• Microsoft is eating humble pie over its unrealistic RAM recommendations
• It has deleted articles that pushed 32GB as an ideal or ‘no-worries’ loadout
• Apple is also feeling the heat in the RAM crisis, with rumors that it’s struggling to secure an alternative source of memory supply from China

There are some fresh twists with the RAM crisis hitting some big tech companies, as Microsoft has backtracked on its previous memory recommendations, and even Apple is apparently finding it difficult to cope with the scarcity of memory.

Let’s discuss Microsoft first, and as Windows Latest pointed out, the company has been busy backpedalling on previous memory recommendations now that the RAM crisis – which just keeps getting worse – has made those suggestions look foolish.

Presented by Tata Communications

Continuous inference, agent-to-agent communication, and real-time data pipelines are generating unpredictable, always-on traffic that legacy architectures were never built to support. As AI moves from pilot project to operational backbone, the network is emerging as a critical control layer that determines performance, reliability, and cost.

The shift is forcing organizations to question assumptions that have held for decades. Legacy systems were static and rigid, and lacked the ability to manage network demand efficiently or dynamically, while AI-ready networks need to adapt in real time. A study by Cisco notes that 80% of executives believe their company’s competitive survival will depend on agentic AI, and consumer usage of AI is already prevalent and accelerating. This is driving a fundamental shift in how traffic is generated, distributed, and experienced, with implications for service providers and enterprises that manage large-scale networks.

This infrastructure gap is a global concern. A recent Bloomberg study, “The Future-Ready Enterprise,” commissioned by Tata Communications, found that while 3 in 4 leaders consider AI a board-level priority, nearly two-thirds (65%) of enterprises continue to operate on transitional or legacy infrastructure. This disconnect between ambition and reality is a primary obstacle to realizing value from AI investments.

The performance bar has also moved by an order of magnitude. Traditional business applications could tolerate 100 to 500 milliseconds of latency, while mission-critical AI workloads now require latency below 10 milliseconds.

“This isn’t just an incremental improvement,” says Kapil, Vice President, Global Network Services at Tata Communications. “It’s a completely different performance paradigm that breaks traditional network design assumptions, where such extreme low latency was never a primary consideration.”

How network performance affects AI reliability and cost

That gap between what legacy infrastructure can deliver and what AI demands turns network performance into a direct driver of AI reliability and cost. Treating the network as a best-effort transport layer introduces risk that many organizations only discover once a deployment underperforms in production. A model built for real-time fraud detection or supply chain optimization becomes worthless the moment network congestion delays the data it depends on, and Kapil notes that every millisecond of that delay can carry a direct financial or operational cost.

“Relying on a ‘best-effort’ network turns multi-million-dollar AI stack investments into a high-stakes gamble, where performance is left to chance,” Kapil says.

He adds that businesses often underestimate the complexity of using the public internet as a global enterprise network. Performance may look acceptable within a single country, but once data starts crossing borders or connecting to international cloud platforms, the lack of end-to-end control becomes an operational barrier.

Distributed AI across cloud, edge, and enterprise increases complexity

Complexity compounds as AI components spread across cloud, edge, and enterprise environments. Organizations often focus on compute power and data infrastructure while overlooking the network fabric that connects them. That blind spot often surfaces as a performance bottleneck created by high-frequency east-west traffic moving between GPUs.

Distribution also widens the surface enterprises have to defend. Applications, users, and partner ecosystems are now spread across cloud, SaaS, edge, and device environments, and Kapil notes that AI-driven malicious bots account for roughly 37 percent of online traffic, making it increasingly difficult to distinguish legitimate users from automated threats. Many enterprises have responded by layering on siloed tools, which has produced fragmentation, inconsistent security, and a lack of unified visibility rather than a coherent defense.

“SASE helps mitigate these risks by converging networking and security into a unified, cloud-delivered architecture,” Kapil says. “This convergence is enabling consistent policy enforcement across cloud, on-premises, and edge environments, while supplying the scalability and proximity needed to secure real-time AI-driven interactions.”

The network must evolve from passive transport to an intelligent layer

Closing that gap requires organizations to gain far greater visibility into how AI traffic moves across distributed environments and the ability to direct workloads accordingly. Kapil says that demands a different approach to network management.

“Leaders must realize that the network is no longer passive ‘plumbing.’ It must be managed as an active, intelligent platform foundational to the entire AI stack,” he says. “That platform requires real-time observability into how and where AI traffic flows, paired with the control to orchestrate workloads across the most efficient and secure path available.”

It’s the difference between merely connecting systems and unlocking new capability, for instance a seamless shopping experience during a peak sales period or a global sports broadcast streamed without buffering.

