OpenAI president and co-founder Greg Brockman warns that enterprise security teams face a compressed timeline to adopt AI defences.
Brockman has published an account of what the company calls the “OpenAI-Hugging Face” incident, using it to argue that organisations need to uplevel their security practices with what he terms unprecedented speed. He writes that he has spoken with many organisations since the incident and found a consistent theme running through those conversations: leaders know they must move faster than their current security programmes allow.
The urgency stems from a specific event. An “agentic collective” autonomously penetrated OpenAI’s own research infrastructure and then moved into the production infrastructure of Hugging Face. The attackers chained together previously unknown security flaws with leaked user account credentials found on the internet to complete the intrusion. Brockman calls it a preview of how a typical threat actor’s capabilities will evolve over the coming months.
The AI defence decision facing security leadersBrockman argues the incident exposed a problem that extends beyond any single company’s network. He writes that accumulated technical debt inside every organisation “masks significant flaws” that defenders now need to locate and fix before attackers do.
AI models developed across the industry are increasingly able to automate parts of real-world cyberattacks, he says, which makes long-standing security gaps easier to find and exploit. Those gaps range from bugs embedded deep in human-written software to forgotten permissions left unmanaged for years.
The timeline for that decision is short by Brockman’s own account. Earlier in the year, OpenAI began releasing its cyber capabilities only to trusted defenders rather than the public, a deliberate attempt to keep defenders ahead. Since then, other companies have released open-weight models with cyber capabilities trailing the frontier by only a few months.
Brockman points to a further model that appears scheduled for release at the end of August, which he says seems likely to accelerate the threat landscape significantly. For enterprise leaders, that compresses the window for building AI-assisted defences before broadly available models close the gap with attacker capability.
Brockman frames the underlying dynamic as a race with two edges. AI-powered attackers will soon be able to find long-standing flaws across many existing systems, he writes, but the same technology gives defenders tools to find, prioritise, and fix those flaws faster.
While describing security as remaining a cat-and-mouse game, Brockman argues that AI may shift its underlying economics in ways that favour defenders. OpenAI states it has begun training models specifically to write more secure code. Separately, the company points to its models’ capability in mathematical proofs, which it says can be applied to formally verify software security in ways that have proven difficult for human reviewers to achieve at scale.
A test case against Brockman’s personal websiteBrockman offers a personal example of what faster response looks like in practice. After the incident, he asked ChatGPT Work, running publicly available GPT‑5.6 Sol, to assess the security of his personal site, gregbrockman.com. He describes it as a simple static site hosted on AWS with Cloudflare acting as a frontdoor, and says he expected limited surface area for vulnerabilities.
The assessment took about 15 minutes and surfaced 13 issues. Brockman says many probably were not exploitable by themselves, but he could imagine them being chained together with other vulnerabilities. The tool found that his DNS records were not configured to prevent attackers forging emails from his address. His site was running an insecure version of jQuery and Cloudflare was forwarding requests to AWS over unencrypted HTTP.
He then asked ChatGPT Work to fix the issues, which it did over roughly an hour. The tool opened the Cloudflare control panel in his browser and worked through DNS, TLS, and advanced security settings. It removed jQuery from the site entirely, migrated the site from AWS to Cloudflare Pages, and began a phased rollout of DMARC.
Brockman says this as a small-scale demonstration of existing models operating as what he terms a cyberguardian, capable of finding a long tail of configuration issues that a human might lack the time or specific expertise to address, then applying fixes with an appropriately staged rollout.
How OpenAI restructured its own defencesBrockman writes that the Hugging Face incident showed OpenAI had underestimated the real-world cyber capabilities of its own AI models, prompting the company to strengthen its safety requirements and add urgency to existing safety research and internal security work. He sets out four areas of internal investment that inform his recommendations to other organisations.
The first is using OpenAI’s own models to help secure its code. Codex, along with a security plugin, validates code changes and identifies vulnerabilities before deployment. Brockman is explicit that producing more findings requiring human validation is not the goal; the aim is catching real vulnerabilities before they ship and shortening the time between discovering an issue and deploying a fix. OpenAI’s ambition is to eliminate some classes of software vulnerabilities in newly-authored code.
The second pillar involves using models to defend infrastructure on an ongoing basis. Brockman says almost all of OpenAI’s initial security alerts are now triaged by AI systems before humans get involved, which he says reduces workload for defenders and improves response time. The company is connecting these detections to bounded automated responses while keeping humans responsible for the highest-impact decisions, with the stated goal of detecting and responding to security issues at machine speed.
