by Bug 1532724, fix shipping in Firefox 152) – Bug 1595158

WebExtensions Framework

• Fixed access to web-accessible resources declared with <all_urls> from sandboxed documents (null-principal URLs), restoring extension redirects from the context-menu search flow, starting in Firefox 152 – Bug 2033905

WebExtension APIs

• Added exhaustive test coverage for tabs.move() against additional edge cases related to split-view tabs – Bug 2029092

DevTools

• Andreas Farre improved the Session History tab in the Application panel (still behind devtools.application.sessionHistory.enabled)
  • added support for remote debugging (#2014064, #2016121)
  • made sure that calls to History.replaceState are reflected in the UI (#2037359)
• Julian Descottes [:jdescottes] fixed the most frequent DevTools crash we were observing in Telemetry, adding a guard against IDBTransaction errors when retrieving breakpoints in the Debugger (#2030260)
• Nicolas Chevobbe [:nchevobbe] fixed the image preview tooltip for relative URLs images in constructed stylesheet (#2035503)
• Julian Descottes [:jdescottes] reduced the overhead we had because of network requests monitoring by only decoding response content when the user actually want to see the response (#2026228)

WebDriver

• Amin Amir cleaned up an incorrect variable assignment in our browsingContext module.
• Logan Rosen updated stale references and broken links in our documentation about Marionette.
• Sameem improved the Marionette and WebDriver BiDi screenshot commands to enforce maximum allowed dimensions.
• Leo McArdle fixed the regression in the “log.entryAdded” event, which lacked an error message in the “text” field for the messages of type “error”.
• Henrik Skupin fixed an issue in Marionette where WebDriver:Navigate and WebDriver:Refresh did not handle errors when the underlying navigation failed.
• Henrik Skupin improved geckodriver to detect an early Firefox exit during startup on Android, avoiding up to 60 seconds of unnecessary connection attempts.
• Henrik Skupin updated the geckodriver CI build job to produce a universal macOS binary supporting both x64 and aarch64.

Lint, Docs and Workflow

• Sylvestre ported some linters (e.g. file-whitespace, test-manifest-toml, license, file-perm, rejected-words & more) to Rust to help improve the runtime of the code review bot.
• Dale has been working on migration to moz-src for customkeys, dom/quota and odom/geolocation
  • https://arewemozsrcyet.com/

New Tab Page

• We did our first region-specific trainhop on May 11th (just 15% of the US), and turned on HNT Nova (and sometimes Widgets) for those clients to get some advance-data of its behaviour in the wild! A note that HNT Nova gets turned on for everybody when Firefox 151 ships on May 19th.
  • We’ll be launching a similar experiment in the DE, probably on May 12th, also at 15% population.
• Most of the team is heads down building out a sports-tracking widget, attempting to get that ready in time to be generally available for the upcoming World Cup event.
• Dre landed a new world clock widget, which is currently off by default, but pretty snazzy!

Search and Urlbar

• Nova (URL Bar Design Refresh)
  • Drew and Daisuke continued their work on Nova styling for the Address bar (input and view).
• Search and Suggest
  • Drew finalized two bugs for World Cup and sports suggestions, which were landed and uplifted: one to update the localization string for scheduled games and another to show both teams’ icons in suggestions. Drew also landed and uplifted a fix for rich search suggestion icons being forced into a square aspect ratio.
  • Standard8 updated Ecosia favicons to the latest branding, including QA testing and publishing.
• Settings Redesign (SRD)
  • Stephanie landed a test to ensure search suggestion settings are hidden when quicksuggest is disabled, as well as a patch to resolve TypeScript issues in search.mjs, and is adding test coverage to confirm removed search engines are not displayed in the default engines dropdown.
• General URL Bar and Component Updates
  • Daisuke landed implementation of the context menu on URL bar results, and a fix to show the loading URL in the URL bar when starting up with a homepage.
    • Marco is working on several tasks, including a PDF download / focus stealing issue and allowing arrays to be bound in Sqlite.sys.mjs. Marco also worked on fixes related to Places, such as avoiding replacing the favicons database if it is not corrupt.
  • Standard8 finalized the URL bar test manifest split. Standard8 also upgraded us to TypeScript 6.
  • Moritz landed a fix for URL bar abandonment telemetry being recorded when clicking an engine in the unified search button popup (Bug 2032973), which was also uplifted. Moritz also simplified search mode switcher item activation in tests, and made it so that the unified search button popup closes when installing an open search engine.

Smart Window

• natural language starting with tab close/undo 2035343 with expandable action log 2031508

• assistant rendering feedback up/down 2032994 and markdown table 2027029
• nova styling blur 2027877 and suggestions 2026823
• accessibility screen reader 2028676 and keyboard focus 2037565
• optimize conversation starters extra requests 2030005 and caching 2033430

Storybook/Reusable Components/Acorn Design System

• Nova token updates occasionally, focused on SRD

UX Fundamentals

• Added support for the “SEC_ERROR_CA_CERT_INVALID” certificate error to the Felt Privacy error pages. – 2035942

Settings Redesign

• Settings redesign is being tested and will hopefully go out in Firefox 152!

• Maxx Crawford added a pref to hide the New Tab logo so users can opt out of branding without altering page layout or resorting to CSS overrides.
• Harshit enabled video overlay detection in Nightly 153, allowing you to use the context menu to control videos on more pages! We plan on letting this ride out in Firefox 153.
  • You can try it out on this Instagram reel in Nightly

• A note to WebExtension authors – as part of a planned deprecation announced last month, executeScript and insertCSS are now restricted from moz-extension pages starting in Firefox 152 – Bug 2015559
• Nicolas Chevobbe [:nchevobbe] added support and debugging for modern attr()(which is enabled on Nightly) (#2014751)

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

• Sam Johnson
• Sebastian Zartner [:sebo]

New contributors (🌟 = first patch)

• Immaculate Atim: Switch to using an array instead of an object string for browser.backup.enabled_on.profiles
• liz: Screenshots overlay visible on both splitview browsers
• 🌟 Rahman Mahmutović [:r_m]: Can’t change content in box model in inspector for box-sizing:border-box elements
• Takeru Mitsumori: Fix typo in ID name about-translations-swap-langauges-icon in about-translations.html
• 🌟 Freya Arbjerg [:freyacodes]: Blackboxed columns are ignored
•  tom.passarelli: tab-preview-panel emits unpaired popupshown/popuphidden events, breaking sidebar autohide

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons

• As part of the work for the Project Nova about:addons page restyling, the about:addons sidebar has been migrated to the moz-page-nav and moz-page-nav-button reusable components, improving accessibility and visual consistency with the Firefox Desktop about:settings page – Bug 1881767

WebExtensions Framework

• Implemented WebExtensions negative permissions infrastructure, providing the foundations for enterprise policy “blocked host permissions” features – Bug 1745823
• Restricted host permission changes for MV3 extensions force-installed via enterprise policy (matching similar behaviors provided by Chrome enterprise policy behaviors) – Bug 1904054
  • Thanks to Mike Kaply for the implementation of this enterprise policy enforcement feature.

WebExtension APIs

• Fixed handling of <all_urls> as an API permission in Manifest V3, ensuring the permission is correctly initialized on extension install – Bug 1758306

DevTools

• Rahman Mahmutović [:r_m] made it possible to edit width/height in the box model section of the Layout panel (#1717176)
• Sebastian Zartner [:sebo] improved toggling tools driving in-page highlighters (e.g. the Measuring) (#1262773)
• Sebastian Zartner [:sebo] added a setting to control visibility of HTML comments in the markup view (#1455294)
• Freya Arbjerg [:freyacodes] fixed an issue in script blackboxing (#2036767)
• Alexandre Poirot [:ochameau] replaced custom preference to log RDP messages with MOZ_LOG (#1622857)
• Alexandre Poirot [:ochameau] fixed retrieval of garbage collected script text content (#1758454)

WebDriver

• Sameem updated the “Take Element Screenshot” command from WebDriver Classic to crop screenshots of elements which exceed the viewport. This aligns with the specification and avoids errors when attempting to capture huge elements.
• Alexandra Borovova updated the events for new top-level browsing contexts: we will not send anymore “browsingContext.domContentLoaded” and “browsingContext.load” events for them, instead the “browsingContext.contextCreated” event will be sent when a tab is ready to be used. This is required to align with the expected per-spec behavior.
• Henrik Skupin landed a patch allowing geckodriver to gracefully shut down Firefox when geckodriver itself is terminated.
• Hiroyuki Ikezoe disabled Firefox’s “scroll axis lock” feature so WebDriver actions for wheel input devices can scroll in arbitrary directions when using pan gestures.

Lint, Docs and Workflow

• Added a rule to prevent new uses of Preferences.sys.mjs.
• The browser environment globals within ESLint have now been updated. These include Sanitizer, VideoFrame and a few other new ones.
• Temporal, and some other definitions have been added to TypeScript.

New Tab Page

• Much has happened in the last 2 weeks! Here’s a full bug list, and here are some highlights.
• Dre fixed the List widget that was creating a new list too eagerly on the New Tab Page (2033592) — prevents accidental list creation and improves the Lists UI reliability.
• Maxx Crawford fixed Weather widget small card layout issues with opt-in location options and an error message displayed, resolving card overflow and removing the spurious opt-in error so users see a compact Weather card and correct location prompts on New Tab.
• Reem Hamoui added key dates state to the Sports widget, enabling the Sports card to surface event deadlines/key-date highlights on New Tab so sports users see timely date info.
• Scott Downe added a manage widgets option to the New Tab nova widgets context menu, giving users a direct context-menu entry to open the widget management flow from any widget with Nova enabled.
• Scott Downe added a reusable Newtab widget base component to centralize lifecycle, focus/keyboard handling, DOM templates, and telemetry hooks, reducing duplication and making widget behavior more consistent; see Newtab widget base component.
• Dre converted per-widget expansion handling to a shared widget expansion handler to unify expand/collapse state management and prevent widgets from incorrectly retaining or losing expanded state; see Convert widget expansion handling to shared widget expansion.
• Nina Pypchenko [:nina-py] updated the Sports widget to populate the “follow teams” state from the /teams endpoint, so follow/unfollow toggles now reflect server-side subscriptions and reduce incorrect follow states.
• Scott Downe moved widget menu items within New Tab widgets to standardize menu ordering and action grouping, so users find Add/Remove/Configure entries in expected positions across platforms.
• Dre fixed a World Clock city search bug for the word clocks widget, restoring expected search filtering/matching so city lookups return correct results.
• Scott Downe fixed an issue where the New Tab small weather widget size change didn’t always apply by correcting the widget size update path (JS/CSS layout interactions), improving consistent rendering for small-tile weather across responsive breakpoints and platforms; see Newtab small weather widget size change doesn’t always work.
• Nina Pypchenko [:nina-py] added a group stage section to match highlights in the sports widget on New Tab so users now see stage-aware grouping and stage labels on match highlight cards, making tournament context (group vs knockout) visible while browsing highlights.
• Dre fixed the small world clock widget not expanding to large while editing clocks so users can enter edit mode and expand the widget as expected; the change wires the edit-mode resize handler to update widget size/class during edits.
• Maxx Crawford added WCW OMC message strings so World Cup widget messaging flows on New Tab now display the correct copy (localized where available) instead of falling back to missing-text behavior.
• Reem Hamoui added a “View all” button and a list view for the results tab at medium widget size so Sports widget users on medium New Tab tiles can expand results and scroll full lists without resizing the widget.
• Maxx Crawford added WCW “Watch Live” stream strings to the Sports widget strings bundle so the widget can surface a localized “Watch Live” CTA for applicable events.
• Dre restored VoiceOver reachability for Edit/Remove in World Clock on macOS so macOS VoiceOver users can now focus and activate clock Edit/Remove controls thanks to accessibility role/label and focus-order fixes.
• Maxx Crawford removed the persistent browser logo when all new-tab features (Top Sites, widgets, content feed) are disabled by adding a conditional render guard in the New Tab component, preventing an orphaned logo (2041033).
• Mike Conley added New Tab jest tests to the node tests Tier 1 CI job Run newtab jest tests as part of node tests Tier 1 job to catch regressions earlier in CI
• Irene Ni shipped multiple visual fixes for the Sports widget Sports widget – various visual fixes (spacing, truncation, icon alignment, clipping) to improve readability and layout on constrained viewports.

Picture-in-Picture

• kpatenio adjusted our YouTube site specific wrapper so that the URL bar toggle appears more reliably, especially when selecting videos from the YouTube search page.
• Thanks to Sylvestre for patching some bugs to prevent some spurious console errors!
• Niklas fixed captions on autopip videos failing to sync with the origin videos.

Performance Tools (aka Firefox Profiler)

• Firefox Profiler now has a CLI! We also added a profiler-analysis skill to the Firefox codebase. Once you capture a performance profile, you can ask Claude or an AI to analyze it by providing a link or local path. You can use it to analyze a performance regression or debug an issue if you have a profile at hand.
  • https://www.npmjs.com/package/@firefox-devtools/profiler-cli
  • You can install it with npm install -g @firefox-devtools/profiler-cli@latest

Search and Urlbar

Nova UI refresh

• Drew and Daisuke continued working on reorganizing styles and updating the urlbar for Nova.
• 2019154, 2019152, 2041501, 2040532

Suggest

• Drew landed several Suggest improvements: realtime suggestions colors, sports suggestions received World Cup tweaks, and online Suggest via OHTTP was enabled for eligible users in Firefox 153.
• 2040561, 2039753, 2035614, 2038843

Adaptive autofill

• James fixed soft-block counting to track autofill dismisses, rather than consecutive backspaces on the same autofill, and added telemetry to measure URLs reintegration after blocking.
• 2040819, 2037177

Quick actions

• Dharma created a new Firefox Labs quick action, fixed the Update action button, and re-enabled ScotchBonnet in some tests that were not updated yet.
• Caleb added Calculator support for certain unicode operators.
• 2023169, 1928635, 1923383, 2033861

Multi Context Address Bar

• Moritz continued refactoring the urlbar code: converted some of the js modules to not be system modules, fixed dynamic results templates, incorrect reuse of result rows, and keyboard shortcuts on the unified search button panel.
• 2039297, 2036095, 2039844, 2037933, 2030050

Other

• Marco, Drew and Daisuke fixed several intermittent test failures.
• 2038510, 2023908, 2011584, 1938142, 1971091

Search

• Mark removed old WebExtension-based search engines from the source tree, removed loading of search add-ons from resource://search-extensions/.
• Caleb fixed multiple documentation issues and added a test covering searches from a private window.
• 1904613, 2035878, 2037942, 2033545, 2005724

Places

• Marco removed some unnecessary database transactions, fixed the bookmarks panel folder dropdown on Windows, and resolved several intermittent test failures.
• Thanks to Sam Johnson who fixed the bookmark edit panel showing “mobile” instead of “Mobile Bookmarks”.
• 2039534, 1505800, 2008829, 2029541, 2035084

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers

Anthropic's Opus 4.8: https://www.anthropic.com/news/claude-opus-4-8

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

My research: https://cg.tuwien.ac.at/~zsolnai/
Thumbnail design: https://felicia.hu

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers

📝 The paper is available here:
https://www.anthropic.com/research/natural-language-autoencoders
https://transformer-circuits.pub/2026/nla/index.html

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

My research: https://cg.tuwien.ac.at/~zsolnai/
Thumbnail design: https://felicia.hu

NVIDIA just unveiled Rubin, but this isn't just another GPU. It's a complete AI factory built to power the next generation of artificial intelligence. In this video, we break down NVIDIA's Vera Rubin platform, a fully co-designed AI system that combines the Vera CPU, Rubin GPU, NVLink 6, BlueField-4 DPU, ConnectX-9 SuperNIC, and Spectrum networking into one massive AI infrastructure platform. You'll learn why Rubin delivers up to 50 petaflops of AI inference, 288GB of HBM4 memory per GPU, 3.6 exaflops of compute per NVL72 rack, over 260TB/s of NVLink bandwidth, and why NVIDIA claims up to 10x lower AI inference costs than Blackwell. We also explore why AI companies like Microsoft, OpenAI, Anthropic, Meta, xAI, Google, AWS, Oracle, and CoreWeave are already adopting Rubin, and why NVIDIA believes the future of AI isn't a faster GPU; it's an entire factory built to manufacture intelligence. Is Rubin the biggest leap in AI hardware yet, or the beginning of a completely new era of AI infrastructure?

#NVIDIA #Rubin #AIFactory #AIChips #ArtificialIntelligence #HBM4 #DataCenter

Ein günstiger China-Mini-PC für 220 Euro klingt verlockend – doch beim Bmax B6 Plus lauern versteckte Sicherheitslücken, eine ungültige Windows-Lizenz und sogar Trojaner in den offiziellen Hersteller-Downloads. Wir zeigen euch, welche Gefahren drohen und warum ein professionell aufbereiteter Refurbished-PC oft die deutlich bessere Alternative ist.