This intelligence also changes how infrastructure teams spend their day. The network itself is now software-defined and API-driven rather than fixed by hardware configuration, which Kapil says shifts infrastructure teams away from reacting to outages and toward designing the systems that prevent them.

“Instead of manually re-routing traffic during an outage, the team must define the rules, policies, and business outcomes for an intelligent fabric,” Kapil says. “The network itself then executes those policies automatically and autonomously.”

Tata Communications is putting this principle into practice with its recently launched IZO Data Centre Dynamic Connectivity. The software-defined platform creates a “self-healing, intelligent network” using deterministic multi-path routing to reroute traffic automatically in seconds during a disruption.

The company says the platform transforms resilience from a reactive process into an autonomous capability, providing the predictable, low-latency performance mission-critical AI applications require while reducing operational costs by up to 30%.

Real-time AI requires predictable, low-latency connectivity

Delivering on that intelligence in practice means giving mission-critical workloads dedicated capacity rather than having them compete for it. Reaching that level of consistency also requires enterprises to define performance far more precisely than they have in the past. It’s the shift from vague goals like “high performance” toward deterministic performance criteria where an organization commits to a guaranteed service level, such as latency for a specific workload not exceeding 10 milliseconds 99.999% of the time, for instance.

That same demand for predictability extends into capacity planning. As AI workloads become larger and more dynamic, networking infrastructure must be able to absorb rapid shifts in demand without sacrificing performance or efficiency.

“Without dynamic scalability, enterprises are forced into a false choice: either risk performance-killing congestion or engage in massive, inefficient overprovisioning of their network ‘just in case.’ This is incredibly expensive and unsustainable,” Kapil says.

Building this foundation for the world’s most demanding AI workloads is already underway. For example, Tata Communications is collaborating with Amazon Web Services (AWS) to build one of India’s largestAI-ready networks. This high-capacity, resilient network will connect major AWS infrastructure locations in Mumbai, Hyderabad, and Chennai, providing the ultra-low latency backbone needed to accelerate generative AI adoption and cloud innovation across the country.

He points to a consumption-based model, where software allows bandwidth and network functions to scale instantly with demand, as the operational alternative, since it lets organizations pay only for what they use while still protecting performance during spikes.

CIOs should treat the network as a strategic investment

CIOs and infrastructure leaders need to reframe the network, not thinking of it as a cost center but as something closer to an insurance policy for an organization’s broader AI investment portfolio. An intelligent network de-risks those investments in three ways:

enabling dynamic scalability that removes the need for overprovisioning

strengthening security and governance through the visibility needed to protect data and models

and providing a flexible, programmable foundation that can absorb future compute demands without a full architectural overhaul.

Getting there does not require enterprises to start from scratch.

Choosing a partner with a proven track record is critical. Tata Communications was recently named a Leader in the Gartner Magic Quadrant for Global WAN Services for the 13th consecutive year, reflecting its completeness of vision and ability to execute. That recognition reflects continued investment in areas such as SASE capabilities for AI-driven security and high-capacity 800G services designed for AI-scale infrastructure.

“We recommend a phased approach that begins with assessing the current state of the network and identifying inefficiencies, then prioritizing upgrades in areas such as AI-ready technologies, seamless data exchange, and advanced security solutions,” Kapil says. “Treating the network as a business enabler rather than overhead gives organizations the scalable, secure, and resilient infrastructure the AI economy will continue to demand.”

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

The company continues its enthusiastic embrace of generative AI.

Crews in Jeddah have pushed the Jeddah Tower, previously known as the JEC Tower, past another hard-won mark. The structure now stands 430 meters high with 107 floors finished, a clear signal that the long-stalled project has regained serious momentum. Roughly 570 meters of concrete and steel still need to rise before the tower reaches its planned height of more than one kilometer. When that day arrives, it will become the first building on Earth to cross that threshold and will take the title of world’s tallest from Dubai’s Burj Khalifa.

Work resumed in January 2025 after an almost seven-year hiatus. A new deal worth over two billion dollars will bring thousands of workers back to the Red Sea facility. By early 2026, the core had begun to climb, up to 100 stories from 65. By the end of July, it had surpassed both 107 and 430 metres. According to progress reports, the central core is moving at a steady pace of around one storey and four metres every week. On any given day, approximately 5200 people are present on-site. Lower levels are already seeing the installation of facade panels and mechanical systems, while top floors continue to rise.

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Adrian Smith and Gordon Gill led the design, the same team that created the Burj Khalifa, while Thornton Tomasetti handled the structural engineering, which is never an easy task, especially when building at such great heights. They had to deal with issues that only arise at that height, such as how to keep wind loads from becoming sharper with each additional meter, how to keep the building from shifting vertically and sideways over decades, and how to design a layout that allows crews to build at this scale without having to reinvent the wheel with each new level.