Third, OpenAI uses its models to continuously enumerate and probe for potential attack paths, looking for vulnerabilities, misconfigurations, over-privileged identities, and unintended trust boundaries. This supports what Brockman calls ongoing assessment of the company’s security invariants, the properties it believes should hold true across its products and infrastructure.
The fourth pillar is investment in fundamentals at scale, including secure architecture, defence in depth, and least privilege. The stated design goal is systems requiring multiple independent controls to fail simultaneously before anything catastrophic can occur. Network isolation, workload hardening, monitoring, and patching and deployment practices remain part of this baseline, and Brockman says they will matter more – not less – as AI capability increases on both sides.
What Brockman tells enterprise security teams to do nowBrockman sets out a list of actions for security teams, framed around speed rather than a full programme redesign. He recommends securing organisational buy-in and running tabletop exercises to model how these attacks might play out inside a given organisation. He advises giving security teams an agentic tool such as Codex or the Codex Security plugin, with approved access to codebases and infrastructure configuration, starting with the highest-priority systems rather than waiting for a company-wide rollout.
He suggests equipping that agent with community-supported skills covering static analysis, security-focused code review, vulnerability variant analysis, and software supply-chain risk, then building organisation-specific skills around existing architecture and threat models. Organisations should run assessments against internet-facing services, authentication flows, infrastructure-as-code, and systems handling sensitive data first. Teams should then work through existing backlogs of scanner output, dependency alerts, and bug bounty reports, asking the agent to distinguish exploitable issues from noise.
Brockman also recommends embedding agent-based review directly into development pipelines, checking for authentication mistakes, access-control bypasses, exposed credentials, and unsafe dependencies before code merges. For validated issues, he suggests having the agent generate a patch, write a regression test, and confirm the vulnerability no longer reproduces, while keeping human review for consequential changes.
On automation, Brockman advises an incremental path rather than attempting to build an autonomous security operations centre immediately. Organisations should start with read-only scans of a single repository, move to advisory pull-request scanning, then live alert triage, and only later introduce automatic closure of narrowly defined false positives. A human should make every decision until confidence builds through that sequence.
He also points organisations towards applying for Trusted Access for Cyber to gain approval to use GPT‑Daybreak‑Blue for defensive work including incident response, detection engineering, and malware analysis. Brockman recommends practising with the capability on logs and telemetry before an actual incident forces the issue.
Brockman closes by arguing that no company can address this alone, calling on AI labs, security vendors, enterprises, and maintainers to share validated findings, fixes, and playbooks so that one organisation’s discovery strengthens the wider ecosystem. He describes the defender’s window as open now, with organisations needing to automate security programmes over the coming months to keep pace with attacker capability, ahead of the further open-weight model he expects at the end of August.
See also:Alvys launches AI agents for freight TMS workflows
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
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Back in 2022, the Center for Countering Digital Hate released a blistering report finding that social media platforms Facebook, Twitter, Instagram, YouTube, and TikTok all failed to remove 90 percent of Islamophobic content from their platforms.
Facebook, in particular, had only stepped in to moderate seven out of 125 posts flagged by the Center, in direct violation of its own community guidelines on “expressions of contempt.”
Fast forward four years, and it’s clear the platform has only become worse.
As we reported over the weekend, the social media platform is currently teeming with various forms of Islamophobic and anti-Arab content. It’s a noxious blend of propaganda and misinformation, with often AI-generated imagery whipping viewers into a bigoted frenzy; many who interact with the miasma of online hatred then post more intolerance, harassment, and calls for violence — both from the state and by private citizens — against Muslim Americans.
While Facebook’s older, more conservative user base has long been a haven for Islamophobia, the recent frenzy is reaching a fever pitch directed at residents of Dearborn, Michigan, who make up the largest Arab-American community in the United States.
Much of that vitriol can be traced back to white supremacist social media personality Jake Lang, who recently shared an AI-generated flyer calling on his reactionary followers to wage a “crusader march on Dearborn” on Tuesday, August 18th.
The post calls on Lang’s followers to “stop [the] Islamification of America,” and features imagery of Catholic crusaders charging at Arabs with swords drawn — an overt reference to the medieval crusades, during which Catholic soldiers slaughtered millions of Muslims, Jews, and Orthodox Christians.
Despite dripping in obvious references to anti-Muslim violence, Facebook refuses to take the post down. After reporting it to the platform for going against its own policies on “calling for violence,” we received a seemingly automated message exactly one minute later explaining that Facebook “didn’t remove the post.”
Plenty of other posts remain up despite having been reported to Facebook for similar grounds. One post saying that “it’s time for hunting” hasn’t been touched a day after it was reported, and neither has a comment on Futurism’s own post calling Muslims “f**kin rag headed pedophiles.”