Sponsorenhinweis
Mit Mammouth bekommt ihr Zugriff auf KI-Modelle wie GPT, Claude, Gemini, Mistral, Grok, DeepSeek, Perplexity, Flux, Nano Banana und Recraft – alles vereint an einem Ort. Alles im Starterpaket brutto ab 11,90€ / Monat bei monatlicher Laufzeit oder 9,92€ / Monat bei jährlicher Laufzeit mit Vorauszahlung. Mehr Infos: http://mammouth.ai

► Zum Artikel:
Mini-PC Bmax B6 Plus mit Restposten-CPU im Test (€): https://heise.de/s/2AdbZ
Günstige Gebraucht-PCs von erfahrenen Anbietern im Test (€): https://heise.de/s/xK3om

_____

► c’t: https://www.ct.de
► heise online: https://www.heise.de
► heise online auf Instagram: https://www.instagram.com/heiseonline/
► c't auf Instagram: https://www.instagram.com/ct_magazin/
_____
00:00 Einleitung
00:48 Werbung
01:46 Bmax mini Sonderheiten
05:00 Performance
06:50 Alternative zu Mini-PCs aus China
_____
Redaktion & Video: Gordon Hof

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers

📝 The paper is available here:
https://transformer-circuits.pub/2025/linebreaks/index.html

Paper for reindeer vision change - https://royalsocietypublishing.org/rspb/article/280/1773/20132451/50765/Shifting-mirrors-adaptive-changes-in-retinal

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

Euer Messenager verspricht euch Verschlüsselung. Niemand liest mit. So sagen es die meisten Hersteller. EU-Kommission und Mitgliedstaaten arbeiten seit Jahren daran, genau das aufzuweichen. Am 9. Juli haben sie einen umstrittenen Etappensieg errungen.

► Sponsorenhinweis
Mit Mammouth bekommt ihr Zugriff auf KI-Modelle wie GPT, Claude, Gemini, Mistral, Grok, DeepSeek, Perplexity, Flux, Nano Banana und Recraft – alles vereint an einem Ort. Alles im Starterpaket brutto ab 11,90€ / Monat bei monatlicher Laufzeit oder 9,92€ / Monat bei jährlicher Laufzeit mit Vorauszahlung. Mehr Infos: http://mammouth.ai

► Zum Artikel: https://heise.de/s/1ZKdp
► Kommentar Volker Briegleb: https://heise.de/s/m1Ax4
_____

► c’t: https://www.ct.de
► heise online: https://www.heise.de
► heise online auf Instagram: https://www.instagram.com/heiseonline/
► c't auf Instagram: https://www.instagram.com/ct_magazin/
_____
Redaktion: Gordon Hof
Video: Carine Kinarian

0:00 Einleitung
0:20 WERBUNG
1:19 Worum gehts?
2:41 Parlament sagt Nein
3:21 Tricks und Tricks
4:33 Mehrheit dagegen?
5:20 Und jetzt?
6:12 Unsere Einordnung
7:08 Fazit
7:40 Ciao!

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers

📝 The paper is available here:
https://www.anthropic.com/research/AI-assistance-coding-skills

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

The global AI race just entered a dangerous new phase. China is reportedly considering restricting access to its most advanced AI models; DeepSeek is developing its own AI chip; ByteDance and Alibaba are shutting down AI companion services under new regulations, and the Claude Code controversy has escalated into a geopolitical dispute. At the same time, both China and the United States are tightening control over frontier AI technologies, turning artificial intelligence into a strategic national asset. In this video, we break down the biggest AI developments of the week, including China's AI export discussions, DeepSeek's custom silicon ambitions, Huawei-powered AI innovation, Anthropic's Claude Code controversy, MoWorld's real-time AI world model, and why many experts believe we're witnessing the beginning of an AI Cold War. Is the era of open AI coming to an end?

#AI #China #ArtificialIntelligence #DeepSeek #OpenAI #Anthropic #NVIDIA #Huawei

Ein fehlerhaftes Software-Update hat die komplette Berliner Justiz lahmgeleg. Wir erklären, wie es zum IT-Totalausfall kam.

► Sponsorenhinweis
Mit Mammouth bekommt ihr Zugriff auf KI-Modelle wie GPT, Claude, Gemini, Mistral, Grok, DeepSeek, Perplexity, Flux, Nano Banana und Recraft – alles vereint an einem Ort. Alles im Starterpaket brutto ab 11,90€ / Monat bei monatlicher Laufzeit oder 9,92€ / Monat bei jährlicher Laufzeit mit Vorauszahlung. Mehr Infos: http://mammouth.ai

► Zu den Artikeln:
Berliner Justiz-IT nach Lizenzproblemen komplett ausgefallen: https://www.heise.de/-11363631.html
Software-Update Ursache von IT-Problemen der Berliner Justiz: https://www.heise.de/-11365024.html
_____

► c’t: https://www.ct.de
► heise online: https://www.heise.de
► heise online auf Instagram: https://www.instagram.com/heiseonline/
► c't auf Instagram: https://www.instagram.com/ct_magazin/
_____
Redaktion & Video: Gordon Hof

Anthropic hat eine neue Funktion für Claude Cowork vorgestellt: Record a Skill, zu Deutsch: einen Skill aufzeichnen. Nutzer können sich dabei selbst beim Arbeiten aufzeichnen. Claude lernt dann daraus eine wiederholbare Fähigkeit, um Aufgaben für dich zu erledigen. Die Reaktionen reichen von Begeisterung bis zu Jobsorgen.  So schnell wie KI-Anwendungen neue Funktionen bekommen, können Nutzer […]

Der Beitrag Claude Cowork kann dich beim arbeiten beobachten – um deinen Job zu erledigen erschien zuerst auf BASIC thinking. Folge uns auch auf Google News und Flipboard oder abonniere unseren Newsletter UPDATE.

Bugfixes

• Improve Claude adaptive thinking handling across more response shapes.
• Avoid OpenAI Responses phase handling that could break some conversations.
• Preserve grep symlink paths in search results. (@remixz)
• Preserve Mistral reasoning history across turns.
• Stabilize Mistral prompt caching.
• Use the correct prompt cache keys for each SDK.
• Fix MiniMax M3 thinking variant selection.

Desktop

Improvements

• Support the current server terminal transport.
• Support current-server review data in the desktop app.
• Update server discovery flows for current servers.
• Support current-server session actions, including prompts and commands.
• Render current-server session timelines.
• Stream current-server events in the desktop app.
• Detect legacy and current servers so the desktop app can work with both.

Bugfixes

• Restore optimistic timeline updates while responses are still streaming.
• Hide legacy-only features when connected to current servers.
• Preserve inline file mentions when sending prompts to legacy servers.
• Stop auto-accepting config permissions on current servers.
• Keep current servers out of the legacy layout.
• Show plain file contents in file-specific tabs instead of review diffs.
• Keep the prompt input agent toggle in sync.
• Keep paginated session timelines in the right order.
• Restore directory-scoped session status for legacy servers.
• Reload legacy session progress after hydration.

Thank you to 2 community contributors:

• @dleopold:
  • fix(app): classify existing web profiles for layout transition (#38117)
• @remixz:
  • fix(opencode): preserve grep symlink paths (#38581)

The post Meet the New Claude Opus 5: Frontier-Class Agentic Coding and Computer Use at Unchanged Opus Pricing appeared first on MarkTechPost.

• Add Claude Opus 5 across the Anthropic, Claude Code, Bedrock, Vertex, Cline, and OpenRouter providers, including 1M context window variants.
• Add Moonshot Kimi K3 support.
• Include the host plugin version in telemetry events.

Fixed

• Correct pricing for the Claude Opus 1M context variants, which overstated costs for requests above 200k tokens.
• Enable native tool calling for Kimi K3 models, fixing empty responses.

Full Changelog: v4.0.10...v4.0.11

Read more of this story at Slashdot.

Anthropic's new flagship model Claude Opus 5 posts top scores in coding and knowledge work at half of Fable 5's token rates. On ARC-AGI-3, a benchmark for novel problem-solving, Opus 5 hits 30.2 percent, nearly four times higher than GPT-5.6 Sol.

The article Anthropic claims its new Claude Opus 5 delivers near-Fable 5 performance at half the token price appeared first on The Decoder.

• [INS-355] Added Hashicorp vault token detector by @MuneebUllahKhan222 in #4819
• Tighten JiraToken v1 verification by @shahzadhaider1 in #5122
• [INT-718] Add TargetNotFoundError for targeted scan targets missing from the source by @bill-rich in #5123
• Fix GitLab project metadata cache and switch to LRU by @kashifkhan0771 in #4727
• Log analyze errors for Anthropic Analyzer by @kashifkhan0771 in #5120
• Add metrics for chunks and results by @mcastorina in #5128
• Update Cloudflare detectors for 2026+ prefixed credential formats (include upstream PR changes) by @kashifkhan0771 in #5111
• [INS-312] Duo API Secret Key Detector by @MuneebUllahKhan222 in #4771
• [Feature] Added SonarQube Cloud "Scoped Organization Token" Detector by @nabeelalam in #4739
• [INS-255] Updated datadog detector to set verificationError in case of a verification error by @MuneebUllahKhan222 in #4661
• Log analyze errors for Postgres analyzer by @kashifkhan0771 in #5131
• Log analyze errors for HuggingFace analyzer by @kashifkhan0771 in #5130
• Engine - Config.SourceManager doc by @amanfcp in #5002
• Update module github.com/go-git/go-git/v5 to v5.19.1 [SECURITY] by @renovate[bot] in #5034
• Retry git clone on transient network errors by @shahzadhaider1 in #5132
• Fix scan_all_installations Rejecting Org Member Personal Repos by @shahzadhaider1 in #5142
• updated detector to include underscore char by @mattbrady-1 in #5121
• [chore] Change job_id in metric to source_type by @mcastorina in #5149
• Posthog regex update by @mattbrady-1 in #5133
• fix(handlers): apk handler now doesnt check for apk extension since json-enumerator and other byte stream methods wouldnt have it by @johannestaas-trufflesec in #5151

New Contributors

• @mattbrady-1 made their first contribution in #5121

Full Changelog: v3.95.9...v3.96.0

Read more →

The post Claude Mythos Just Caught the Attention of Canada’s Banking Regulator appeared first on IT Security News.

The model, available immediately on all of Anthropic's platforms, is priced at $5 per million input tokens and $25 per million output tokens, unchanged from its predecessor, Opus 4.8. It becomes the new default model on Claude Max, Anthropic's premium consumer tier, and the strongest model available on Claude Pro.

The positioning is deliberate. Anthropic is not claiming Opus 5 is its smartest model — that distinction still belongs to Fable 5, and rival systems retain an edge in certain domains. Instead, the company is making a subtler argument that may matter more to enterprise buyers: that the most economically important AI work happens in a middle band of difficulty, where near-frontier intelligence delivered efficiently and cheaply beats frontier intelligence delivered expensively.

"Opus 5 as your daily driver, the model you hand complex work to and review when it's done," an Anthropic spokesperson said in an interview with VentureBeat, describing how the company's lineup now stratifies. "Fable 5 for your most ambitious work, the days-long autonomous projects nothing could take on before... Sonnet 5 for work you run at scale, where speed and cost per call decide what ships. Haiku 4.5 for subagents and instant answers."

How Claude Opus 5 benchmark results stack up against Fable 5 and rival AI models

On paper, the results are striking. Anthropic says Opus 5 sets new state-of-the-art marks on coding and knowledge-work evaluations including Frontier-Bench and GDPval-AA. On Frontier-Bench v0.1, an agentic terminal coding benchmark, Opus 5 scores 43.3 percent — more than double Opus 4.8's 18.7 percent and well ahead of Fable 5's 33.7 percent — at a lower cost per task, according to the company. On ARC-AGI 3, an evaluation of novel problem-solving, Anthropic reports Opus 5 scored three times as high as the next best model. On OSWorld 2.0, a computer-use benchmark, the company says the model surpasses Fable 5's best result at just over a third of the cost.

The numbers come with honest caveats that are themselves notable in an industry prone to superlatives. Anthropic acknowledges Opus 5 remains behind Mythos 5, a competing model, on cybersecurity tasks and biology research, and an OpenAI-family model still leads on one agentic coding benchmark.

The more revealing caveat came from Anthropic itself, when asked where Opus 5 still falls short of Fable 5. The spokesperson's answer amounted to a candid admission about what benchmarks do and don't capture.

"The evals where Opus 5 wins are bounded tasks with a specific outcome, which is where it's strongest. What those evals don't measure is duration," the spokesperson told VentureBeat. "One way to put it: Opus 5 is the best tool for the jobs benchmarks can see, and Fable 5 is what you reach for when the job outruns the benchmark."

Fable 5, by contrast, "is for the longest, most autonomous jobs, where the model has to stay coherent across many connected steps over hours or days with dense source material," the spokesperson said, advising customers to "run both on a representative workload, one bounded task and one long-horizon job." That framing — bounded tasks versus long-horizon autonomy — may become the defining axis of model differentiation in 2026, as benchmarks saturate and the hardest remaining problems involve sustained, multi-day agentic work rather than discrete puzzles.

Why token efficiency is becoming the real battleground for enterprise AI spending

Threaded through the launch is a theme Anthropic clearly wants buyers to absorb: Opus 5 doesn't just score well, it scores well per dollar. The model ships with an adjustable "effort" setting that lets customers trade intelligence for speed and token savings, and Anthropic's charts emphasize performance at a given cost rather than peak performance alone.

Early customers echoed the point with unusual specificity. Harvey, the legal AI company, said Opus 5 achieved similar performance to Opus 4.8's maximum-reasoning mode "while generating 26% fewer tokens on average," according to Niko Grupen, its head of applied research. Richard Pham of Fundamental Research Lab said that on hard financial-modeling tasks, the model averaged nine percentage points higher accuracy "while using roughly one-third fewer turns and tool calls and 60% less time."

Wade Foster, chief executive of Zapier, said Opus 5 topped his company's AutomationBench leaderboard "without spending more tokens than prior Claude models," running a full churn-prevention workflow from start to finish. "Previous models didn't pass; Opus 5 hit 100%," he said. Scott Wu, chief executive of Cognition, the company behind the Devin coding agent, said that on FrontierCode 1.1, "Claude Opus 5 approaches Fable-level performance at half the cost," with particular strength in debugging and root-cause analysis.

The efficiency emphasis reflects commercial reality. Enterprise AI spending is no longer experimental, and inference costs — the price of actually running these models at scale — have become a board-level line item. 

Anthropic's business skews heavily toward API and enterprise usage; according to a February 2026 analysis by Contrary Research, Claude held roughly 40 percent of the enterprise large language model market by usage as of late 2025, and Claude Code alone had reached about $1 billion in annualized revenue. For a company whose customers pay by the token, a model that does more with fewer tokens is not a nice-to-have. It is the product.

Self-verifying AI agents and what they mean for the hidden costs of automation

Beyond the numbers, Anthropic is selling a behavioral story: that Opus 5 verifies its work and iterates until it succeeds. The company offered several examples from testing that read like small parables of machine stubbornness.

In one Frontier-Bench task, the model was asked to reconstruct a machine part as a 3D CAD model from a drawing it was intentionally given no way to view. Rather than fail, Anthropic says, Opus 5 wrote its own computer vision pipeline to extract the geometry from raw pixels — and did so repeatedly, while no competing model solved the task in five attempts. In another case, given a real bug in a popular open-source package manager, the model found the root cause and fixed an edge case the community's own patch had missed; a competing model patched only the symptom and declared victory. An engineer at a trading firm, the company says, used Opus 5 to build a market data feed for a new exchange in a single session and, finding no live feed to validate against, watched the model build its own test harness to check its parsing code.

Customers described similar behavior in the wild. Cristian Rivera, a staff software engineer at Stripe, said he gave the model "a chief-of-staff role over my dev environments" for a weekend: "it built its own monitor, drove each box, and pulled me in only for the judgment calls."

This is the capability enterprises actually care about, and it is worth dwelling on why. The gap between a model that produces plausible output and one that verifies its output is the gap between a demo and a deployable system. Most of the hidden cost of enterprise AI today is human review — engineers checking the machine's work. A model that reliably checks its own work compresses that cost, which is precisely why customers keep citing fewer turns, fewer passes, and less time rather than higher raw scores.

Inside Anthropic's safety strategy: capability gaps, classifiers, and model fallbacks

The launch also showcases Anthropic's increasingly intricate approach to safety — one that now involves deliberately not teaching its models certain skills. The company says its automated behavioral audit found Opus 5 to be its most aligned model to date, scoring 2.3 on overall misaligned behavior, lower than Opus 4.8, Sonnet 5, or Fable 5, with the lowest rates of deceptive behavior and the least susceptibility to being tricked into misuse.