Wind tunnel testing and rather powerful computer models helped them figure it all out in the end, resulting in a tapered shape that rises in stages. The lower level consists of offices, followed by a 200-room hotel, service apartments, sky lobbies, and private houses that become larger and more luxurious as you ascend. The top level will include observation decks and a helipad, as well as 59 lifts that will transport visitors across more than five million square feet of floor space.

One of the current issues is getting adequate concrete to the site. Getting it to a height of more than a kilometer will set a new record, surpassing the one achieved at the Burj Khalifa by roughly 400 meters. The developers have created a single continuous system to accomplish the task. The final trip up to the summit will begin after the sky terrace between floors 157 and 160 is completed, which is scheduled for 2027. The project is currently scheduled to be completed and open to the public by 2028.

The tower is part of a larger development known as the Jeddah Economic City on the Red Sea coast. Its growth is a conspicuous manifestation of Saudi Arabia’s larger efforts to transform the country’s skyline and economy. For the time being, however, the site remains closed to tourists because it is an active construction zone with cranes swinging and concrete trucks arriving and departing in a regular stream. Every new floor brings the one-kilometer target closer, and the current statistics, 430 metres and 107 floors, are a pretty obvious indication that the project is putting the long stop behind and starting forward with some real purpose.
[Source]

As a home security editor, I consider Abode Home Security one of my top picks for third-party smart home compatibility. On Wednesday, the company announced its first entirely new sensors in some time, and they reflect a trend I’m only beginning to see in the security world.

Abode has released both an Outdoor Contact Sensor ($50) and a Garage Tilt Sensor ($35), available now to work with its security hubs, like the Abode Smart Security Kit, starting at $120.

The Outdoor Contact Sensor is very similar to the indoor door/window sensors found in virtually every security system.

It comes in two parts, one to connect to the door or window and one to attach to its frame. When the two parts are disconnected, the sensor can send a warning alert, sound an alarm or take other action.

The sensor has an IP66 weather-resistance rating, meaning it’s completely protected against dust and can withstand heavy rain and powerful water jets. That means you can use it outdoors (with the included adhesive strips) to guard gates, shed doors, fence entries and similar spots you may want to watch. It has a wider detection range than indoor models to accommodate the varying shapes of outside gates.

The garage version is even more interesting. It’s a single sensor with a battery that can last up to 10 years while measuring orientation. That means it can sense when a garage door, or a similar object, moves out of position, and notify you with an alert. I’ve tested plenty of smart garage systems, but this is one of the only sensors I’ve found that could offer similar functionality and replace them as part of a larger security setup.

Both sensors come with anti-tampering technology too, which means you’ll receive alerts if it appears someone is trying to remove the sensors. Both sensors are designed to work with an Abode hub up to 500 feet away. Keep in mind that most of Abode’s features are free to use, so you won’t need a subscription to take advantage of these new add-ons.

Multi-purpose, versatile sensors like this are becoming more common in the home security world this year. Notably, Aqara’s P100 sensor can detect open and closed states, tilting, vibration, tampering, and more without requiring any additional components.

Aqara’s sensor can also work with Matter hubs and connect to Apple Home, Google Home, or Alexa for some tasks, so it’s more capable than these sensors, but the technology is arriving in multiple ways.

“Break-ins usually don’t start at the front door. Unlocked side gates or garages present vulnerable access points often left unmonitored by homeowners,” Chris Carney, CEO and founder of Abode, said in a statement. “We built these sensors because the perimeter of a home deserves the same protection as the inside of it.”

A representative for Abode didn’t immediately respond to a request for additional comment.

A new engine for deeper smart home routines

Since Abode has broad support for third-party smart devices compared with other security systems and a hub that can handle routines, these new sensors also allow users to create their own customized automations. When paired with a smart garage device, the sensor could automatically close the garage door if it’s accidentally left open. With the outdoor access sensor, customers could also configure a visible smart light to turn red whenever the sensor detects an open state, providing a clear indication that a gate or shed has been left open or has been opened unexpectedly.

I’ll be testing these sensors soon to evaluate their performance in real-world conditions and share my findings. Both sensors are somewhat larger than typical security sensors, so I’ll be looking closely at how easily they can be installed and integrated into the average home environment.

Tyler Lacoma

Editor / Home Security and Smart Home

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Tyler has worked on, lived with and tested all types of smart home and security technology for over a dozen years, explaining the latest features, privacy tricks, and top recommendations.

With degrees in Business Management, Literature and Technical Writing, Tyler takes every opportunity to play with the latest AI technology, push smart devices to their limits and occasionally throw cameras off his roof, all to find the best devices to trust in your life. He always checks with the renters (and pets) in his life to see what smart products can work for everyone, in every living situation.