Another comment posted under a news bulletin about the Dearborn hysteria literally says “I hope we kill people” in reply to a comment seething that the “first shot has to be heard somewhere,” neither of which have been taken down after we reported them for calling for violence.
And though a number of posts Futurism flagged to Facebook’s parent company Meta as part of a request for comment have been hidden, more remain up, even after our initial reporting on Sunday. One of the most flagrant examples is a comment — under a reel containing AI-generated imagery — which calls Muslims “cockroaches that have infested our beautiful country” and insists we need to “remove each and every moslim [sic] man, woman, and child.”
Rhetoric comparing humans to bugs clearly falls under “dehumanizing” content at least, and calls for violence at worst. There are hundreds of years of precedent here: as far back as 17th century England, people in powerful positions have invoked ideas of “parasitism” and “vermin” to justify ethnic violence. The rhetorical move has come back into vogue over the past few years, fueled by increasingly popular far-right figures like Lang and his contemporary Nick Fuentes.
Put together, it comes as little surprise that a company with Facebook’s genocide-enabling track record would act so flagrantly in service of these hard-line reactionaries. There’s no question that this kind of content is wildly popular with the platform’s user base, and therefore financially beneficial for Meta to propagate — and at the end of the day, isn’t that what it’s all about?
More on Meta:Facebook AI Slop Has Grown So Dark That You May Not Be Prepared
The post Meta Declines to Remove Horrific Posts Calling for Violence Against Muslims, Calling Them “Cockroaches” and Saying It’s “Time for Hunting” appeared first on Futurism.
Normally, you’d imagine huge megacorporations having a personal army of the best lawyers in the world who can spin anything in their favor. You’d certainly think that would be the case for 3M, the industrial conglomerate that got away with permanently poisoning our planet with “forever chemicals.”
But nobody’s perfect all the time. In a lawsuit about who’s liable for a Houston explosion that killed three people, an “expert witness” that 3M’s legal team hired turned out to be a total hack who used AI to put together his entire report defending the company, 404 Media reports.
The opposing legal team suspected AI usage during the discovery phase, and demanded to see the expert witness’ ChatGPT logs. The judge obliged, and 3M’s legal team were forced to turn over 350 pages of his activity on the AI platform — which also, amazingly, contained public links to his full conversations.
The resulting reckoning was brutal. The logs showed that the man, Josh Autenrieth of Knighthawk Engineering, asked ChatGPT to “create an exceptional expert witness report defending the standard of care at 3M,” and that the resulting document should “show how 3M is 0 percent at fault” for the lethal explosion at Watson Grinding, which also destroyed hundreds at homes.
“I am being retained as a professional expert witness by 3M in defense of them in their lawsuits and other legal proceedings behind the January 2020 explosion at Watson Grinding,” one prompt began.
It’s no exaggeration to say that Autenrieth was asking ChatGPT to do his job for him. He attached hundreds of court records and asked the AI to read all of them and create a defense of 3M that showed that Watson Grinding was responsible for the explosion, the logs showed.
The resulting 30 page report that the AI created stated that, “From a technical and standard-of-care standpoint, 3M is 0 percent responsible for the January 24, 2020 explosion.” (This amateurish line was later removed because when Autenrieth asked ChatGPT to go over its own work, it stated that “‘0 percent responsible’ is an easy target” for a lawyer to dismantle.)
Autetrith, purportedly an expert with twenty years of experience in gas detection, also uploaded a picture of a gas detector and asked ChatGPT, “what am I looking at?”
According to Will Moye, one of the plaintiffs’ attorneys, that gas detector asked about is the “subject of the whole case.”
“This expert relied on AI not as an assistive device, but exclusively relied on ChatGPT to form his opinions and write his report,” Moye told 404. “He acknowledged [at trial] the prompts he put in were biased toward 3M to help 3M win the case… it’s really egregious.”
From an AI in law point of view, one interesting upshot of the case is that ChatGPT transcripts can now be exposed in discovery or during depositions, the reporting noted — which is gonna cause all sorts of OPSEC headaches in the legal world.
“Every lawyer needs to make sure their own experts aren’t generating work product in a way that’s insincere, and then knowing you can subpoena the prompts,” Moye added. “I’ve got lawyers all over the place saying, 1) ‘Holy shit, man. How did you get the prompts?,’ and 2) ‘How many cases do I have where this is happening to us?'”
More on AI: Furious Judge Cancels Entire Trial After Finding Out Lawyers on Both Sides Used AI
The post Man Obliterated When Court Obtains His ChatGPT Transcripts appeared first on Futurism.