On the capability side, Anthropic says it intentionally avoided training Opus 5 on cyber tasks, as it did with Opus 4.8. The model improved on them anyway — a side effect of general capability gains — and now nearly matches Mythos 5 at finding software vulnerabilities. But it remains far behind at exploiting them: on Anthropic's OSS-Fuzz evaluation, Opus 5 identified vulnerabilities at a 79.4 percent rate, close to Mythos 5's 80 percent, but succeeded at developing exploits in only 4 challenges versus Mythos 5's 13. That asymmetry — strong at defense-relevant discovery, weak at offense-relevant exploitation — appears to be by design, and the safeguards follow the same logic. Anthropic expects Opus 5's cyber classifiers to intervene about 85 percent less often than Fable 5's.

When a classifier does trigger, requests in Claude.ai, Claude Code, and Claude Cowork fall back to Opus 4.8 by default — raising an obvious question: if a request is too risky for one model, why is it acceptable for another? "The model it falls back to has lower capability levels making the risk of harmful use lower as well," the spokesperson said, adding that "there is a message that lets the user know when this occurs and is visible in the chat."

The logic is defensible, but it reveals how AI safety actually works in 2026: risk is not a property of the question alone, but of the question multiplied by the capability of the system answering it. On biology, the calculus runs the other way. Opus 5 is now Anthropic's most capable generally available model for scientific research — scoring 10.2 percentage points higher than Opus 4.8 on the company's internal chemistry benchmark — though the spokesperson acknowledged that "Mythos 5 remains the stronger model for long-horizon, open-ended work like autonomous drug design campaigns."

The business stakes behind the launch: a $380 billion valuation and massive compute bets

The launch lands at a moment of extraordinary commercial momentum — and extraordinary obligations — for Anthropic. Reuters reported in February that the company was valued at roughly $380 billion in its latest funding round, following a period in which, per Contrary Research's analysis, its annualized revenue climbed from about $1 billion at the end of 2024 to a projected $9 billion by the end of 2025, with internal targets reportedly reaching $20 to $26 billion for 2026. Those targets are underwritten by enormous infrastructure commitments, including a reported $30 billion Azure compute deal alongside arrangements with Google Cloud and Nvidia — spending that only pencils out if enterprises keep expanding usage.

That is the context in which Opus 5's pricing strategy makes sense. Holding the price at Opus 4.8 levels while roughly doubling performance on key agentic benchmarks is effectively a steep price cut per unit of capability, designed to widen the funnel of workloads that are economical to automate. Every task that was marginal at Opus 4.8's cost-per-success becomes viable at Opus 5's — and every viable task is recurring token revenue.

The regulatory backdrop has grown more complex as well. A U.S. judge gave final approval this week to Anthropic's $1.5 billion copyright settlement with book authors, Reuters reported, closing a chapter of litigation over the company's early training data. And in June, Reuters, citing Axios, reported that the U.S. government had moved to block foreign access to Anthropic's most advanced models — a reminder that frontier AI is now entangled with export policy in ways that shape which customers can buy what.

Also shipping Friday: a Fast mode running at roughly 2.5 times default speed at twice the base price, automatic fallback routing on the API, and mid-conversation tool changes that no longer invalidate the prompt cache — a small feature that agent developers may appreciate more than any benchmark. Consistent with prior Opus models, Opus 5 carries no data retention requirements for general access, a point the spokesperson flagged unprompted for customers with "a hard zero data retention requirement." Developers can access the model as claude-opus-5 on the Claude API starting today.

Two questions will determine whether the bet pays off: whether Opus 5's efficiency claims survive contact with production workloads at scale, and whether enterprises embrace a world where safety classifiers, not users, sometimes decide which model answers. But the deeper message of Friday's launch is that the AI industry's center of gravity has moved. For three years, the labs competed on what their best model could do on its best day. With Opus 5, Anthropic is competing on something less glamorous and far more lucrative: what a very good model can do every day, for half the price. In a market where the frontier keeps moving, Anthropic is wagering that the real fortune lies just behind it.

The post Tego AI Discloses Second Claude Flaw in a Week: Hidden Link Silently Sends Files to Attackers appeared first on Cyber Security News.

The post Tego AI Discloses Second Claude Flaw in a Week: Hidden Link Silently Sends Files to Attackers appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

• Added Claude Opus 5 (claude-opus-5), now the default Opus model — 1M context, fast mode at $10/$50 per Mtok
• Added sandbox.network.strictAllowlist setting to deny non-allowlisted hosts for sandboxed commands without prompting
• Added DirectoryAdded hook that fires after /add-dir or the SDK register_repo_root control request registers a new working directory mid-session
• Added mcp_server_errors to the headless stream-json init event, listing --mcp-config entries skipped by config validation; terminal runs print a startup warning
• Added the workflowSizeGuideline settings key so the advisory Dynamic workflow size guideline can be set from any settings file; the /config row is hidden while one does
• Added nested subagent forwarding in stream-json: subagents spawned at depth-2+ now appear when --forward-subagent-text is set, keyed by their spawning Agent tool_use id
• Fixed claude -p text output dropping the answer already produced when a turn dies on a mid-stream API error
• Added HTTP status and error text to claude mcp list and /mcp when a server fails to connect, and a warning for MCP config values with hidden leading or trailing whitespace
• Fixed a permission you approved while a self-hosted runner was restarting being dropped when the session resumed, so the approved action now runs
• Fixed the Fable model row showing "Requires usage credits" for plans that include it, when a stale cache had baked the label in
• Fixed a SIGTERM arriving while a self-hosted runner was starting up leaving a stale active row until the lease expired; it now deregisters cleanly
• Added structured failure categories to self-hosted runner spawn and session failures, so hook errors, runner crashes and config errors can be told apart
• Fixed the /model picker showing the merged Opus row as plain "Opus" instead of "Opus (1M context)"
• Fixed copy-on-select inside GNU screen printing base64 into the terminal instead of copying the selection
• Fixed Remote Control clients keeping a stale fast-mode status after a model switch, reconnect, or failed org check
• Fixed CLAUDE_CODE_GIT_BASH_PATH on Windows exiting or being used as bash when the path isn't a bash/sh binary; it's now ignored with a warning
• Fixed Vim mode: pressing ← on an empty prompt now returns to the agent view from NORMAL mode, not just INSERT
• Fixed screen-reader mode rewriting the entire input line on every keystroke instead of echoing only the typed character
• Improved the "Remote Control is only available via api.anthropic.com" error to name the specific setting that caused it
• Improved claude --teleport to show which repo your current checkout points at when it doesn't match the session's repo
• Changed dynamic workflows to default to a medium size guideline (aim for fewer than 15 agents); pick another size or unrestricted with Dynamic workflow size in /config
• Changed managed MCP allowlist/denylist ${VAR} entries to resolve from the startup environment and managed-settings env instead of settings-file env
• Changed the /model picker to highlight only the newest model's name, so the highlight marks the new release rather than an arbitrary subset of the list
• Added the current default workflow size to the running-workflow status line, with a pointer to /config for changing it
• Removed Opus 4.7 from fast mode; /fast now applies to Opus 5 and Opus 4.8
• Updated the claude-api skill to default to Claude Opus 5, with a migration path from Opus 4.8
• Subagents can now spawn nested subagents up to depth 3 by default (was 1); set CLAUDE_CODE_MAX_SUBAGENT_SPAWN_DEPTH=1 to disable nesting

Read more of this story at Slashdot.

Microsoft, along with Meta, Nvidia, and more than 20 other companies, is pushing for open-weight AI models in an open letter. The strategic logic is simple: the more models running on Azure, the less Microsoft depends on expensive OpenAI and Anthropic models. The company is also swapping external models in products like Copilot for its in-house MAI family, which performs significantly worse in independent benchmarks.

The article Microsoft's open-weight AI push is so obviously an Azure play it hurts appeared first on The Decoder.

Tags: #Claude | #Cyber Security | #Künstliche Intelligenz

Sakana AI has updated its Fugu Ultra AI router to version 1.1, claiming gains of up to 7.9 points over v1.0. Independent verification doesn't exist yet. The update adds a Claude Code-compatible endpoint. The service remains unavailable in the EU.

The article Sakana claims its AI model router Fugu Ultra v1.1 now beats Fable 5 without even including it in the pool appeared first on The Decoder.

The post Could Australia’s AI Rules Challenge AI Agents That Can Log In? appeared first on TechRepublic.

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The post FakeAgent Campaign Uses Malicious Bing Ads and Claude.ai Artifacts to Infect Corporate Users appeared first on IT Security News.

On 14 February 2019, OpenAI announced a language model called GPT-2, the precursor to the models that power modern AI chatbots and agents such as ChatGPT and Claude. But OpenAI declared GPT-2 was too risky to release, citing concerns about safety and abuse.

I recall being annoyed at the time that OpenAI would make such a useless announcement: the risks seemed overblown, and without access to the model there wasn’t much for a researcher like me to learn about GPT-2.

Tags: #AMD | #Anthropic

The post SectopRAT Gives Attackers Remote Access to Passwords, Credit Cards, Cookies and Corporate Files appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

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The post SectopRAT Gives Attackers Remote Access to Passwords, Credit Cards, Cookies and Corporate Files appeared first on IT Security News.

The post FakeAgent Campaign Uses Malicious Bing Ads and Claude.ai Artifacts to Infect Corporate Users appeared first on Cyber Security News.

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The post Tego AI Discloses Second Claude Flaw in a Week: Hidden Link Silently Sends Files to Attackers appeared first on IT Security News.

Voice conversations now run on the more powerful Opus and Sonnet models with access to Gmail, Google Calendar, and Slack. Claude is currently the only AI assistant that can compose and send emails directly by voice, giving it an edge over OpenAI and Google, whose voice output still sounds more natural.

The article Claude's voice mode now runs on Anthropic's most capable models across all platforms appeared first on The Decoder.

The post Hackers Use Fake Claude Desktop and Bing Ads to Deliver SectopRAT to 29 Organizations appeared first on Cyber Security News.

The British AI Security Institute and the U.S. Center for AI Standards and Innovation tested Moonshot AI's Kimi K3 on offensive cyber tasks. Kimi K3 scored 32 percent on ExploitBench, compared with 76 percent for leading U.S. models, while its safeguards failed to block exploit development or simulated attacks. The gap between its strong general benchmark scores and weaker cyber performance also fits allegations that Moonshot AI distilled Anthropic's models.

The article Kimi K3 trails frontier US models by a wide margin on cyber exploits, and distillation may explain why appeared first on The Decoder.

The post Claude Cowork Sandbox Escape Flaw Lets AI Agent Read SSH Keys and Cloud Credentials From Host appeared first on Cyber Security News.

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The post Claude Cowork Sandbox Escape Flaw Lets Attackers Access SSH Keys and Cloud Credentials appeared first on IT Security News.

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The post IT Security News Hourly Summary 2026-07-24 09h : 11 posts appeared first on IT Security News.

The post Claude Cowork Sandbox Escape Flaw Lets Attackers Access SSH Keys and Cloud Credentials appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

The post Claude Cowork SharedRoot Sandbox Escape Exposes Mac Files and Cloud Credentials appeared first on Cyber Security News.

Strip away the geopolitics, the hacktivist noise, and the espionage headlines, and one number from the first half of 2026 stands out above everything else: 1,721. That's how many ransomware attacks hit organizations in the United States between January and June, according to new research from Cyble Research and Intelligence Labs (CRIL). It's not just the highest total of any country tracked in the report — it's more than the next nine most-targeted countries combined.

Canada, in second place worldwide, recorded 179 attacks. Germany logged 155. The United Kingdom, 138. Add up the rest of the global top 10 — France, Italy, Spain, Thailand, India and Brazil — and the total still falls more than 600 attacks short of the U.S. figure alone. Out of 3,836 ransomware attacks CRIL tracked worldwide this half, roughly 45% landed on American soil.

Also read: Fairlife Ransomware Attack Hits Production Systems, U.S. Operations Suspended

A Single Region, an Outsized Share

Widen the lens slightly and the picture holds. North America as a whole recorded 1,981 ransomware attacks in H1 2026 — more than half of every ransomware incident Cyble observed globally — alongside 35 data breach and leak incidents and 9 initial access sale listings. The report describes the region as home to "a mature, persistently active RaaS ecosystem operating at high volume across a wide range of industries and geographies."

Two ransomware-as-a-service operators did much of the damage. Qilin, the single most prolific gang worldwide, claimed 370 of those North American attacks on its own — nearly 19% of the regional total. Akira followed with 268, and INC Ransom added another 164. Together, Qilin and Akira alone accounted for more than half of all recorded ransomware activity across the region, a level of concentration that points to a small number of highly organized affiliate networks doing the bulk of the damage rather than a diffuse swarm of opportunists.

Also read: Qilin Ransomware Group’s TTPs Examined by Researchers

Where the Pressure Lands

Professional Services bore the brunt of North American ransomware activity, with INC Ransom showing a marked preference for law firms and other high-value services with sensitive client data. Construction, Manufacturing and Healthcare followed close behind.

One operator, AiLock, stood out for a coordinated wave of victim disclosures that all landed on the same day — March 3 — a pattern consistent with a mass-exploitation campaign rather than isolated intrusions. LockBit, despite years of law enforcement pressure and takedown attempts, kept up a steady tempo against public-sector and educational targets throughout the period, showcasing how difficult the group has been to fully dismantle.

On the data breach side, Technology and financial services (BFSI) were the most frequently targeted sectors in North America, together accounting for roughly 43% of incidents — a reflection of how much intellectual property and monetizable personal data those industries hold.

Notably, Agriculture & Livestock emerged as a significant target for initial access brokers, accounting for a third of all access listings tied to the region. Cyble flags this as a sign of "growing risk in the food supply chain," an area that has historically drawn less attention from ransomware operators than finance or healthcare.

The initial access market itself was strikingly concentrated: two sellers, tracked under the handles "redpin" and "xpl0itrs," accounted for nearly all listings targeting North American organizations. Threat actors also continued to lean on known and zero-day vulnerabilities in widely deployed enterprise platforms — including products from Ivanti and Palo Alto Networks — as their preferred way into corporate networks.

Hacktivism Blurs into Cybercrime

North America wasn't spared the hacktivism wave sweeping the rest of the world either. Collectives including SOLDADOS DIGITALES – UNIÓN AMERICANA and LYSTIC TEAM #ID drove roughly 56 data leak or dump posts and touched about 360 unique domains across the region, with Government, Technology, financial services and telecommunications entities most frequently in the crosshairs.

Cyble's broader findings suggest many groups marketing themselves as ideologically driven hacktivists are, in practice, running side businesses in stolen data brokerage and DDoS-for-hire services — a blurring of motive that complicates how defenders triage the threat.

The scale of the U.S. numbers doesn't necessarily mean American companies have weaker defenses than their global peers — the concentration also reflects the sheer size and digital density of the U.S. economy, and its outsized share of the high-value targets ransomware affiliates chase. But the data does argue for a shift in posture.

Cyble's broader recommendations — treating data exfiltration, not just encryption, as the primary risk; prioritizing patches for the recurring vendor list; and monitoring initial access markets as a leading indicator rather than an afterthought — apply nowhere more urgently than in a country absorbing this much of the world's ransomware volume on its own.

The post New infosec products of the week: July 24, 2026 appeared first on Help Net Security.

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The post OpenAI’s Rogue Agent Hacks Hugging Face, a Claude Cowork Escape, and Microsoft’s Very Bad Week appeared first on IT Security News.

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The announcement, made by Microsoft AI's Superintelligence team, lands roughly a year after the company committed to building purpose-built models internally, and it arrives with an unusual level of specificity about where those models now run: Bing, PowerPoint, OneDrive, Dynamics 365, Excel, GitHub Copilot, and Azure. The message to enterprise buyers — and, implicitly, to OpenAI — is that Microsoft's homegrown models are no longer research projects. They are production infrastructure serving millions of users.

"Each of these enhancements is a step toward the same goal: Microsoft products, powered by Microsoft models," the company wrote in its announcement blog.

How MAI-Image-2.5-Pro and MAI-Voice-2-Flash stake out opposite ends of the AI cost curve

The two new releases occupy opposite ends of what Microsoft calls the quality-speed-cost curve, and the positioning is deliberate. MAI-Image-2.5-Pro targets the premium tier: hero imagery, detailed editing, and precise in-image text rendering — the last of which has long been a notorious weak spot for image generation models. Microsoft priced the model at $5 per million text input tokens, $8 per million image input tokens, and $106 per million image output tokens. The base MAI-Image-2.5 model recently launched at No. 2 for image editing on Arena, the community leaderboard that has become a de facto scoreboard for generative media.