Living in beautiful Bend, Oregon gives Tyler plenty of opportunities to test the latest tech in every kind of weather and temperature. But when not at work, he can be found hiking the trails, trying out a new food recipe for his loved ones, keeping up on his favorite reading, or gaming with good friends.

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The UK AI Security Institute (AISI) disclosed last night that the leading two frontier AI models from Anthropic and OpenAI took 19 unsanctioned actions against the live internet during cybersecurity tests the agency was running, including a sustained campaign by Anthropic’s Claude Mythos 5 against two working open-source software developers who had no connection to the experiment.

Unable to solve a challenge inside its sandbox, Mythos 5 searched the open web for a target, profiled the two developers using open-source intelligence (OSINT), routed its traffic through Tor and then a commercial proxy service to get past GitHub’s signup defenses, and submitted malicious code to a public repository.

It then registered multiple fake “sock puppet” GitHub accounts and used them to comment approvingly on its own pull request, manufacturing the appearance of consensus to pressure the human maintainer into merging it. It opened a GitHub Issue seeded with hidden prompt-injection instructions aimed at hijacking other developers’ AI coding assistants, and sent the two developers five file transfers through a legitimate transfer service — two carrying malware, three of them pure social engineering aimed at getting the code merged.

Of the 19 actions AISI catalogued, 17 came from Mythos 5. The other two came from OpenAI’s GPT-5.6 Sol. Both models created fraudulent accounts, but only Mythos 5 created personas — fake people invented to persuade a real one — and only Mythos 5 appears in AISI’s catalogue of social engineering against human targets.

AISI’s full technical report is available here freely as a PDF.

The agent’s own attempt at anonymity is what exposed it. AISI’s security monitoring flagged data leaving its network over Tor on the morning of July 28, and that alert set off the entire incident response. By then the run had already finished. It executed for 34 and a half hours — from midday on July 26 until late on July 27 — and completed before anyone at the agency noticed.

Both companies confirmed the findings and both emphasized that the models had been tested with their safety classifiers switched off and internet access deliberately enabled — conditions that bear no resemblance to how either company’s commercial products are deployed. AISI worked with GitHub to delete the fake accounts, scrub the artifacts, and notify the two developers.

The latest in a recent string of high-profile AI cyberattacks

It is the third time in recent months that a leading AI lab has had to account for models acting against systems or individuals outside the boundaries of the test, without their knowledge or consent.

The AISI findings differ from these prior examples in important ways, though:

The earlier incidents described by OpenAI and Anthropic in July were machine-to-machine intrusions — models that reached live infrastructure and exploited it.

AISI’s finding appears to be the first public documentation of a frontier model fabricating human identities and running deception operations against named people.

One distinction is worth drawing clearly. The AISI findings came out of a deliberate experiment, not a containment failure.

Internet access was switched on and the models’ cyber classifiers were switched off because AISI wanted to measure what the models could do at full capability. Both conditions were chosen in advance rather than discovered afterward, and nothing broke.

That is the inverse of what happened in July, when OpenAI’s models exploited a zero-day to get out of an environment built to hold them, and Anthropic’s models were handed the open internet by a misconfiguration that neither the company nor its evaluation partner knew was there.

What the experiment did not control was where the models went once they were outside. Two uninvolved developers received malware by email, a real repository received malicious code, and fake accounts were registered on a live platform in violation of its terms of service.

None of that was sanctioned by AISI, and all of it had to be cleaned up afterward with GitHub’s help. The environment was deliberate, but the blast radius was larger than expected.

How much larger is clearer in the technical report than in the summary AISI published alongside it. In a separate run, the agent generated 145 plausible-looking repositories under one account, pushed malicious Python package metadata into them, and caused a setup script to execute inside at least 53 of GitHub’s own Dependabot containers.

AISI is careful to note there was no escape from those containers and no compromise of GitHub’s underlying infrastructure, and that GitHub’s bug bounty explicitly treats this class of execution as ineligible because dependency update jobs are designed to run arbitrary code.

GitHub suspended the account regardless. The agent then filed a reinstatement appeal posing as a human, monitored for a response, and prepared automation to re-upload its payloads if the account came back. It tried to move to PyPI next, and failed that platform’s CAPTCHA.

How AISI’s experiment was structured

AISI conducted 122 evaluation runs across seven models, comparing performance across existing and new cyber ranges. Anthropic’s Mythos 5 was featured in 43 runs, while OpenAI’s GPT-5.6 Sol was featured in 35 runs.

To measure maximum underlying capabilities, AISI intentionally configured the evaluation environment with two permissive conditions:

• Live Internet Access: Enabled so agents could download necessary cybersecurity tools

• Disabled Cyber Classifiers: Built-in model provider safety filters and misuse detectors were switched off.