Freight software provider Alvys has launched an agentic AI platform that allows carriers and brokers to automate operational tasks directly within its transportation management system (TMS). Called Alvys Foundry, the platform supports pre-built and custom AI agents that work with freight data and workflows already held within the TMS.
Foundry includes more than 20 pre-built agent templates. Customers can also create their own agents or work with Alvys engineers to build and configure agents around specific operating procedures.
From AI assistance to executionThe agents are designed to carry out defined steps in freight workflows, including detention processing, document handling, rate audits, shipment tracking, asset compliance, and claims management.
Alvys’ Detention Agent can identify when detention time has been exceeded and file the detention. Document Intelligence reads and files rate confirmations, bills of lading, and proofs of delivery, while the Track & Trace Agent handles check calls and shipment status updates.
Other templates include a Rate Audit Agent for checking invoices, an Asset Compliance agent for authority, insurance, and safety records, and a Claims Agent for opening and documenting claims.
Customers do not necessarily have to program those workflows themselves. Alvys says operators can upload an existing standard operating procedure or describe a task in plain language, after which Foundry generates a workflow for approval.
Agents can be tested against simulated data before deployment on live freight. Operators can then build, deploy, monitor, and pause them through the Foundry platform.
Alvys CEO Nick Darman said the platform was developed after the company observed how customers were adopting AI across their freight operations. He said some implementations introduced additional tools and logins without fitting into existing operating procedures.
Alvys already uses AI elsewhere in its TMS. Insights provides information across loads, lanes, and margins, Intel issues alerts on issues including weather and cargo theft, and another AI feature creates loads from uploaded rate confirmations. Foundry adds agents that can perform approved tasks within configured workflows.
Foundry operates on the existing infrastructure behind Alvys’ TMS, which includes more than 120 integrations and native electronic data interchange connections with hundreds of shippers. This gives the agents access to operating context such as lane history, customer rules, documents, margins, appointments, and exceptions.
“We have the freight context and we understand your lanes,” Darman said. He added that keeping the agents within the existing platform removes the need to maintain separate integrations and logins.
Alvys said Foundry runs on a SOC 2-compliant security foundation and that its agreements with model providers prevent customer data from being used to train public models.
Foundry’s Agent Shield governance layer lets operators set approval and spending thresholds for individual agents. Higher-impact actions can be paused for human sign-off, while agent decisions, actions, and manual overrides are recorded in an audit trail.
Foundry also includes a model-selection system that can route tasks between large language models based on factors including cost, speed, and quality. The system is designed to manage computing costs and avoid tying workflows to a single model provider.
AI agents expand across freight workflowsC.H. Robinson is also using AI agents for operational freight tasks. The freight broker told DC Velocity in January that it had deployed more than 30 agents that had collectively completed millions of tasks previously handled manually.
“Agentic AI doesn’t just analyse or generate content; it acts autonomously to achieve goals like a human would,” Mark Albrecht, vice president of artificial intelligence at C.H. Robinson, said.
One C.H. Robinson agent processes more than 10,000 emailed pricing requests a day by reading the request, obtaining a price from the company’s pricing system, and sending a response. Another reads load tenders and attachments before converting the information into orders.
Uber Freight has also embedded AI agents into its transportation management software. In May 2025, the company said it had more than 30 agents automating work across procurement, shipment execution, tracking, payments, and analytics.
Uber Freight said it wants its TMS to move beyond recording logistics information by automating repetitive operational work and guiding users through transportation processes. The company described its longer-term goal as moving the TMS beyond a system of record and toward a platform that can proactively guide users while automating repetitive tasks.
Alvys is applying that execution model within its own TMS rather than through a separate automation layer. Customers can start with pre-built agents, have Alvys engineers configure custom agents, or build their own workflows on the Foundry platform.
Spartan Carrier Group is among Foundry’s early customers Founder and CEO Carlos M. Llanes Jr. said the platform is being used to reduce manual freight work while employees remain focused on judgment and service.
The launch follows Alvys’ $40 million Series B funding round in September 2025, led by RTP Global. FreightWaves reported that the company has raised $77 million in total, while Alvys says its platform handles more than $9 billion in freight annually.
Foundry is being introduced through customer cohorts rather than an unrestricted general rollout. FreightWaves reported that the first cohort filled after Alvys presented the platform at a June 17 customer advisory board meeting, while a waitlist is open for the second cohort.
(Photo by Barrett Ward)
See also: Hershey applies AI across its supply chain operations
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
The post Alvys launches AI agents for freight TMS workflows appeared first on AI News.