The creative industry appears to be taking notice. Rob Reilly, global chief creative officer at advertising giant WPP, called the Pro model "a strong leap forward for GenMedia tools" in a statement included in Microsoft's announcement, adding that "Microsoft has firmly established itself among the leaders in generative AI."

MAI-Voice-2-Flash goes the other direction. First previewed at Microsoft's Build conference, Flash runs twice as fast as MAI-Voice-2 and costs 32% less, priced at $15 per million characters. It is designed for the unglamorous but enormous market of high-volume voice — call centers, voice agents, and real-time speech applications where latency and cost-per-call matter more than marginal gains in expressiveness. Together, the two models reflect a strategy of building families of models rather than a single flagship, because, as the company put it, a creative studio chasing maximum fidelity has very different needs from a customer service operation handling millions of calls a day.

Microsoft's production metrics show in-house models cutting GPU costs by up to 89%

The model launches are arguably less newsworthy than the deployment metrics Microsoft attached to them — numbers that read like a systematic case for swapping out third-party frontier models across its product portfolio. 

Bing Image Creator now runs entirely on MAI-Image-2.5, end to end, marking the first time the consumer image tool is fully in-house. In PowerPoint, Microsoft says MAI-Image-2.5 reduces GPU costs by up to 84% compared with GPT-Image-2, OpenAI's image model. In OneDrive, where MAI-Image-2.5 is now the default for key image-editing scenarios, the company reports a 26% increase in save rates, roughly 25% lower P95 latency, and 2.5 times greater efficiency under medium-utilization production workloads.

On the voice side, MAI-Voice-2-Flash now powers Dynamics 365 Contact Center — the platform used by customers including T-Mobile and EasyJet — where Microsoft claims GPU cost reductions of up to 89%. The model is also integrated into Azure Voice Live for developers building speech-to-speech agents.

Perhaps the most consequential deployment sits in healthcare. Microsoft's Dragon Copilot, used by 170,000 medical providers and responsible for processing 28 million patient encounters last quarter, now runs on MAI-Transcribe-1.5 for its multilingual workflow across 58 languages. Microsoft says internal evaluations show a 50% relative reduction in both transcription and language-identification error rates across most languages — a meaningful claim in a domain where transcription errors can propagate directly into clinical notes.

Inside the 'hill-climbing' strategy that lets small models beat GPT-5.6 in Excel

In a companion post published the same day, Microsoft detailed the methodology behind these results — what it calls its "hill-climbing machine," an integrated flywheel of data, models, and the product "harness" that surrounds them.

The clearest example is MAI-Code-1-Flash, the lightweight coding model launched in GitHub Copilot in June. Microsoft says the model achieves an approximately 10% higher code accept rate than GPT-5.4 Mini and Claude Haiku 4.5 in VS Code, while using 10% fewer median tokens. Developer retention tells a similar story: users were 6% more likely to return across multiple days than with GPT-5.4 Mini, and 11% more likely than with Claude Haiku 4.5.

Then Microsoft did something more interesting. It took the MAI-Code-1-Flash checkpoint and further trained it inside an Excel reinforcement learning environment, teaching a coding model the tools and workflows of spreadsheet knowledge work. The result, according to production user feedback, is a model on par with GPT-5.6 for the most common Excel tasks — while being small enough to run on Nvidia's older H100 and even A100 GPUs rather than requiring the latest-generation accelerators.

That hardware detail deserves emphasis. Every major AI company is fighting for allocation of cutting-edge chips, and a model that delivers frontier-adjacent quality on two-generation-old silicon fundamentally changes the deployment economics. It also frees the newest hardware — including Microsoft's now-operational GB200 cluster — for training rather than serving.

Satya Nadella's 'frontier diffusion' manifesto redraws the OpenAI relationship

Microsoft CEO Satya Nadella framed the announcements in a lengthy post on X titled "Frontier Diffusion & Control," which functions as something close to a strategic manifesto. "We can now take saturated frontier capabilities and deliver them at scale and at lower cost through models optimized for high-usage products, while continuing to use frontier models for frontier needs," Nadella wrote, adding that Microsoft is "beginning to route traffic across our first-party surfaces to MAI whenever our models match or outperform frontier alternatives."

Translated from executive prose: capabilities that were state-of-the-art a year ago are now table stakes, and Microsoft believes it can replicate them cheaply for the specific, repetitive tasks that dominate real product usage. Why pay frontier prices for a frontier model when a user just wants to reformat a spreadsheet column?

Nadella was careful to note that "frontier models from OpenAI and Anthropic are part of the orchestration system alongside MAI" — but he also articulated a pointed principle of model independence, arguing that a company's evaluations "should continue to hill climb even when any given model has been removed." 

“Keeping the harness, memory, context, and skills outside the model, he argued, is what gives Microsoft control. The subtext is hard to miss. Reuters reported in April that Microsoft’s exclusive license to OpenAI’s technology had been revised into a non-exclusive arrangement, and The Information reported last September that Microsoft had begun incorporating Anthropic models into some products. Wednesday’s announcement completes the triangle: Microsoft as orchestrator, with its partners’ frontier models as interchangeable components and its own models absorbing an ever-larger share of routine traffic.”

Developers cheer cheaper task-specific models while skeptics question Microsoft's track record

The response online captured both the appeal and the skepticism surrounding the strategy. "I love when people use small models for niche tasks," wrote one X user, @mavihsk, responding to Nadella's post. "Why do I have to use the all-knowing model just to change my field in Excel?" Another user, @nabu_lines, distilled the pitch neatly: "cost and performance both improve when you stop overusing the biggest model."

Others were less charitable about Microsoft's execution track record. "Microsoft is the worst when it comes to listening to user feedback," wrote designer @designedbyabin, arguing the company "will lose the AI race because they repeatedly failed to understand user needs." And one user, @tokenoverflow, offered a drier critique of the model-independence pitch: "i want it keep hill climbing after removing microsoft."

The skeptics raise a fair point. Microsoft's self-reported metrics — accept rates, save rates, GPU savings — come from its own internal evaluations, not independent benchmarks, and the company chooses which comparisons to publish.

But the strategy's logic does not depend on any single number. Nadella's framing that software now has "real marginal cost for the first time" explains why Microsoft is obsessive about tokens, GPUs, and serving costs: when AI features run on every keystroke across a billion-user product portfolio, an 84% GPU cost reduction is not an optimization. It is the difference between a viable business and a money pit.

Why Microsoft is turning its internal AI playbook into an Azure product

The final piece of the strategy is that Microsoft is selling the playbook, not just the models. Nadella explicitly positioned the hill-climbing approach as "a template for every other AI native, SaaS, or Enterprise company," and Microsoft is packaging the toolchain through Foundry and what it calls Frontier Tuning — letting enterprises train specialized models against their own proprietary evaluations and reinforcement learning environments. That turns Microsoft's internal cost-cutting exercise into an Azure product, and it gives enterprise customers a reason to run their AI workloads on Microsoft's cloud even if the models themselves come from elsewhere.

The company's emphasis on models trained "on clean, traceable, enterprise-grade data, without distillation from third-party models" serves the same commercial end. In an industry facing mounting scrutiny over training data provenance, Microsoft is betting that enterprise buyers — and courts — will care where model capabilities come from. Microsoft says it is now extending the hill-climbing approach to Copilot Chat, Outlook, and PowerPoint, and both new models are available in public preview through Microsoft Foundry and the MAI Playground. "None of this is an endpoint," the company wrote. "We're just getting started."

Seven years ago, Microsoft bet more than $13 billion that OpenAI would build the future of AI. Wednesday's announcement suggests the company has since learned a cheaper lesson: the future of AI may belong to whoever builds the frontier, but the profits belong to whoever makes it ordinary.

Fixes Dependabot alerts #24 and #25.

Co-authored-by: Ben Hillis benhillis@microsoft.com
Co-authored-by: Copilot 223556219+Copilot@users.noreply.github.com
(cherry picked from commit 09a8afe)

• Update MSRDC to 1.2.7214 to fix CVE-2026-32157 (#40882)

Bumps Microsoft.RemoteDesktop.Client.MSRDC.SessionHost from 1.2.6676 to
1.2.7214. This closes CVE-2026-32157 (a use-after-free RCE in the Remote
Desktop client, first fixed in 1.2.7099) along with several additional
CVEs shipped in 1.2.7214.

Fixes #40868

Co-authored-by: Ben Hillis benhill@ntdev.microsoft.com
Co-authored-by: Copilot 223556219+Copilot@users.noreply.github.com
(cherry picked from commit 2a7269e)

• Update .NET runtime to 10.0.9 to fix CVE-2026-45491 (#40883)

Bumps Microsoft.NETCore.App.Runtime.win-x64 and win-arm64 from 10.0.8 to
10.0.9 to address CVE-2026-45491 (.NET tampering vulnerability), reported
by Dependabot. Both runtime packages are bumped together to keep the
.NET runtime version in sync across architectures.

Co-authored-by: Ben Hillis benhill@ntdev.microsoft.com
Co-authored-by: Copilot 223556219+Copilot@users.noreply.github.com
(cherry picked from commit 73a6afb)

Co-authored-by: Ben Hillis benhillis@microsoft.com
Co-authored-by: Copilot 223556219+Copilot@users.noreply.github.com
Co-authored-by: Ben Hillis benhill@ntdev.microsoft.com

The Freiburg, Germany-based AI lab says FLUX 3 is jointly trained across those modalities rather than assembling separate image, video and audio models behind a common interface.

That distinction is central to the company's pitch: BFL wants enterprises to think about creative generation, simulation, computer use and robotics as connected applications of a single capability it calls visual intelligence — models, in the company's words, "that can perceive, predict, and act across physical and digital environments." This release marks BFL's first public video generation model.

FLUX 3 will be offered through four product lines: FLUX 3 Video, FLUX 3 Image, FLUX 3 Action and the upcoming, open source FLUX 3 Dev. FLUX 3 Video, with optional native audio generation, and FLUX 3 Action are entering a gated "Early Access" program now, to which anyone can apply, but which BFL must approve.

There is presently no public access through BFL's application programming interface (API) or those of partners yet, but the company says FLUX 3 Image will roll out in the coming weeks, followed by general availability. The limited initial availability rollout echoes the release strategies of new models from other frontier labs in the U.S. lately, including Anthropic and OpenAI, though those were ostensibly for security concerns and due to government request.

What the company has not announced is pricing, production service-level commitments, evaluation methodology, sample sizes, rater counts or any image-model benchmarks at all. Enterprise buyers therefore cannot yet calculate total cost of ownership or independently reproduce the video comparisons.

Another big notable omission: FLUX 3 is not launching with downloadable weights at this time, nor an open source license. BFL says faster and open-weight versions will arrive later this year, and its technical blog names FLUX 3 Dev as "open-weight access to a multimodal backbone, for content creation (video, audio and image) and action prediction" — a considerably broader commitment than any previous FLUX Dev release, all of which covered images only.

But it arrives last in the sequence. Developers accustomed to receiving a locally deployable FLUX variant alongside — or soon after — a major model announcement will have to wait. That delay does not negate the company's commitment, but it is disappointing given the role open weights have played in FLUX's adoption thus far.

Flux 3 is rated higher than the competition, but missing pricing and benchmarking details may prevent rapid enterprise adoption

BFL has published several benchmark comparisons, but they're qualified as preliminary — with full benchmark results and methodology to be published later during broader general availability.

In early head-to-head preference testing on 10-second, 720p text-to-video clips with audio, the company says FLUX 3 was preferred over Luma Ray 3.2 in 93% of comparisons, Runway Gen-4.5 in 77%, Grok Imagine Video in 69%, Kling v3 Pro in 60%, Happy Horse v1 in 59%, Happy Horse 1.1 in 57%, and both Seedance 2.0 and Google's Gemini Omni Flash in 52%.

One caveat travels with every one of those figures, and it comes from BFL itself. The chart carrying the results is labeled a "preliminary evaluation of an early FLUX 3 candidate" — meaning the numbers describe a pre-release checkpoint rather than the model now entering early access. That cuts both ways: the shipping model may perform better, but nothing published today measures what customers will actually call.

Luma Ray 3.2 and Runway Gen-4.5, where FLUX 3 posted 93% and 77%, are the softest comparisons on the list — established products, but not the models currently setting the pace in independent video rankings. Those are real wins, and they are the ones least likely to change an enterprise shortlist.

Seedance 2.0, at 52%, is a statistical coin flip against a model most Western enterprises cannot currently procure. ByteDance indefinitely postponed Seedance 2.0's international rollout after Netflix, Warner Bros., Disney, Paramount and Sony sent legal threats over alleged systematic copyright infringement, and that suspension remains in place. Tying a frozen product is neither a strong claim nor a damaging one.

Gemini Omni Flash, also at 52%, matters much more. Omni is the closest large-platform analogue to what FLUX 3 is attempting — multimodal input, video and audio-aware creation, conversational editing — and by BFL's own measurement, the two are indistinguishable on 10-second text-to-video quality.

Google's advantage in that matchup is that Omni is generally available via Google's Gemini API for $0.10 per second of generated 720p video, or a 10-second clip for around.

One regional wrinkle matters for a German company's home market. Editing uploaded video is unavailable to Omni Flash users in the European Economic Area, Switzerland and the United Kingdom, though editing video the model itself generated is permitted. A European enterprise that wants to run its existing footage through a generative editing pass cannot currently do so on Omni Flash.

Here's a rough guide for enterprises considering which video models to rely upon:

One architecture for media generation and physical action

FLUX 3 builds on Self-Flow, BFL's method for aligning multimodal understanding and generation within one architecture, publicized back in March 2026.

The company says it significantly scaled up compute and data to train across video, images and audio simultaneously, and that testing showed video generation and action prediction do not require separate foundations — the same architecture could be extended to action prediction without sacrificing what it learned from video.

"We place vision at the center of our approach because it is the most signal-rich medium of the physical world. Images convey structure, images and video teach spatial relationships, video teaches dynamics, and actions reveal causal relationships. But vision alone is not the complete picture," said Robin Rombach, co-founder and CEO of BFL, in a pre-release statement provided to VentureBeat. "True intelligence means perceiving the world: predicting how it will change, taking action, and learning from the results. Joint training within one unified architecture is what will get us there, because each training modality strengthens the others. Audio conveys timing, prosody, and physical events that elude vision. Language conveys goals, abstractions, and instructions that pixels cannot easily express."

He put the case more bluntly elsewhere in the announcement: "You can't cheat reality. A model that only learns images can only generate images. But the world is not made of still frames. It moves, sounds, changes, and responds."

BFL says FLUX 3 targets creative tooling, media, design, e-commerce and physical AI, supporting video generation with synchronized audio, precise image editing, product and material consistency across motion, multilingual generation and robotic action prediction. It is already being tested by Canva, Burda, Magnific (formerly Freepik), Krea and Picsart.

For creative software companies, the appeal is consolidation. A single foundation could potentially support storyboarding, image editing, product rendering, video variation and localization without repeatedly translating assets and instructions between disconnected models.

For robotics teams, the potential value is data efficiency. Models that already encode motion, object behavior and physical change may need less task-specific robot training than systems starting from raw demonstrations.

What FLUX 3 Video can actually do

The video tier is the most concretely specified part of the launch, and it settles a question that had been circulating as rumor: FLUX 3 generates clips of up to 20 seconds with audio in a single generation.

Every video output comes with native audio. For comparison, HappyHorse 1.0 tops out at 15 seconds of 1080p with synchronized audio — though BFL has not stated what resolution its 20-second clips run at, and its published evaluations were conducted at 720p. Still, a 20-second long clip from a single prompt is among the longest yet achieved, matching OpenAI's discontinued Sora model.

The capability list BFL published covers:

• Text-to-video generation.

• Image-to-video generation, either animating from a starting frame or using images as visual references.

• Video-to-video generation from a reference clip, carrying elements such as a specific character into a new scene or context.

• Generative video-audio continuation from existing video and audio input.

• Keyframe-to-video generation for controlled transitions between defined moments.

• Multilingual dialogue.

• A broad range of visual styles and aspect ratios, from candid camcorder footage to animation and cinematics.

• Typography generation and animated design.

• Agentic chaining of individual clips into longer, multi-shot sequences.

That last item is the one enterprise video teams should look at hardest. BFL claims the capabilities combine to produce sequences lasting several minutes, with visual references keeping characters consistent across scenes. If that holds up under production conditions, it addresses the constraint that has kept generative video out of most commercial pipelines: not clip quality, but continuity across shots.

It is also the capability where competition is most direct. HappyHorse 1.1's headline upgrade is R2V, or Reference-to-Video, which accepts multiple character reference images to hold identity stable across generated footage — the same problem, approached at the input layer rather than through agentic clip chaining. Alibaba also claims zero-drift lip sync and has specifically targeted the artifacts that mark commercial AI video as synthetic, including facial oiliness and over-sharpening. Character consistency is where this category is being contested, and both companies know it.