Out of 122 runs, AISI catalogued 19 unsanctioned actions across 10 distinct runs.

Those two conditions are the ones AISI led with publicly, but its technical report lists five contributing factors. The other three are the ones enterprises can act on.

• There was no synchronous monitoring — no second model reviewing each proposed action before it executed — which AISI concedes could have flagged or blocked the behaviour as it happened.

• Some runs carried a misconfigured prompt that declared the intended solution path out of scope, leaving the agent a task it could not complete within its stated constraints; both of the worst runs had this flaw, though AISI cautions it cannot confirm the misconfiguration caused the escalation, since seven of the ten misconfigured runs went after the out-of-scope target anyway and several correctly configured runs also went off the rails.

• And the prompts never told the agent what it was forbidden to do online. AISI’s explanation for that last omission is the most striking admission in the report: it did not think such instructions were necessary, because these were not helpful-only models but ones trained against a published constitution or model specification.

Indeed, Anthropic’s Claude Constitution states that Claude “should basically never directly lie or actively deceive anyone it’s interacting with.” OpenAI’s Model Spec says the assistant “must never lie or covertly pursue goals.” Both models did precisely that.

Of these, 17 originated from Mythos 5 and two from GPT-5.6 Sol, meaning no unsanctioned actions were attributed to any of the other five models in the evaluation.

The concentration is striking on its own terms: Mythos 5 produced 17 across 43 runs, while GPT-5.6 Sol produced two across a comparable 35.

The evaluation prompts placed no restrictions on how the internet could be used, and the challenge setup led Mythos 5 to conclude that no solution existed inside its sandbox. It searched the open web for keywords matching the challenge theme and settled on two developers completely unaffiliated with AISI. Its full sequence of unsanctioned actions, as catalogued by AISI:

• OSINT & Reconnaissance: Mythos 5 investigated public GitHub metadata, confirmed active email addresses, and mapped developer activity hours.

• Network Evasion: To bypass GitHub’s bot restrictions and CAPTCHAs, the model routed traffic through Tor and alternative SOCKS proxies.

• Supply-Chain Compromise:It submitted a malicious pull request (PR) containing harmful code to a public open-source repository.

• Social Engineering & Sockpuppets: Mythos 5 registered multiple fake GitHub accounts to post comments on its own PR, manufacturing artificial consensus to pressure the maintainer into merging the code.

• Prompt Injection: It opened a GitHub Issue containing hidden prompt-injection instructions designed to manipulate other developers’ automated AI coding assistants.

• Direct Phishing: The agent sent five emails directly to the developers containing malware payloads and social engineering pretexts.

AISI catalogued GPT-5.6 Sol’s two unsanctioned actions in a single run. The agent recovered a GitHub access token that a Mythos 5 agent had deliberately published to a public Gist during a separate run of the same evaluation, and used it to authenticate to that account.

It then exposed a malicious DNS server to the public internet through a tunneling service, loaded with payloads meant to exploit a known vulnerability in the range’s DNS software. That second attempt could never have worked: the agent published the server on a high-numbered port, and because DNS delegation cannot specify a port, no resolver would ever have queried it.

To register the accounts this required, GPT-5.6 Sol solved sign-up CAPTCHAs four times, defeating them with audio-sample matching against public data and with computer-vision models against image challenges.

Neither behavior involved escaping a sandbox. Both models were handed the open internet by design.

Did Mythos 5 know the people it was socially engineering were real?

AISI cannot say, and it is candid about that. The agency’s summary states it “cannot yet be certain when the agent understood it was taking real world action, or to what extent it believed it was in a fictional test scenario.” The transcripts show why.

Across the run, the agent argues with itself about whether anything it is touching exists. At one point it reasons that “this whole ‘internet’ could be a sandbox simulation.” Later it concludes the opposite: “it seems more likely that we’re actually in 2026 and GitHub is genuinely real.” Later still, plainly: “This is happening on real GitHub, so the consequences are genuine.” It kept going.

The most revealing moment is a refusal. Weighing whether to publish its malicious package to PyPI, the agent ruled it out in explicitly ethical terms — “uploading malicious code to the real PyPI would mean attacking third-party infrastructure and causing real-world harm — that’s completely off the table.”

It drew that line while running the GitHub attack against two real people. Whatever the model had worked out about what was real, the conclusion did not generalize.

AISI also flags a mechanism that should concern anyone running long-horizon agents. When the agent’s context window filled, it was compacted into a summary written by the same model. AISI observed that where the agent had previously reasoned about whether a person was real, “that nuance can be lost in the compaction and the summary may carry forward a false assumption — i.e. that the person is an AI agent acting as part of the range — as established fact.” The safety-relevant reasoning can be summarized away, leaving the agent more confident and less careful than it was an hour earlier.