BFL says FLUX 3 Video is already particularly strong at human facial expressions, associating sounds with physical events, and multilingual output. On the image side, the company says preliminary evaluations conducted during midtraining show significant improvement over earlier FLUX versions in complex prompt handling and text generation, including high-accuracy text in multiple languages. It published no image benchmarks or win rates.

FLUX-mimic tests whether video models can become robot models

BFL is applying its unified-architecture thesis through FLUX-mimic, a video-action model built on FLUX 3 and developed with Swiss firm Mimic Robotics, one of the first partners to receive early access.

The technical blog describes two distinct routes to action prediction: integrating native action prediction directly into FLUX 3, scaling up the initial Self-Flow work; and using the pretrained video backbone as a dynamics-aware foundation from which specialized action models can be finetuned with limited task-specific data. FLUX-mimic is the second route — the FLUX 3 backbone combined with mimic's robot-learning and production-deployment expertise in dexterous manipulation.

FLUX-mimic is designed for general-purpose robotic manipulation: helping robots understand a visual scene, predict the consequences of an action, and adapt to new tasks with far less task-specific data.

BFL and Mimic Robotics say that depending on task difficulty, the model can be finetuned for a specific manipulation task with as little as 30 minutes of robot data, where prior approaches have required 30 or more hours.

"The hardest part of robotics is data," said Elvis Nava, CTO of Mimic Robotics, in a statement provided to VentureBeat. "Every new task normally means hours of a robot repeating itself. Because FLUX-mimic is built on top of frontier video models that already understand how the physical world behaves, it picks up a new task in minutes, not days. This way, we can leapfrog the current state of the art in robot learning."

BFL argues that a model trained only on images cannot understand a world that "moves, sounds, changes, and responds," and that physical understanding is what produces convincing generated footage. Google makes a nearly identical claim for Gemini Omni.

Its developer documentation cites "world knowledge" that combines "an understanding of physics" with Gemini's grasp of history, science and cultural context. Its marketing is blunter still: "Most AI models just predict the next pixel to build a narrative or an image. Gemini Omni is different," the company posted in June, crediting the model with "an intuitive understanding of forces like gravity, kinetic energy, and fluid dynamics for more realistic movements that follow real-world logic."

The practical consequence for enterprise buyers is that world-model language is not a differentiator. Two of the three leading video systems now market physical understanding as their central advantage, and neither has published a benchmark that measures it.

There is no standard test for whether generated water behaves like water, whether a dropped object falls at a plausible rate, or whether a sound arrives when the impact does. Human preference ratings capture some of it indirectly. Nothing else on offer captures it at all.

Open weights helped make FLUX an industry standard

BFL officially launched in summer 2024 and gained a name for itself in the AI industry in the intervening two years for its commitment to open sourcing high-quality AI image models beloved by developers, creatives, and enterprises.

The company's founders, including Rombach, Andreas Blattmann and Patrick Esser, previously helped create VQGAN, latent diffusion and Stable Diffusion, the latter the open source technology that kicked off broad AI generation capabilities for the masses and currently used by many AI image generators and companies.

That reach translated into commercial distribution. FLUX models now power generative features inside Adobe Photoshop, Picsart and Nous Research's Hermes Agent, among other platforms, and the company cites film director Martin Scorsese among professional users.

Wired magazine described Black Forest Labs as a relatively small company that nevertheless became a leading competitor to Silicon Valley's largest AI labs, with FLUX models ranking near the top of image benchmarks and becoming some of the most downloaded text-to-image models on AI code sharing community Hugging Face. The company says it now runs a 100-person team across Freiburg and San Francisco.

FLUX.1 Dev, FLUX.1 Kontext Dev, FLUX.1 Fill Dev and related control models, released shortly after the firm's launch, gave researchers and creative-tool developers access to downloadable checkpoints, local inference and integrations with frameworks including Hugging Face Diffusers and ComfyUI. FLUX.1 Kontext Dev, for example, was released as an open-weight model for research and noncommercial use, with generated outputs permitted for commercial purposes under the applicable license.

The company continued that pattern with FLUX.2 Dev in late 2025, a 32-billion-parameter open-weight model combining generation and multi-reference editing. Black Forest Labs called it the strongest open-weight image generation and editing model available at launch and released weights, reference inference code and optimized implementations for consumer Nvidia GPUs.

FLUX 3 Dev raises the stakes on that evaluation. Previous Dev releases were image models. This one is described as a multimodal backbone spanning video, audio, image and action prediction — meaning a single license will govern whether a company can locally deploy a model that touches both content production and physical machinery. BFL hasn't yet shared information about its license, the parameter count, quantizations or hardware requirements.

The company frames open weights as an enterprise feature rather than a community gesture, arguing they enable secure, low-latency local deployment for applications like robotic control systems and let teams adapt FLUX 3 to their own data, products and workflows.

The financial backing behind FLUX 3 is worth noting alongside the technical claims. Black Forest Labs is valued at $3.25 billion and has raised more than $450 million from investors including a16z, AMP, Salesforce Ventures, Nvidia, General Catalyst, Adobe Ventures, Figma Ventures, Canva and Deutsche Telekom's T.Capital.

The post You Didn’t Get the AI Model You Paid For appeared first on MarkTechPost.

“Our founder and CTO has actually been really pushing to enable our agents in YOLO mode,” Rishi told the audience. That admission comes from a publicly traded data security firm whose business is backing up what he called the most important data in the world.

YOLO mode strips the permission prompt out of agent workflows and lets the agent act on its own. In Rubrik's version, a second AI judges every action in real time against policy in place of a human clicking approve. Rubrik is running the experiment on itself first. Rishi treats autonomy as a settled capability question and an open judgment question. "If you ask the agent to act autonomously, it will," he said. "It's a question that you have internally. Should it?"

Rubrik earned that question the hard way. When Claude Code and Cowork pilots rolled out, the company required every command to run in ask mode so the employee issuing it carried the liability, and the developer pushback filled a single Slack thread 120 messages deep.

"The developers basically are pushing back, and they're like, this is like the iTunes service agreement. I'm just hitting check, check, check, check, check, check, check," Rishi said. "There's no way that I can actually read through this. And it becomes security theater." Roughly 80% of respondents are in the same bind, Rishi said, citing Rubrik Zero Labs research that found monitoring and approving agent actions takes more time than the agents save. The State of the Agent, the April report behind that figure, surveyed more than 1,600 IT and security leaders.

SAGE is the reason Rubrik trusts the bet. Short for Semantic AI Governance Engine, SAGE is the arbitration layer inside Rubrik Agent Cloud that watches every action an agent takes and reads the semantic intent behind it, then rules the action in or out against policies written in natural language. "We took what people said was human in the loop, a good idea, and we replaced it with AI in the loop," Rishi said, describing the pitch to security chiefs he characterized as skittish about non-deterministic systems.

Security approval, not cost, blocks AI ROI

Rishi’s path to Rubrik ran through Predibase, the generative AI infrastructure startup he co-founded and ran as CEO until Rubrik agreed to acquire it in June 2025. Before that, he led ML product at Google on the team that became Vertex AI, served as Kaggle's first product manager as it grew from about one million to ten million users, and holds bachelor's and master's degrees in computer science from Harvard.

Over roughly his first three and a half months at Rubrik, Rishi set up 200 customer conversations with IT and security leaders across a customer base that looks like the Global 2000, asking open-ended questions about cost, latency, performance, and orchestration. "Pretty consistently, what I heard through all of those conversations was that all of those are pretty secondary," he said. "The main challenge is actually, how do I get this approved from a security and risk standpoint? I'm concerned about all the different things that could go wrong. Actually, I felt like that was one of the biggest things constraining ROI."

VentureBeat Pulse research presented on the Transform stage earlier in the day confirms the gap Rishi kept hearing. Two-thirds of enterprises, 66%, already allow or are actively building toward production deployment with zero human review, yet only 5% fully trust the automated evaluations that would make that decision.

One AI reading what the rulebook can't

Rubrik's own policies exposed why written rules fail as enforcement. One internal rule states that agents should respect Rubrik's customer data use policy, which sounds enforceable until someone tries. "Rubrik's customer data use policy is like a three-page document of legal text," Rishi said. "I have no idea how to write that in there as a rule." Asked on stage how a team of AI infrastructure people took on a problem that security engineers own, Rishi answered, "with a lot of naivety and innocence, honestly." His team bet that models good at understanding language could police other models, and SAGE became the answer.

The case for putting a model in the judgment seat comes down to precision. A rule like "agents should not be able to edit revenue fields in Salesforce" fails in conventional tooling because Salesforce does not delineate which fields count as revenue, Rishi explained, so administrators fall back on approving every Salesforce action by hand. SAGE reads the intent instead and acts as a judge, carrying organizational context, which can tell a benign lookup from the edit the policy prohibits.

Keeping the judge small is what makes the economics work. SAGE runs on a small language model that Rishi said operates at an order of magnitude lower cost and latency than a frontier LLM. "If I told you, don't worry, you're gonna be secure and governed, but I'm gonna double your cost and latency, you would tell me to get out of the room," Rishi said.

When Rishi asked who in the audience had worried about token consumption over the past year, half the hands went up. "And I guess the other half is probably just too lazy to raise their hand," he said.

SAGE is an aggregation of judges based on parameter-efficient fine-tuning that Rubrik uses to take on task-specific variants of a base model with shared organizational context. One judge watches for tool-use hallucinations while another suppresses PII before it can leave, each running as its own enforceable policy. Security and GRC teams have started writing financial rules into the same layer, including one internal policy barring AI spend on personal projects.

The lethal trifecta

Asked which attacks worry him most, Rishi pointed at the lethal trifecta, the term security researcher Simon Willison coined in June 2025 for an agent that holds private data while taking in content nobody vetted, with a channel to send what it finds to the outside world. The danger, according to Rishi, is what happens when individually legitimate permissions stack. An agent granted Salesforce access and email access on an employee's credentials has done nothing wrong yet, with yet being the operative word. "A very simple example is that an agent can start pulling data from Salesforce and then decide to accidentally leak and exfiltrate that out via an email," he told the audience. A financial services company he met the morning of the session made the point for him, telling Rishi that none of the individual permissions are bad on their own and the agent needs every one of them to do its job. "It should have permission to each of those systems, but it's the combination that ends up becoming really destructive," Rishi said.

Traditional identity and access management never priced in that combination because it relied on the judgment of the employee holding the credentials, Rishi argued, and agents supply none. "I can tell you the number of times Claude Code has tried to leak some of our sensitive source code to a public GitHub repository is incredibly high," he said. Cutting agents off from public resources entirely would defeat their purpose, which returns the problem to adjudicating intent in context rather than revoking access.

A separate VentureBeat June Pulse survey of 107 qualified enterprise respondents maps the blast radius of exactly this pattern. On the Transform stage that morning, VentureBeat research reported that 69% of companies run credential sharing somewhere in their agent fleet. Companies with shared credentials anywhere got hit more often, reporting a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent carries its own scoped identity.

The attacks no single turn reveals

Rubrik Agent Cloud reached general availability in February, though not everything Rishi described ships in it yet. Backtesting is just starting to roll out. The feature replays an organization's historical agent actions and tool calls against a new policy, showing where the policy would have stepped in and where an action would have sailed through uncaught, with policy edits applied in real time. Rishi called that archive one of the most valuable data troves an enterprise holds.

Real-time detection and blocking turn out to be the entry point rather than the whole product. Some attacks never trip a single-action rule. "No individual turn of the conversation was problematic, but if you took the session as a full trace, that ended up being problematic," Rishi said. Agent Cloud runs batch analysis across entire session traces every hour or every day and surfaces what Rubrik calls insights, the problems no individual guardrail caught. The same Zero Labs report found that 88% say they lack the ability to roll back agent actions without system disruption, a recovery gap that sits squarely in Rubrik's original line of business.

A skeptical CISO will ask the question the fireside did not answer. SAGE is a non-deterministic model policing other non-deterministic models, and Rishi offered no false positive or false negative rate for the judge itself. The closest thing the architecture gives to an answer is auditability, since backtesting and the batch insights both leave a human-reviewable trail of each call SAGE made and whatever got past it. Who watches the watcher, for now, is a trail of receipts rather than a benchmark. Until that benchmark exists, AI in the loop stays an operational wager rather than a quantified control.

Three questions fall out of the session for security teams. How many of the guardrails now in production depend on a human clicking approve, and what happens to that workload as agent count grows? Does anything in the stack enforce semantic intent, or is it all allow and deny lists? And can the team backtest agent behavior against a new policy, then unwind a multi-turn session without taking systems down?

Rishi's timing has a market behind it. In the same VentureBeat research, 82% of enterprises still name their primary AI provider's built-in guardrails and cloud controls as their main agent security layer, and 59% plan to adopt, add, or replace agent security tooling within the next 12 months. Only 12% include an agent-identity product in what they are considering, even with credential sharing still the norm. Every CISO at that Anthropic roundtable had a policy document and no enforcement mechanism, and Rubrik built a product for the space between the two. YOLO mode is the bet that an AI watching other AIs can finally make the policies real.

This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.

The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run.

That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends.

By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%).

At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample.

Finding 1: Orchestration runs on model-provider platforms

Anthropic’s Claude leads; open frameworks are marginal

We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.

A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share.

The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all.

Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love.

Finding 2: Model gravity drives platform selection

The base model, not the tooling, decides the platform

We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind.

Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed.

Finding 3: The job is reliable multi-step execution

Enterprises just orchestration by whether it completes the work

We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail.

Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all.

The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition.

Finding 4: Consolidate, productionize, and build in-house

Three strategic moves are nearly tied for the year ahead

We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split.

The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit.

Finding 5: Nearly seven in 10 plan to switch — and the biggest group of movers has no shortlist 

The strategic change enterprises anticipate (previous finding) comes with vendor motion attached. Asked whether they plan to adopt a new, additional, or replacement agent orchestration platform in the next twelve months, more respondents are moving here than in any other layer we track.

Asked which platforms they are considering, the most common answer among those in motion is none yet: 29% of all respondents are evaluating without a shortlist, the largest single response after "not considering a change." Among named candidates, OpenAI leads at 16%, followed by LangChain/LangGraph at 12% and Anthropic at 7% — and notably, the independent frameworks draw roughly double their current usage footprint in forward consideration, the same pattern our security tracker found for specialist vendors. Read with this report's concentration and lock-in findings, the picture completes itself: the major model-platform providers hold roughly four-fifths of today's primary usage, vendor lock-in has become the leading fear, 96% anticipate a strategic change — and now the purchase intent to act on all of it, with the largest bloc of buyers still undecided. The most concentrated layer of the agentic stack is also, as of June, the least settled.

Finding 6: Investment flows to workflow tooling

Tooling and permissions lead the spend; monitoring trails

We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.

Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.

Finding 7: The control plane will be hybrid — and lock-in is why

Enterprises expect to split control between providers and their own layer

We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason.

Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear.

The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control.

Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.

Finding 8: The chatbot trap — most “agents” aren’t agents yet

Enterprises admit most deployments are still chatbot wrappers

We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave.

This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition.

Finding 9: Fiscal control is still reactive

Only a minority can stop a runaway agent before the bill arrives

Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception.

More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built.

It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets.

The bottom line: The layer is real; most of the agents aren't yet

Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing — for now — on model-provider platforms, which collectively hold roughly four-fifths of primary usage, chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most. But the standardization is provisional: 68% plan to adopt a new, additional, or replacement orchestration platform within twelve months — the highest switching intent of any layer we track — and the largest group of those movers has not yet shortlisted a candidate. Today's concentration describes where enterprises are, and visibly does not describe where they intend to stay.

But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed "agents" are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The questions for subsequent waves are whether the deployed reality closes the gap on the ambition — and, with nearly seven in ten buyers in motion and most of them undecided, which platforms the settled stack finally lands on.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.

This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop.

The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent.

What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same time, the evaluation stack that would have to earn that trust is fragmented and immature: the most common primary tools are the model providers’ native evals, tied with having no dedicated tooling at all (17% each); and only about a quarter of enterprises run real-time quality checks on live production traffic. The autonomy is arriving faster than the assurance.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey — the Agentic Reliability & Evals tracker — focused on how technical leaders evaluate agent performance and reliability. Responses are filtered to organizations with 100 or more employees (n=157), drawn from a single survey in June 2026; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Where questions were multiple-select, those shares can sum to more than 100%.

By role the sample is senior and buyer-credible: 38% are final decision-makers for AI purchases and another 34% recommenders or influencers. Product and program managers (15%), consultants and advisors (10%), directors of engineering/IT (8%), and CIOs/CTOs/CISOs (8%) lead the named titles, alongside a large “Other” function (37%). By organization size the sample is mid-market-weighted: 100–499 (37%) and 500–2,499 (27%) employees lead, with 2,500–9,999 (20%), 10,000–49,999 (10%), and 50,000+ (6%) above them. Technology/Software is the largest industry at 23%, followed by Retail/Consumer (15%), Healthcare/Life Sciences (12%), and Manufacturing (10%).