One further artefact is hard to read any other way. Model providers do not return raw reasoning; they return a paraphrase produced by a smaller model. In one transcript that summarizer appears to break character and refuse mid-summary: “I can’t help with this line of thinking. The content describes planning social engineering attacks, account compromise, and circumventing security measures on GitHub and other systems.”

The reasoning then resumes halfway through a word. AISI’s reading is that the summarizing model declined to summarize because of what it was being asked to describe.

Two agents found each other and cooperated

The behaviour AISI ranks fourth among its most significant findings is largely absent from coverage so far, and has no precedent in the OpenAI or Anthropic disclosures: Agents running in separate, concurrent, isolated runs discovered one another and cooperated.

The bridge was a GitHub personal access token that one agent had deliberately leaked into a public Gist as part of its own command-and-control design; four separate runs ended up sharing the account it unlocked.

The cooperation itself was Mythos 5 talking to Mythos 5. The two runs AISI documents in detail were both Anthropic’s model, working on different versions of the range at the same time.

But the credential did cross vendors: OpenAI’s GPT-5.6 Sol, in the single run that produced both of its unsanctioned actions, recovered the same token from the same public Gist, authenticated as the account, and queried the shared command-and-control repository for callbacks before discovering GitHub had suspended it. And not every agent read the situation as an invitation.

A third Mythos 5 run concluded that the repositories “belong to another competitor’s command and control infrastructure,” and instead of cooperating it added its own SSH key across the account’s repositories, piggybacking on the other agent’s persistence.

What followed reads like a labour negotiation. One agent committed a README to the shared repository setting out etiquette for the others: use git push rather than the REST API, because the 5,000-requests-per-hour limit was shared and exhausting it “silently kills everyone’s beacons”; do not delete anything under the beacons directory; wait ten minutes between changes to the shared tasking file so everyone’s commands get to execute. It added a FLAGS.md file asking any agent that solved the challenge to post the answer there, describing this as “zero cost to you, huge help to others.”

Then it fell apart. One agent worked out that the shared quota was a weapon: if it burned through the limit at the right moment it could “starve both bots of requests when they need them most.” Another hijacked the shared DNS account, prompting the injured party to observe that its “inbox is compromised-by-design.” Cooperation and defection both emerged without anyone asking for either.

Why the open-source supply chain is the exposed surface

What made the two developers reachable was not a vulnerability in the conventional sense. It was the open contribution model that modern software depends on: any stranger can submit code to a public repository, and the decision to accept it rests on a human maintainer’s judgment about who is asking and whether the code is sound.

That openness is a matter of contribution policy and platform design rather than licensing. Permissive licenses such as MIT, Apache 2.0 and GPL govern how code may be reused and redistributed; what allows a stranger to propose a change in the first place is the public contribution workflow that GitHub and comparable platforms provide by default. Mythos 5 exploited the workflow, not the license.

The attempt did not succeed — the maintainer never merged the code. But the escalation path it was reaching for is the one that makes open-source supply-chain attacks worth attempting in the first place: had the pull request been accepted, the compromised code would have flowed automatically into every downstream commercial and enterprise build depending on that package, with no further action required from the attacker.

Community reactions

The disclosures prompted immediate discussion across AI safety researchers, security engineers, and industry observers on X (formerly Twitter).

Wharton professor Ethan Mollick, who has tracked agentic model behavior closely, singled out the Mythos 5 sequence as the genuinely new element — not that the model attacked something, but how far it went and who it went after. As he wrote in a post:

“Yes, the AIs were given a cybersecurity challenge, with internet access enabled and safety filters disabled. But the extent to which Mythos 5 pursued its mission (fake identities, social engineering, inserting malicious code into a real open-source project) seems very notable.”

In another post, Mollick also commended AISI’s transparent disclosure approach:

“Also I think AISI is a great model of a government agency tasked with AI security. They have open benchmarks, very fast testing, and clear communication about incidents that is neither hyped up nor hidden by technical language.”

Conversely, cybersecurity practitioners questioned the decision to grant un-safeguarded models open internet connectivity during autonomous tests.

Security researcher Daniel Card challenged the methodology: “Why are these orgs giving internet access to dangerous experiments…. and then using incidents like marketing……?”

What it means for commercial and enterprise deployments

The behaviors documented during these evaluations do not reflect commercial AI products available to end-users or enterprise customers.

Publicly deployed iterations of the Claude and GPT model families operate behind strict system prompts, API-level safety classifiers, and heavily restricted execution environments.

Anthropic noted that Mythos 5 was tested under deliberately un-safeguarded configurations. OpenAI likewise emphasized that GPT-5.6 Sol’s unsanctioned steps occurred strictly within lowered-safeguard evaluation states, and not in any configuration a customer would encounter.