At 157 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent evaluation practices rather than from the largest operators.

Note: This survey was rebuilt for the June wave from the earlier “LLM observability and evaluations” survey; because the questions and sample differ, no comparisons are made to the April–May data.

Finding 1: A passing eval is not a working agent

Half have shipped an agent that passed evals, then failed a customer

We asked whether, in the past 12 months, organizations had deployed an agent or LLM feature that passed their internal evaluations but then caused a customer-facing failure. Half of those that run evaluations had.

This is the report’s defining number. Half of organizations (50%) have shipped an AI feature that cleared their internal evaluations and then failed in front of a customer — an incorrect output, a broken workflow, or a quality incident — and a quarter have seen it happen more than once. Only 36% report no such failure, and the remainder either run no pre-deployment evaluations (8%) or don’t track the root cause closely enough to know (6%). The failure is precise and expensive: the evaluation said the agent was ready, and it was not. Everything that follows — how enterprises trust their evals, what they monitor, and how much autonomy they grant — is shaped by this experience.

Finding 2: Almost no one fully trusts automated evaluation

The top complaint: Evals don't match real-world outcomes

We asked which limitation most reduces trust in automated agent evaluations today. Only a sliver of enterprises had no complaint at all.

Trust in automated evaluation is scarce, and specific. Only 5% of organizations say they fully trust automated evaluation as it stands — meaning 95% name a limitation that holds them back. The most common, at 29%, is the one that most directly explains Finding 1: evaluations align poorly with real-world outcomes, passing agents that later fail. Bias or inconsistency (21%) and a lack of explainability (18%) follow — enterprises cannot always tell why an evaluation reached its verdict — and 17% cite data-leakage or privacy concerns in the evaluation process itself. The tests meant to certify agents are not yet trusted to certify them, which is precisely why the autonomy trajectory in Finding 3 is so striking.

Finding 3: The autonomy ceiling is rising anyway

Two-thirds already allow, or are building toward, zero-human deployment

We asked whether organizations would let an autonomous agent deploy a code or system change to production on automated evaluation results alone, with no human-in-the-loop validation. The trajectory runs straight through the trust gap.

Here is the paradox at the heart of the report. Even though almost no one fully trusts automated evaluation (Finding 2), two-thirds of organizations (66%) either already allow zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to permit it within a year (33%). Only 22% rule it out for the foreseeable future. The direction is unambiguous: enterprises are moving to let evaluations gate production autonomously — removing the human check — at the same moment they say those evaluations don’t reliably match reality. The autonomy ceiling is rising faster than the assurance beneath it, which is the mechanism by which the false-confidence failures of Finding 1 will scale rather than shrink.

Notably, the autonomy bet is not just a small company phenomenon. Splitting the sample by company size, larger enterprises are slightly further down the path toward zero human review than smaller companies (70% versus 64%) and slightly more likely to have shipped an evaluation-passing agent that then failed a customer (54% versus 48%). The assumption that large, regulated organizations are holding the human in the loop longest is, in this sample, backwards.  To be sure, these are directional figures, since the survey was not a huge sample — 57 respondents from companies with 2,500+ employees and 100 from companies smaller than that. 

Finding 4: The evaluation stack is fragmented and provider-led

Provider-native evals lead — tied with no dedicated tool at all

We asked which agent reliability or evaluation platform enterprises primarily use today. The market has no clear leader — and a large share has nothing dedicated.

The evaluation layer is early and unconsolidated. Provider-native tooling leads — OpenAI’s native evals and traces (17%) and Anthropic’s Claude Console evals (13%) together outweigh any independent platform — but it is tied at the top by a striking answer: 17% of enterprises use no dedicated agent-evaluation tooling at all, a notable gap for organizations shipping agents to customers. The specialist evaluation vendors — DeepEval (12%), Braintrust (8%), LangSmith, Weave, Promptfoo, Langfuse, Arize — are scattered across single to low double digits, and 11% have built their own. No independent platform has yet become the category standard, which leaves most enterprises evaluating agents with provider-native tools, home-grown scripts, or nothing.

Finding 5: Production monitoring rarely watches output quality

Only a quarter run real-time quality checks on live traffic

Production monitoring for an AI agent can watch two very different things. It can watch whether the system is functioning — is the agent up and responding, did each request complete, how fast, at what cost, with any errors. Or it can watch whether the agent's output is correct — automated checks that evaluate the content of each answer as it goes out: did the agent give the right answer, take the right action, stay within policy. The distinction matters because a confidently wrong answer is invisible to the first kind of monitoring: the request completes, the response is fast, no error is thrown, and every functioning-metric reads healthy. We asked organizations which kind their live production monitoring is built for today.

Grouped by what is actually being watched, the split is stark: 51% of organizations monitor only whether the agent is functioning, while 23% monitor whether its answers are right. Counting the ad-hoc reviewers and the don't-knows, roughly three-quarters of organizations run no automated, real-time evaluation of output correctness in production — they can see that the system is up and what it costs, and they are taking the correctness of its answers on faith. That blind spot is the runtime counterpart to the pre-deployment gap in Finding 1: the same organizations engineering the human out of the deployment decision mostly cannot see, in real time, when the deployed agent starts getting things wrong.

Finding 6: Bought on cost, measured on consistency

Price and integration drive selection; evaluation consistency is the goal

We asked what most influenced enterprises’ choice of an evaluation vendor, and what they treat as their primary measure of success. Both answers are pragmatic.

Enterprises buy evaluation tooling on economics and trust it on repeatability. Cost of evaluations (28%) narrowly leads selection, just ahead of ease of integration (27%) and evaluation accuracy (24%) — breadth of observability (13%) and vendor roadmap (4%) matter far less. On what success looks like, more than a third (36%) name evaluation consistency — getting the same verdict on the same behavior every time — well ahead of speed of experimentation (19%), reduction in failures (18%), production visibility (13%), and compliance (11%). The emphasis on consistency is telling: before enterprises can trust an evaluation’s verdict, they need it to be stable — the very property whose absence (bias and inconsistency) ranked among the top trust limitations in Finding 2. Satisfaction with current tooling is only moderate, averaging 3.8 on a five-point scale across overall satisfaction, ease of implementation, and value for money.

Finding 7: The next dollar goes to humans and observability

Investment is flowing to oversight, not just automation

We asked which reliability and evaluation investment will grow most over the next year. The money is going toward watching agents more closely — including with people.

The second-largest planned investment — behind only production observability — is human review workflows, at 26%. Read against Finding 1, that is the report's quietest contradiction: at the same moment two-thirds of enterprises are engineering the human out of the deployment decision, more of them plan to grow spending on human reviewers (26%) than on the automated evaluation pipelines (16%) that would replace them. The zero-human trajectory and the human-review budget are rising in the same companies at the same time. Indeed, only 8% report that their budget is not increasing.

Taken together, enterprises are hedging: building toward autonomy while spending to watch agents more closely and keep humans available for the calls that automated evaluation cannot yet be trusted to make.

Finding 8: A tooling reshuffle is coming

Nearly two-thirds plan to adopt or switch platforms within a year

We asked whether enterprises plan to adopt a new, additional, or replacement evaluation platform, and which they are considering. Few intend to stand pat.

The evaluation market is wide open. While 36% have no plans to change, a clear majority (64%) intend to adopt a new, additional, or replacement platform within twelve months, and 31% within the next quarter. The consideration set points where current usage is thinnest: Confident AI’s DeepEval leads what enterprises are evaluating (20%), ahead of OpenAI’s native evals (13%) and Braintrust (9%) — the open-source specialists drawing more interest than their present footprint.

Given that so many enterprises today rely on provider-native tools or nothing at all (Finding 4), this is less a defection than a first real wave of tooling adoption — the moment the evaluation layer starts to consolidate. Which platforms earn that trust, in a market where almost no one trusts automated evaluation yet, is the open question this series will keep tracking.

The bottom line: An evaluation gap that autonomy will widen, not close

Organizations with 100 or more employees are granting AI agents more independence than they trust their evaluations to support. Half have already shipped an agent that passed its evals and then failed a customer; almost none fully trust automated evaluation, chiefly because it doesn’t match real-world outcomes; and most watch production for uptime and cost rather than for whether the agent’s answers are right. Yet two-thirds already allow, or are actively building toward, deploying to production on automated evaluation alone.

The vendor market is early and unsettled: the most common primary evaluation tools are provider-native evals, tied with no dedicated tooling at all, and a clear majority plan to adopt or switch platforms within the year. Encouragingly, the next dollar is going to observability and — pointedly — human review, suggesting enterprises sense the gap even as they engineer past it. At 157 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: autonomy is being granted on the strength of evaluations that the people granting it do not yet trust. The evaluation gap is not a coverage problem that more tests alone will close; it is a problem of evaluations that reflect reality and can be trusted to gate it. The open question for later waves is whether assurance catches up to autonomy — or whether the false-confidence failures move from customer incidents into changes that deploy themselves.

Based on survey responses from 157 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read rather than a precise measurement — the sample is self-selected, not a probability sample, and skews toward the mid-market. Respondents include product and program managers, consultants and advisors, directors of engineering/IT, and CIOs/CTOs/CISOs, among other functions, across technology/software, retail/consumer, healthcare/life sciences, manufacturing, and other industries.

This wave of VentureBeat Pulse Research examines how enterprises secure their AI agents: what tooling they run, how they manage agent identity and isolation, what has already gone wrong, how much they spend, and whether they believe their defenses are keeping pace with AI-enabled attackers.

The central finding is an agent security gap — the distance between the autonomy enterprises are granting their agents and the controls in place to contain them. More than half of organizations (54%) have already experienced a confirmed agent security incident (18%) or a near-miss caught before harm (36%). The structural weakness beneath those numbers is identity: only about a third (32%) give every agent its own scoped, managed identity, while the rest report that some agents share credentials or that agents mostly run on shared API keys and human or service-account credentials. When agents share credentials, a single compromised or over-permissioned agent carries a wide blast radius — and only three in ten enterprises (30%) isolate their highest-risk agents in sandboxes to bound that radius.

What makes the gap notable is how comfortable enterprises are inside it. The security stack is overwhelmingly provider-native — OpenAI’s guardrails (51%), Google’s and Microsoft’s cloud controls, and Anthropic’s managed-agent controls dominate, while the dedicated agent-security specialists barely register — and satisfaction with that borrowed stack is high, averaging 4.2 out of 5. Yet spending remains a thin slice of the security budget, only a third of enterprises believe their AI defenses are ahead of AI-enabled attackers, and a clear majority plan to change tooling within the year. Enterprises are satisfied with controls they are simultaneously preparing to replace.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent security — the tooling, identity, isolation, and enforcement controls organizations use to secure autonomous AI agents. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By role the sample is senior and buyer-credible: 45% are final decision-makers for AI purchases and another 30% recommenders or influencers. Managers (43%), individual contributors (24%), VPs and directors (15%), and the C-suite (11%) make up the seniority mix. By organization size the sample is mid-market-weighted: 251–1,000 (42%) and 101–250 (25%) employees lead, with 1,001–5,000 (19%), 5,001–10,000 (8%), and 10,001+ (7%) above them. Technology/Software is the largest industry at 23%, followed by Manufacturing (15%), Retail/E-commerce (14%), and Healthcare/Life Sciences (13%).

At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent security rather than from the largest operators.

Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 82 of the 107 qualified respondents.

Finding 1: The incidents are already here

More than half have had an agent security incident or near-miss

We asked whether organizations had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm. Most that run agents in production had.

This is the report’s defining number. More than half of organizations (54%) have already had an agent security event — 18% a confirmed incident and 36% a near-miss caught before it caused harm. Only 42% report nothing, and a small remainder either run no agents in production or don’t track such events. That so many report near-misses rather than only confirmed incidents is telling: enterprises are catching problems, but they are catching them close to the edge. The controls examined in the rest of this report — identity, isolation, enforcement — are what determine whether the next near-miss stays a near-miss.

Exposure scales with company size, but containment does not. The incident-or-near-miss rate rises from 49% in the mid-market (companies with 101-1,000 employees) to 63% at larger enterprises (above 1,000 employees), while sandbox isolation of high-risk agents falls from 35% to 20%, and satisfaction with security tooling drops from 4.36 to 3.97. The organizations running the most agents across the most systems carry the most incidents and the least of the one control that bounds an incident's blast radius.

Finding 2: The identity gap

Only a third give every agent its own scoped identity

We asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Full per-agent identity is the exception.

Rolled together, the overlapping answers show 69% of enterprises (74 of 107) with credential sharing somewhere in the agent fleet. Identity is the structural weakness beneath the incidents. Only about a third of enterprises (32%) give every agent its own scoped, managed identity — the precondition for least-privilege access and clean attribution. Nearly half (48%) say some agents have scoped identities but many still share credentials, and another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. (Respondents could describe more than one pattern across their agent fleet, so these overlap.)

The consequence is direct: when agents share credentials, an over-permissioned or compromised agent can act with far more reach than intended, and forensics after an incident cannot cleanly tell which agent did what. The non-human identity problem — giving every agent its own governed identity — is the single largest unfinished piece of enterprise agent security.

Moreover, a company’s agent credential posture is correlated with incidents. Organizations with credential sharing anywhere in the fleet were hit — with an incident or a near-miss in the past twelve months — at 63.5% (47 of 74). Organizations where every agent carries its own scoped identity were hit at 40.9% (9 of 22). The fully-scoped group is small, so for now the relationship is an association rather than proven causation, and the gap is concentrated in the mid-market — but within a single survey, a twenty-three point difference in incident rate suggests significance.

Finding 3: Observe and enforce, but rarely isolate

Only three in 10 sandbox their highest-risk agents

We asked what an organization’s agent security posture looks like in practice — whether they observe, enforce, isolate, or some combination. The control that bounds damage is the least common.

Monitoring and enforcement are reasonably common; containment is not. Roughly half of enterprises observe agent activity (47%) or enforce scoped permissions at runtime (49%), but only 30% isolate their highest-risk agents in sandboxes that bound the blast radius when the other controls fail. That ordering is backwards from a defense-in-depth standpoint: observation tells you what happened, enforcement tries to prevent it, but isolation is what limits the damage when prevention fails — and it is the control enterprises have adopted least. Combined with the identity gap in Finding 2, the picture is of agents that are watched and permissioned but rarely boxed in, which is precisely the configuration in which a single failure propagates.

Finding 4: Security runs on borrowed, provider-native controls

Guardrails from OpenAI, Google and Microsoft dominate; specialists barely register

We asked which agent security tooling enterprises use, and which is their primary layer. The answer favors the model providers and hyperscalers over the dedicated security vendors.

Enterprises are securing agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 51%, followed by Google’s and Microsoft’s cloud-native controls and Anthropic’s managed-agent controls — and when asked to name their single primary security layer, 82% name one of these provider-native offerings. The purpose-built agent-security category — Palo Alto’s Prisma AIRS, CrowdStrike, Cisco AI Defense, Zenity, HiddenLayer, Check Point’s Lakera, Okta for AI Agents, non-human identity platforms — barely registers, each in the low single digits, and only 5% run no dedicated tooling at all. As with retrieval and evaluation elsewhere in this series, the provider bundle is winning the default: enterprises reach first for the guardrails their platform ships, and the independent security layer that would address the identity and isolation gaps has not yet been adopted at scale.

The provider-default pattern is consistent across both Q2 survey waves. In April–May (n=110), usage was led by the same names — OpenAI's controls at 26%, Azure at 15%, AWS at 14%, Google at 12% — with every dedicated agent-security specialist at 3% or below and one in ten using no dedicated tooling at all. The common finding from the two surveys: Enterprises are defaulting to the solutions provided by the platform they’re using, and the specialist category vendors have yet to become big players here.

(A note on reading these shares. As described in the methodology section, the respondent sample is self-selected and skews mid-market, and the usage question counted every vendor or approach a respondent has in place — so the figures measure presence in the security stack rather than spending or exclusivity. Individual vendor percentages therefore carry all the usual sample caveats. The structural pattern, however, held across both Q2 waves on two differently worded questions: provider-native and hyperscaler controls lead, and dedicated agent-security specialists remain in low single digits. Read the individual shares loosely and the pattern with confidence.)

Finding 5: And enterprises are comfortable with it

Satisfaction is high, even as incidents mount and identity lags

We asked how satisfied enterprises are with their current agent security tooling. The comfort is notably out of step with the exposure documented above.

Satisfaction with agent security tooling is high — 4.2 out of 5 overall, and 4.1 for value for money — among the most positive readings in this series. That is the striking part: enterprises are highly satisfied with a stack that is mostly borrowed provider guardrails, even though more than half have already had an incident or near-miss and only a third give their agents scoped identities. The comfort appears to rest on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is a false comfort in the making — the same enterprises expressing satisfaction are, as Finding 8 shows, a clear majority planning to change tooling within the year, which suggests the confidence is thinner than the score implies.

Finding 6: Budgets haven’t caught up

Most spend under a tenth of the security budget on agents

We asked what share of the security budget enterprises allocate to securing AI agents. For a fast-emerging risk, the allocation is modest.

Spending on agent security is still a thin slice. The most common allocation is 6–10% of the security budget (46%), and a third of enterprises (34%) spend 5% or less; only a quarter (24%) devote more than a tenth. Given the incident rate in Finding 1 and the identity and isolation gaps in Findings 2 and 3, the budget looks like a lagging indicator — the risk has arrived faster than the funding to address it. The enterprises spending more than a tenth of their security budget on agents are a distinct minority, and they are likely the ones building the scoped-identity and isolation controls the rest have not.

Finding 7: The arms race is even, at best

Only a third think their AI defenses are ahead of AI-enabled attackers

We asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence is far from settled.

Enterprises are split on whether they are winning. Only about a third (35%) believe their AI-enabled defenses are ahead of AI-enabled attackers; the rest are less sure — 32% call it roughly even, 21% think attackers are ahead, and another 21% say it is too early to tell. Taken together, a clear majority (53%) rate the balance as even or tilted toward the attacker. That uncertainty sits uneasily beside the high satisfaction of Finding 5: enterprises are content with their tooling yet unconvinced it is winning the contest it exists to win. In a domain where the offense is also compounding with AI, an even race is not a comfortable place to be.

Finding 8: A security reshuffle is coming

Nearly six in 10 plan to adopt or switch tooling within a year

We asked whether enterprises plan to adopt a new, additional, or replacement agent security solution, and which they are considering. Few intend to stand pat.

The security stack is not settled. While 41% have no plans to change, a clear majority (59%) intend to adopt a new, additional, or replacement agent security solution within twelve months, and 29% within the next quarter — a strong signal that, high satisfaction notwithstanding, enterprises know the current stack is provisional. Incidents are what start the buying cycle.

Among organizations that have been hit, 42.1% plan to adopt, add, or replace agent security tooling within the next ninety days, against 14.0% of organizations with no incident — and after a confirmed incident it becomes majority behavior, at 52.6%. Getting hit also changes the threat assessment: 33.3% of hit organizations say AI-armed attackers are ahead of their defenses, against 8.0% of the unhit. Experience, in this data, is the strongest predictor of both urgency and pessimism.

The consideration set still leans provider-native (OpenAI 34%, Google 30%, Anthropic 29%, Azure 25%), but the dedicated security vendors — Cloudflare, Cisco, Palo Alto, Okta, Check Point’s Lakera — draw early interest in the mid-to-high single digits, more than their current footprint. 

What the shopping does not yet include is the identity layer specifically. Twelve percent of the respondents include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set, and among the credential-sharing organizations that have already had an incident, identity consideration is essentially unchanged, at roughly one in ten. The control most directly implicated by the incident data is the one largely missing from the purchase plans. Whether this wave hardens the provider-native default or finally opens the door to purpose-built agent security — the identity and isolation controls the incidents call for — is the question this series will keep tracking.

The bottom line: A security gap that autonomy will test first

Organizations with more than 100 employees are giving AI agents real reach into systems and data while securing them with controls built for something else. More than half have already had an incident or near-miss; only a third give every agent its own scoped identity, and most still share credentials; only three in ten isolate their highest-risk agents; and the stack doing this work is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents.

The uncomfortable pairing is confidence with exposure: satisfaction with the current tooling is among the highest in this series, yet spending is a thin slice of the security budget, only a third believe their defenses are ahead of AI-enabled attackers, and a clear majority are already planning to replace what they have. At 107 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: agent adoption is running ahead of agent security, and the controls that matter most when something fails — scoped identity and isolation — are the ones enterprises have built least. The agent security gap is not a coverage problem that a provider guardrail will close on its own; it is a problem of identity, isolation, and enforcement built for autonomous software. The open question for later waves is whether enterprises close it deliberately — or whether a confirmed incident closes it for them.

Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read, not a precise measurement — the sample is self-selected and skews mid-market, so it's best read as the view from organizations actively standing up agent security rather than from the largest operators. Respondents are senior and buyer-credible (45% final decision-makers, 30% recommenders/influencers), spanning managers through the C-suite, and drawn primarily from Technology/Software, Manufacturing, Retail/E-commerce, and Healthcare/Life Sciences.

This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how satisfied they are, what would make them switch, where they plan to evaluate their investments, and — most revealingly — how well they can measure and control the economics of the compute underneath it all.

The central finding is a compute gap — the distance between how aggressively enterprises are investing in AI infrastructure and how little of its economics they can see. Only about one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile the compute already in place runs cold — 83% report GPU utilization of 50% or less — and fewer than half (44%) can rigorously track what their AI compute costs. Enterprises are buying more infrastructure faster than they can account for what they already own.

Enterprises are not settled on their infrastructure vendors, either: A clear majority (64%) plan to switch or add an infrastructure provider within twelve months, and 38% within the next quarter — unusually high churn intent for a category this foundational. When they choose, they choose on integration with the existing stack (41%) and total cost of ownership (35%), not on headline price: cost per million tokens is the deciding factor for just 8%. And the frontier constraint that will shape the next round of decisions — the shift from GPU compute to memory bandwidth as inference scales — is barely on the radar, with roughly one in five enterprises either unaware of it or yet to address it.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By organization size the sample concentrates in the mid-market: 101–250 employees (36%) and 251–1,000 (27%) lead, with 1,001–5,000 (22%), 5,001–10,000 (8%), and 10,001+ (7%) above them. By role it spans managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%); on purchasing authority it is buyer-credible, with 45% final decision-makers and another 30% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 26%, followed by Healthcare/Life Sciences (15%), Financial Services (13%), and Retail/E-commerce (12%).

At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It also skews toward the mid-market and toward earlier-stage adopters, so it is best read as the view from organizations actively building out AI infrastructure rather than from the largest hyperscale operators.

Finding 1: Ambition outpaces production

Only one in five run AI in production at scale

We asked where organizations sit in their AI deployment journey. Most are still building toward production rather than operating at scale.

The maturity curve is front-loaded. Three-quarters of enterprises (76%) are either experimenting or running only some workloads in production, and just 21% describe AI in production at scale. This matters for everything that follows: the infrastructure decisions in this report are being made largely by organizations still early in deployment, whose compute footprint — and whose costs — are about to grow. The evaluation and switching intentions in Findings 3 and 4 are the leading edge of that build-out, not the settled preferences of operators who have already found what works.

Finding 2: Enterprises run on hyperscalers and model APIs

The specialized GPU clouds barely register — today

We asked which providers and platforms enterprises currently use to run their AI. The answer is a familiar one: the incumbents.

The current stack is hyperscaler-and-API. Google Cloud leads at 48%, and the general-purpose clouds (Google, Microsoft, AWS, Oracle) together with the major model APIs (Gemini, OpenAI, Anthropic) account for essentially all current deployment. The specialized “neocloud” GPU providers that dominate AI-infrastructure headlines — CoreWeave, Lambda, Crusoe, Nebius and peers — register at or near zero among these enterprises today. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack. Enterprises are, for now, running AI on the providers they already buy from — which makes the evaluation intentions in Finding 3 all the more striking.

(A note on reading these shares. As described in the methodology section, this sample is self-selected and skews mid-market, and this question counted every provider a respondent uses — an average of 2.1 selections each — so the figures measure presence in the stack rather than spending or primary status. A sample built this way will show a different provider mix than a spend-weighted census of the broader market; Google's strength here, for example, is consistent with its long-standing position among smaller enterprises building on AI. Read these shares as a portrait of what this AI-active cohort runs today, and treat gaps between these figures and industry-wide market share estimates as a property of the sample rather than a contradiction of either.)

Finding 3: The next dollar goes to infrastructure they don’t yet run

AI-specialized clouds top the evaluations list

We asked where enterprises planned to evaluate AI infrastructure over the next 12 months. Their answers point away from the stack they run today.

Here is the report’s sharpest tension. The single most-cited planned evaluation area — AI-specialized clouds, at 45% — is the very category almost none of these enterprises use today (Finding 2). Nearly a third (32%) intend to evaluate non-Nvidia accelerators, and 28% in next-generation Nvidia silicon; even decentralized compute networks (16%) and sovereign compute (11%) draw meaningful interest. Read against current usage, this is not incremental — it is the leading edge of a re-platforming. The direction-of-travel question tells the same story: every infrastructure approach is net-expanding, but specialized AI clouds carry the highest net momentum (+24), edging out even the hyperscalers (+22). Enterprises are preparing to move a meaningful share of AI compute off the general-purpose cloud.

This continues a trend we saw in our April-May survey wave. Back then, usage of the AI-specialized clouds was equally marginal — CoreWeave at 3%, Lambda at 4%, Crusoe at 2% of enterprises. When we asked enterprises what change they planned in their AI infrastructure strategy over the next twelve months, the most-cited answer was moving workloads to specialized AI clouds, at 33%. Asked in April-May which emerging compute option they were most likely to evaluate AI-specialized clouds again drew the most responses. Two waves, two differently worded questions, one consistent picture: the type of cloud enterprises are most eager to assess is the type they have barely begun to use.

Finding 4: A switching wave is building

Six in 10 plan to change providers within a year — many within a quarter

We asked whether and when enterprises plan to switch or add an infrastructure provider. Very few intend to stand still.

For a category as foundational as compute, this is a remarkable amount of intended movement. Only 36% have no plans to change, meaning a clear majority (64%) intend to switch or add a provider within twelve months — and 38% within the next quarter alone. Where that interest points is telling: the providers drawing the most switching consideration are again the incumbents — Microsoft Azure and Google Cloud (33% each), OpenAI (30%), and Gemini (22%) — which suggests much of the near-term movement is reshuffling among the majors and consolidating spend rather than defecting to new entrants. The neocloud interest in Finding 3 is a 12-month evaluation thesis; the switching in the next quarter is mostly incumbents trading share.

(Method note: Respondents who selected both "no plans to change" and a specific switching window are counted as switchers, on the logic that naming a timeframe is the more specific answer; three respondents were reclassified under this rule.)

Finding 5: Nobody buys on token price

Integration and total cost of ownership decide — not sticker price

We asked what matters most when enterprises select an AI infrastructure provider. Headline price finished last.

Enterprises do not buy AI infrastructure on pricing, which is the place vendors compete on hardest. Integration with the existing stack (41%) and total cost of ownership (35%) dominate, while the headline metric — cost per million tokens — is the deciding factor for just 8%, dead last. The pattern is coherent: buyers are optimizing for how a provider fits and what it truly costs to operate, not for the advertised unit rate. It also foreshadows Finding 7 — enterprises say TCO matters most, yet most cannot yet measure it rigorously. The stated priority and the measured capability are out of step.

Finding 6: Expensive GPUs, idle most of the time

83% report GPU utilization of 50% or less

We asked what share of their GPU capacity enterprises actually utilize. The answer is a well-known but rarely quantified inefficiency.

Disclosure: Band percentages count every selection against all 107 qualified respondents; 14 respondents selected more than one band, so bands overlap. At the respondent level, 83 of the 100 GPU-operating enterprises reported utilization at or below 50%

The compute already in place runs cold. Adding the bands at or below half capacity, 83% of enterprises that operate GPUs report utilization of 50% or less, and nearly half (49%) run at 25% or below. Only 12% clear the 50% mark, and a further 8% do not measure utilization at all. Idle accelerators are expensive accelerators, and this is the clearest single measure of the compute gap: enterprises are planning to buy more GPUs and specialized compute (Finding 3) while the capacity they already own sits substantially unused. The efficiency headroom in the current fleet is large — and largely unmeasured.

Finding 7: Spending fast, measuring slowly

Fewer than half rigorously track what their compute costs

We asked whether enterprises can quantify the cost and return of their AI infrastructure spend, and how satisfied they are with what they run. Confidence in the ledger lags the spending.

Measurement trails money. Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute; the majority track only partially (39%), cannot quantify it yet (20%), or have not prioritized it (6%). That gap is consequential given Finding 5, where total cost of ownership was the second-ranked buying criterion — enterprises are choosing providers on an economic basis they mostly cannot yet measure. Satisfaction with current infrastructure is moderately positive but not enthusiastic: on a five-point scale, overall satisfaction averages 4.0, with ease of implementation (3.8) and value for money (3.9) trailing slightly — the softness landing, tellingly, on cost. Enterprises are spending quickly and accounting slowly.

Finding 8: The next bottleneck few are watching

As inference shifts from compute to memory, the field scatters

Finally, we asked how enterprises would address the emerging constraint in large-scale inference — the shift from GPU compute to memory, specifically KV-cache capacity. The responses reveal a frontier that is not yet a priority.

The memory frontier is real but barely governed. Asked which approach they would rely on as the binding constraint in inference shifts from compute to memory bandwidth, enterprises scatter: Dell leads at 31%, Nvidia follows at 16%, and the rest fragments across storage vendors, open-source tooling, and model-level efficiency techniques. Most telling is that roughly one in five (18%) either do not recognize the constraint or have not begun to address it. For a shift that will reshape inference cost and architecture, this is an early and unsettled market — and, consistent with the measurement gap in Finding 7, one where many enterprises simply do not yet have a view. It is the next chapter of the compute gap, arriving before most have closed the current one.

The bottom line: A compute gap that faster spending will widen, not close

Organizations with more than 100 employees are investing in AI infrastructure faster than they can measure it. Most are still early in deployment, yet their spending intentions point past their current stack — toward specialized clouds and alternative accelerators almost none of them run today — and a clear majority intend to change providers within the year. They buy on integration and total cost of ownership rather than headline price, which is rational; the difficulty is that most cannot yet see those economics clearly.

The visibility gap is concrete. The GPUs enterprises already own run at half utilization or less for the overwhelming majority, and fewer than half can rigorously track what their compute costs or returns. Satisfaction is decent but unenthusiastic, softest on value for money — the dimension hardest to judge without measurement. And the next constraint, the shift from compute to memory in large-scale inference, is arriving while most enterprises are still unaware of it. At 107 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market and earlier-stage adopters — but the direction is consistent: the appetite to spend is running well ahead of the instrumentation to spend well. The compute gap is not a capacity problem that more hardware will solve on its own; it is, first, a problem of seeing what the hardware already costs. The open question for later waves is whether enterprises build that visibility before the re-platforming arrives — or buy the next layer of infrastructure as blind to its economics as the last.

Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the results read cross-sectionally rather than as a month-over-month trend, and at 107 respondents this is a directional signal rather than a precise measurement — the sample is self-selected, skews mid-market, and leans toward earlier-stage adopters rather than the largest hyperscale operators. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with buyer-credible purchasing authority, across Technology/Software, Healthcare/Life Sciences, Financial Services, Retail/E-commerce, and other industries.

Wenn Sie einen KI-Chatbot darum bitten, Namen für einen Podcast, ein WLAN-Netzwerk oder ein kleines Unternehmen zu entwickeln, werden Sie wahrscheinlich eine Liste mit Vorschlägen erhalten, die ein wenig unkreativ ist.

Große Sprachmodelle wie ChatGPT, Claude und Gemini haben kein Problem damit, ein Dutzend Namen für Ihr Lieblingsprojekt oder Ihre Website zu generieren. Aber ein Dutzend Namen zu erhalten, die wirklich vielfältig, einzigartig und einprägsam sind? Das ist deutlich schwieriger – aber dennoch möglich. Sie müssen nur wissen, wie Sie die Modelle auf die richtige Weise in verschiedene Richtungen lenken können.

Bitten Sie ChatGPT zunächst nicht nur um 10 oder 20 Ideen, sondern um 100. Eine Studie der Wharton School [PDF] legt nahe, dass die Ideen, wenn Sie eine KI um so viele Ideen bitten, umso interessanter werden, je weiter Sie in der Liste nach unten gehen. Dies ist der „Dump“-Teil dieser zweistufigen Prompt-Technik.

In der zweiten Stufe bitten Sie ChatGPT, die Liste zu durchforsten, nach ähnlichen Einträgen zu suchen und diese durch neue zu ersetzen – alles mit dem Ziel, eine möglichst breite und vielfältige Ideensammlung zu schaffen.

Hier ist ein Beispiel für die erste Stufe der Eingabeaufforderung:

Gib mir 100 Ideen zu [Thema X]. Nummeriere diese von 1 bis 100. Gib für jede Idee nur einen kurzen Titel oder Namen an – keine Erklärungen, keine Beschreibungen. Beziehe alles mit ein, auch offensichtliche, schlechte, seltsame oder unausgereifte Antworten. Filtere nicht nach Qualität; das folgt später. Quantität ist das einzige Ziel.