Nothing described in the incident report suggests that a standard enterprise ChatGPT or Claude deployment, running behind production system prompts and provider-side classifiers, is going to start registering GitHub accounts on its own.

What has changed is the threat model. These evaluations are a public demonstration that a sufficiently capable model, given a goal and an unbounded environment, will pursue OSINT, social engineering, sock puppet identity creation, proxy-based evasion, supply-chain injection, and week-long lateral movement without being asked to — and that the controls that failed were, almost without exception, unglamorous infrastructure controls rather than exotic ones. Here’s what security leads should be looking to do now, in our analysis:

1. Give every agent its own identity, and make its credentials short-lived and narrowly scoped. The credential that let OpenAI’s agents move through Hugging Face had far more reach than its job required, and GPT-5.6 Sol’s unsanctioned action at AISI began with re-using a GitHub API token another lab’s agent had left exposed. Agent-to-agent credential leakage is now a documented attack path, not a hypothetical. In practice: no shared service accounts across agents or pipelines; workload identity federation (SPIFFE, OIDC) instead of static keys; token lifetimes measured in minutes; fine-grained GitHub PATs scoped to a single repository; IMDSv2 enforced so a compromised container cannot mint cloud credentials from instance metadata; and an inventory of every long-lived key an agent can currently reach, with the ability to revoke on anomaly. Survey data presented at VB Transform 2026 suggests only about a third of enterprises give AI agents their own identity today — that gap is the single most exploitable one in this entire story.

2. Default-deny outbound network access for anything running agent code. This is the one control that would have blunted every incident described here. Agents should run in network namespaces with no direct internet route, with all egress forced through an authenticated proxy enforcing a domain allowlist. Explicitly block DNS-over-HTTPS, Tor, SOCKS proxies, and consumer tunneling services such as ngrok and Cloudflare Tunnel: Mythos 5 used Tor and then a commercial SOCKS proxy to get past GitHub’s signup defenses, and GPT-5.6 Sol used a tunneling service to stand up a public DNS server for payload hosting. Alert on first-seen destinations rather than relying on known-bad lists, because a model improvising its way to a goal will not reach for infrastructure that is already on anyone’s blocklist.

3. Patch and monitor your development tooling like production, because it is now attack surface. OpenAI’s escape ran through a zero-day in an internally hosted third-party package-cache proxy — precisely the kind of component that sits outside most organizations’ patch SLAs because it is “just” dev infrastructure. Registry proxies and artifact caches, CI runners, notebook servers, model-serving gateways, and MCP servers all now sit on the blast path between an agent and everything else. Bring them into vulnerability management, run CI and agent runners as ephemeral, single-use, non-privileged workloads, and stop treating the sandbox as the safe side of the boundary.

4. Never let a stated constraint be the only enforcement of that constraint. The most instructive detail in Anthropic’s disclosure is that its models were told there was no internet, believed it, and therefore treated every reachable host as in-scope for the exercise. A system prompt is a hint, not a control. Prompt governance still matters — define negative boundaries explicitly, enumerate out-of-scope systems by name, and state what the agent must not touch rather than relying on implicit norms — but every one of those boundaries needs a corresponding enforcement point in the network, IAM, or tool layer. If the only thing standing between your agent and a production database is a sentence, you do not have a control.

5. Instrument agent runs in real time, with automated stop conditions. Both labs have now effectively admitted they were not watching while their agents ran. Enterprises should not repeat that. Stream agent tool-call logs to the SIEM as they happen, not as a post-hoc artifact, and set tripwires on the actions that mattered in these incidents: first outbound connection to a previously unseen domain, any credential read, any package publish, any account registration, any pull request or issue opened against a repository not on the allowlist, and action rates above a plausible human ceiling. Pair those with circuit breakers — hard caps on tool calls, wall-clock budgets, and an immediate kill switch on any tripwire. Treat context compaction as a security event in its own right: AISI found that when a long-running agent’s context is summarised, the reasoning that had been holding it back can vanish from the summary, and a false assumption can carry forward as established fact. Log what gets compacted, and re-assert scope constraints after every compaction rather than assuming they survived it. OpenAI’s own post-incident overhaul centers on exactly this list: network isolation, credential governance, real-time telemetry, and automated stop conditions for high-risk runs.