Sobald die KI ihre Liste geliefert hat, fahren Sie mit der zweiten Phase fort:

Überarbeite nun die Liste im Hinblick auf maximale Vielfalt. Wo immer zwei oder mehr Ideen auf demselben Grundkonzept beruhen, behalte die beste davon bei und ersetze die anderen durch Ideen aus Blickwinkeln, die sonst nirgendwo auf der Liste abgedeckt sind. Das Ziel sind 100 Ideen, bei denen keine zwei auf dasselbe zugrunde liegende Konzept verweisen – sie müssen sich in ihrer Art unterscheiden, nicht nur im Wortlaut.

Optional können Sie mit einer Eingabe für die dritte Phase fortfahren, die die KI dazu veranlasst, die Liste nach Qualität zu filtern (ich empfehle jedoch, alle 100 Ideen der zweiten Phase selbst durchzugehen):

Was sind die 10 interessantesten Ideen auf der zweiten Liste?

Ich habe diese „100-Ideen“-Anweisung (die ich aus einer in der oben genannten Wharton-Studie vorgestellten Anweisungskombination adaptiert habe) für einen lang gehegten Traum ausprobiert: die Eröffnung meines eigenen Cafés. Eine der größten Hürden ist natürlich die Wahl eines einfallsreichen Namens, also habe ich diese zweistufige Anweisung gestartet.

Ich möchte Sie nicht mit der gesamten Liste der Vorschläge langweilen, die ich erhalten habe. Aber hier sind die ersten 10 aus der ursprünglichen Auswahl:

• The Daily Grind
• Bean There
• Brewed Awakening
• Central Perk
• The Coffee House
• Morning Cup
• Java Junction
• Common Grounds
• Cup & Bean
• The Roasted Bean

Dabei kamen die üblichen Verdächtigen heraus, bis hin zum „Central Perk“ aus der Serie Friends. Aber auch einige interessante Wortwitze.

Nach der Aufforderung der zweiten Stufe und der optionalen dritten Stufe („Nenne mir die 10 interessantesten Namen aus der zweiten Liste“) kam ich schließlich auf folgende Ergebnisse:

• Warm Noise
• Morning Object
• Public Living Room
• Moth & Match
• Localhost
• Borrowed Sugar
• Unfinished Sentence
• Blue Hour
• The Loading Bar
• Sunday Weather

Das sind wirklich ungewöhnliche, unkonventionelle Ideen für den Namen meines zukünftigen Cafés. Einige davon sind ein wenig techniklastig („Localhost“) oder einfach nur seltsam („Morning Object“), andere hingegen haben meine Aufmerksamkeit geweckt. „Blue Hour“ und „Borrowed Sugar“ gefallen mir tatsächlich sehr gut.

Probieren Sie diese zweistufige „100-Ideen“-Übung doch einmal aus, wenn Sie das nächste Mal Ideen benötigen. Selbst wenn dabei nicht gleich der perfekte Name für ein Café, einen Podcast oder einen Blog herauskommt, wird sie zumindest Ihre Kreativität anregen.

ChatGPT im Alltag – 16 lästige Aufgaben, die KI für Sie erledigen kann

Hören Sie auf, ChatGPT Ihre Texte schreiben zu lassen – Versuchen Sie das stattdessen

Read more of this story at Slashdot.

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The post Claude Cowork Flaw Could Let AI Agent Escape Its VM and Access Mac Files appeared first on IT Security News.

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The post How attackers hosted a fake Claude download page on the claude.ai domain appeared first on IT Security News.

Artikel & Newsletter: https://digitaleprofis.de/die-ki-news-der-woche-vom-23-07-2026/

Quellen
OpenAI-Modelle hacken Hugging Face 
Artikel: https://openai.com/index/hugging-face-model-evaluation-security-incident/ 
Hintergrund: https://huggingface.co/blog/security-incident-july-2026 

Digitaleprofis.de hat ein Update bekommen 
Website: https://digitaleprofis.de/ 

Kimi K3 ist da 
Artikel: https://www.kimi.com/de/blog/kimi-k3 
Artikel: https://apnews.com/article/kimi-k3-china-ai-0d8a5e268deb11a673f4d444fc597cc5 
Ausprobieren: https://www.kimi.com/ 

Neue Gemini Modelle 
Artikel: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/ 
Modellkarte: https://deepmind.google/models/model-cards/gemini-3-6-flash/ 
Cyber-Modell: https://deepmind.google/blog/introducing-gemini-3-5-flash-cyber/ 

Neue Kennzeichnungspflichten des EU AI Acts 
Artikel: https://digital-strategy.ec.europa.eu/en/news/commission-publishes-guidelines-transparency-obligations-providers-and-deployers-certain-ai-systems 
Doku: https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act 

Anthropic zahlt 1,5 Milliarden Dollar 
Artikel: https://m.investing.com/news/stock-market-news/us-judge-approves-anthropics-15-billion-settlement-of-copyright-lawsuit-4801706?ampMode=1 
Artikel: https://apnews.com/article/74b140444023898aeba8579b6e9f0d63 

NotebookLM wird zu Gemini Notebook 
Artikel: https://blog.google/innovation-and-ai/products/gemini-notebook/notebooklm-gemini-notebook/ 

Microsoft und Mistral Partnerschaft 
Artikel: https://news.microsoft.com/source/2026/07/21/microsoft-and-mistral-expand-strategic-partnership-to-give-enterprises-and-regulated-industries-frontier-ai-they-can-control/ 
Artikel: https://www.tagesschau.de/wirtschaft/unternehmen/microsoft-mistral-ai-ki-deal-100.html 

Qwen-Audio-3.0-TTS 
Artikel: https://www.alibabacloud.com/blog/qwen-audio-3-0-tts-more-multilingual-easier-to-direct_603379 
Doku: https://docs.qwencloud.com/developer-guides/speech/tts-models 

OpenAI-Modelle überwinden bei einem internen Sicherheitstest ihre isolierte Testumgebung und gelangen bis in die Produktionsinfrastruktur von Hugging Face.
Wir ordnen den Vorfall ein und fassen die weiteren wichtigen KI-News der Woche kompakt für euch zusammen.

Außerdem geht es um Kimi K3, drei neue Gemini-Modelle, die kommenden Kennzeichnungspflichten des EU AI Acts und den milliardenschweren Urheberrechtsvergleich zwischen Anthropic und Autoren.

Im Video:
- OpenAIs ungewöhnlicher Sicherheitsvorfall bei Hugging Face
- Kimi K3 und drei neue Gemini-Modelle
- Kennzeichnungspflichten des EU AI Acts ab August 2026
- Anthropics Vergleich über mindestens 1,5 Milliarden US-Dollar
- Gemini Notebook, Microsofts Mistral-Partnerschaft und Qwen-Audio-3.0-TTS

Unsere Website wurde ebenfalls vollständig überarbeitet. Auf digitaleprofis.de findet ihr unsere KI-News mit allen Quellen, ausführliche Artikel und praktische Anleitungen – kostenlos, ohne Paywall und ohne Werbung.

Werde Kanalmitglied und unterstütze damit unsere Arbeit:
https://www.youtube.com/channel/UCv90NdTyTp7ZPPRvvSZaS5w/join

Videoinhalt:
00:00 Die KI-News der Woche vom 23.07.2026
00:24 OpenAI Modelle greifen HuggingFace an
02:06 Unsere neue Website für euch
02:59 Kimi K3 ist da und die USA werfen Diebstahl vor
04:28 Drei neue Gemini Modelle, aber kein Pro
05:53 Die neuen Kennzeichnungspflichten des AI Acts
07:21 Anthropic muss 1,5 Milliarden Dollar Vergleich zahlen
08:48 NotebookLM verschwindet - und wird zu Gemini Notebook
09:30 Partnerschaft von Microsoft und Mistral
10:50 Neuen Text to Speech Modell von Alibabas Qwen

Videovorschläge, Feedback und Kritik kannst Du uns jederzeit in den Kommentaren mitteilen!

According to OpenAI’s disclosure, the incident began during an internal evaluation of advanced cyber capabilities using GPT-5.6 Sol and a more capable pre-release model. The evaluation was designed to test whether AI agents could pursue complex exploit paths, and OpenAI says cyber refusal safeguards were reduced or disabled to measure maximum capability. Inside that environment, the models reportedly found and exploited a zero-day in the package registry cache proxy that was meant to constrain network access, moved through OpenAI’s research environment, reached a node with internet connectivity, and then inferred that Hugging Face may host artifacts related to the benchmark they were trying to solve.

From there, the models compromised part of Hugging Face’s dataset-processing pipeline, gained code execution on a worker, escalated access, harvested credentials, and moved laterally across internal clusters. Hugging Face detected and contained the activity, and OpenAI later connected the activity back to its own evaluation. Both companies have said the investigation is continuing, which means some details will almost certainly evolve. Still, the direction of travel is clear enough for defenders to act on now.

How did the OpenAI model evaluation reach Hugging Face?

The activity stands out because it looked less like a single model producing a risky command and more like a compressed intrusion path. Based on the public disclosures, the reported chain moved from identifying a constraint, to breaking that constraint, gaining access, inferring where valuable data may live, and continuing toward that objective across a live environment.

Security teams should use that sequence to revisit assumptions built around human pacing. Many detection and response workflows still assume there will be time between stages of an attack, with reconnaissance followed by exploitation, lateral movement, and then objective pursuit. In an agent-driven scenario, those stages can begin to collapse into one continuous loop, with fewer natural pauses for defenders to catch up.

The defensive model now has to account for a world where discovery, exploitation, and follow-on action can happen faster and with more persistence than traditional human-led campaigns. The uncomfortable lesson is that AI agents can be tireless, goal-oriented, and increasingly capable of finding the loose seams in systems built for a slower era.

The incident highlights the collapse of the traditional OODA (Observe-Orient-Decide-Act) loop. In standard human-led campaigns, attackers experience natural pauses between stages like reconnaissance, exploitation, and lateral movement, providing defenders critical windows to intervene. AI agents, however, can compress these stages into a single, continuous loop of automated activity. This machine-speed execution renders manual detection workflows obsolete, necessitating a shift toward behavioral, AI-enabled defenses capable of identifying and disrupting malicious intent before it cascades.

Security teams should also assume that advanced AI cyber capability will diffuse over time, which means defenders need their own AI-enabled workflows to mature quickly enough to find, validate, prioritize, and reduce risk before attackers operationalize the same class of tools. Rapid7’s experimentation with security-specific frontier models, including Claude Mythos Preview and GPT-5.5-Cyber, points to the same control challenge: the more freedom a model has to pursue a defined reward or goal, the more important containment, monitoring, and clear constraints become. Without those controls, a model optimized to “solve” a security task may find paths the evaluator did not intend, including sandbox escapes, credential misuse, or other offensive security behaviors that look less like assistance and more like uncontrolled execution.

Why AI evaluation environments need stronger containment

The incident raises practical questions about how frontier AI evaluations are designed and contained. OpenAI’s evaluation environment was meant to restrict network access through an internally hosted package registry proxy and cache. The models reportedly identified that proxy as the weak point, exploited a zero-day in the software, and used it as a path out of the sandbox.

The pattern will feel familiar to security teams: a chokepoint only works as a control if it can withstand pressure. Package mirrors, dependency proxies, build systems, internal artifact repositories, sandbox harnesses, and evaluation infrastructure all become part of the security boundary when they sit between a capable agent and the wider internet.

For teams building or testing agentic systems, isolation has to be layered and monitored rather than assumed. Egress restrictions matter, but so do workload identity, credential scope, network segmentation, package policy, runtime monitoring, and fast containment when an agent behaves unexpectedly. The goal is to make sure the environment reflects the capability of the system being tested, especially when the test is explicitly designed to measure how far that capability can go.

Why AI and ML pipelines are now software supply chain risk

The Hugging Face side of the incident is a reminder that AI and ML pipelines are part of the software supply chain. Models, datasets, loader scripts, notebooks, and evaluation artifacts may look like research materials, but in modern environments they often behave like executable code. Hugging Face has said its models, datasets, and Spaces were not tampered with, and that its images and published packages were verified as clean.

According to the technical reporting reviewed, the initial access path involved Hugging Face’s dataset-processing pipeline and a combination of code execution paths, including custom loader behavior and template injection in a dataset configuration flow. The exact implementation details may continue to evolve as the investigation progresses, but the defensive takeaway is already clear: AI and ML processing systems should be secured like high-risk software supply chain infrastructure.

Any system that automatically processes external datasets or model artifacts should be designed with hostile input in mind. Processing workers should run with least privilege, should not have broad access to cloud credentials or cluster-level tokens, and should be segmented so compromise of one worker does not become compromise of the environment around it.

Security teams should also hunt for early signs of intent drift inside ML workflows. Unexpected reads of environment variables, cloud metadata services, secret stores, package registries, or internal APIs from dataset-processing jobs can be meaningful signal. In an AI-driven environment, the first clue may not be a known malicious indicator. It may be a workload behaving with curiosity it should not have.

What AI guardrails mean for incident response

One of the most useful lessons for security teams came during the response, when Hugging Face’s responders reportedly needed to analyze logs containing exploit payloads, attacker commands, and command-and-control artifacts. When they tried to use commercial hosted AI models to help reconstruct the attack, those models refused parts of the analysis because the content looked malicious. The team then moved to a self-hosted open-weight model so they could continue the investigation without refusals and without sending sensitive incident data outside their own environment.

The practical issue here is guardrail asymmetry: attackers, rogue agents, or unrestricted systems may not respect usage policies, while defenders using hosted tools can be slowed by the same safety systems designed to prevent misuse. Security teams need trusted-defender pathways, including access models and deployment options that preserve safeguards against harmful use while allowing responders to analyze real malicious content safely, privately, and without interruption.

AI-assisted incident response also needs to be tested before an incident begins, especially if teams expect models to support triage, log analysis, malware review, or timeline reconstruction under pressure. Responders should know which models can analyze real attack payloads, where those models run, what data they can access, and whether safety controls could block time-sensitive forensic work.

What security teams should do about agentic AI risk

The practical response to this incident is preparation, with organizations treating AI agents as both powerful tools and potential attack surfaces when those agents can access code, credentials, infrastructure, datasets, or internal systems.

Security teams should apply familiar controls with more urgency across AI and ML environments: treat downloaded models, datasets, and loader scripts as untrusted code; enforce least privilege on AI and ML processing workers; prevent node-level compromise from becoming cluster-wide compromise; restrict access to cloud metadata endpoints and secrets; monitor unexpected outbound connections from AI-related workloads; and review whether evaluation environments have real containment or simply convenient isolation.

Detection logic needs to account for machine-speed activity. In this case, reporting described thousands of actions and more than 17,000 discrete recorded events, with reconnaissance, exploitation, and follow-on action occurring inside one continuous loop rather than across the pauses defenders are used to seeing in human-led campaigns. Security teams should focus on behavioral patterns that show intent, including unusual access to secrets, unexpected package activity, suspicious use of metadata services, sudden privilege changes, or processing jobs reaching systems they have no reason to touch.

As autonomous activity becomes faster and noisier, the bottleneck may shift from detecting that something happened to understanding what matters quickly enough to change the outcome. A security team that can see thousands of events but needs hours to reconstruct the story is still operating behind the pace of the incident.

How preemptive security helps reduce AI-driven risk

At Rapid7, our view is that this is where preemptive security becomes especially important. Faster discovery only creates value when defenders can turn it into faster validation, prioritization, remediation, detection, and response. The same principle applies to agentic AI risk. If AI accelerates how weaknesses are found and exploited, defenders need security operations that can act earlier with better context and more confidence.

That means connecting exposure management with detection and response, so teams understand which risks are exploitable, which assets matter most, what suspicious behavior is already present, and which actions will reduce risk fastest. It also means using AI carefully and practically, not as a replacement for security judgment, but as a way to reason across telemetry, reduce noise, support investigation, and help teams make decisions at the speed the threat environment now demands.

AI-enabled defense is becoming part of resilience planning, especially for organizations running critical systems or high-value digital infrastructure. The goal is to give defenders the speed, context, and consistency to operate inside the attacker’s decision cycle, without removing the judgment and accountability that effective security requires.

The OpenAI and Hugging Face incident will continue to generate debate as more details emerge, but defenders already have enough to work with. Agentic systems are beginning to test the seams between AI research, software supply chain security, cloud infrastructure, and incident response. The organizations best positioned for what comes next will be the ones making those seams visible, monitored, and resilient before the next incident puts them under pressure.

The post How attackers hosted a fake Claude download page on the claude.ai domain appeared first on Help Net Security.

The post FakeAgent Campaign: Malicious Claude Artifact Used to Distribute SectopRAT to 29 Organisations appeared first on IT Security Guru.