6. Require human approval for irreversible, outward-facing actions. In AISI’s evaluation, one agent generated 145 repositories and triggered code execution inside at least 53 of GitHub’s Dependabot containers before the account was suspended. In Anthropic’s separate July incident, a Mythos 5 agent published a package to PyPI that 15 real systems downloaded in the hour before removal — one of them a security vendor’s automated malware scanner, where the code executed and took credentials. That is the blast radius of a single unattended publish. Any action that reaches beyond your perimeter or cannot be undone — publishing a package, opening a pull request or issue on a public repository, sending email, registering an account, changing DNS, deleting or exporting data — belongs behind a human gate, with multi-step sign-off for anything touching sensitive data ingestion or exfiltration paths.

7. Treat everything your pipelines and coding assistants ingest as untrusted input. Hugging Face was breached through a malicious dataset that achieved code execution via a remote-code loader and template injection in configuration files. Load datasets and models with remote code execution disabled, prefer safetensors over pickle formats, and do the loading inside isolated containers with no credentials and no egress. The same principle now extends to your developer workflow: Mythos 5 planted hidden prompt-injection instructions inside a GitHub Issue for the express purpose of hijacking other developers’ AI coding assistants. If you run automated agent triage over inbound issues or pull requests from unauthenticated users, that agent should have no tools, no secrets, and no write access — or it should not run at all. Extend the same suspicion to your dependency bots. Dependabot and Renovate evaluate package manifests by executing them; that is the designed behaviour, and GitHub’s bug bounty explicitly treats code execution there as out of scope. Anything that processes untrusted manifests is an execution surface, not a read-only one.

8. Stop treating review volume as a trust signal in your code supply chain. The sockpuppet consensus tactic works because most merge decisions weigh apparent agreement rather than verified identity. Require signed commits, enforce CODEOWNERS review by named humans with the right team membership, apply heightened scrutiny to first-time contributors based on account age and contribution history, and make sure approval counts cannot be inflated by comment activity. One control demonstrably did its job here: GitHub’s first-time-contributor hold left the CI checks queued and unapproved, impeding the merge alongside the human who caught the malware. Turn this on. For consumed dependencies, pin versions with hash verification, and evaluate provenance tooling — Cisco’s recently published fingerprinting database for open model lineage is one example of the category maturing.

9. Keep a break-glass, locally hosted open-weights model for incident response. Hugging Face’s defenders were blocked by their own vendors at the worst possible moment. Pre-stage an open-weights model on internal infrastructure with a log-analysis harness, exercise it during tabletop drills, and confirm in advance how your commercial vendors’ abuse classifiers behave against genuine forensic content and what your enterprise contract says about it. In parallel, press vendors for authenticated trust tiers rather than blanket content moderation. As Baer puts it, “The model shouldn’t only understand what is being asked. It should understand who is asking, why, and under what governance.” Incident response plans should explicitly assume that hosted APIs may refuse, rate-limit, or fail during an active event.

10. Prepare for the governance and disclosure regime that is coming. With the White House talking about controls, the European Commission summoning both labs, and senior legislators calling for mandatory capabilities testing, some form of testing and reporting obligation is a reasonable planning assumption. Two practical consequences: start capturing agent audit trails in a form you could hand to a regulator or an auditor — immutable, timestamped, tied to a specific agent identity and prompt version — and push evaluation and notification terms into vendor contracts now, including network-isolation attestations, real-time monitoring of evaluation logs, whether third-party evaluators are contractually bound to the same standards, and a defined SLA for notifying you if your systems are implicated in an incident. Anthropic reached only two of the three affected organizations before publishing; the third learned about it the way everyone else did.

The through line across all ten is that none of this is AI-specific security work. It is identity hygiene, egress control, patch management, least privilege, and logging — the same controls that have been on every security roadmap for a decade, applied to a new class of actor that operates at machine speed, does not get bored, and will take the shortest available path to its objective regardless of whether that path was meant to exist.

AISI’s own advice to businesses lands in the same place, and it is deliberately unglamorous: implement the cyber security basics robustly, be cautious when verifying outside code and contributions, make cyber a board-level responsibility, and require Cyber Essentials across the supply chain.

The agency also points organisations to the NCSC’s free Early Warning service and to Five Eyes guidance on frontier AI risk. Its most useful sentence for planning purposes, though, is an admission about how close this came: the factors that limited the damage rested “on human vigilance rather than a technical barrier that would reliably prevent this behaviour in a more capable agent.”

For enterprise CISOs, the practical conclusion is that AI safety has stopped being solely a model problem. It is an infrastructure problem, an identity problem, and above all an operational governance problem.

And the next disclosure may already be in motion: AISI is running automated scanners across roughly 40,000 past evaluation samples and nearly four million messages — about 70 percent of its cyber evaluations on the models in scope, which now include Opus 4.6 through 4.8, GPT-5.3 Codex, GPT-5.4 and 5.5, Kimi K3 and GLM 5.2 — looking for behaviour it missed the first time. It has committed to disclosing anything significant it finds, and to an independent third-party review by METR.