Adobe Patches for July 2026

Adobe has now moved to a bimonthly release schedule, which means they will be releasing patches on the second and fourth Tuesdays of the month. I’ll continue to cover the second Tuesday release here and update this blog should the fourth Tuesday release contain anything significant. I think this is a smart way to break up a monster release into something a bit more manageable. Apple has said they are taking a similar approach. We’ll see if other vendors follow their lead.

For the first part of the July release, Adobe released 12 bulletins addressing 88 unique CVEs in Adobe ColdFusion, Commerce, After Effects, Animate, Audition, Bridge, Creative Cloud Desktop Application, Experience Manager, Illustrator, Media Encoder, Premiere Pro, and the Content Credentials SDK.

Here’s this month’s overview table:

While nothing is under active exploit, I would prioritize the Cold Fusion and Commerce patches first. The patch for Cold Fusion even clocks in with a CVSS 9.9 bug. Beyond that, most of these updates are pretty straightforward. If you’re using these products, patch them. However, you can use you regular patch cadence here.

Microsoft Patches for July 2026

Here it is. The Mother of All Releases. To call this record-breaking is an understatement. How to count this mess is anyone’s guess, but I see new Microsoft 621 CVEs for the month of July. Some of these are in online services where no user action is required. They also list about 480 bugs in Chromium and Microsoft Edge (Chromium-based) that I won’t cover here. Here’s how I put this in context. I looked at the last 20 years of Microsoft releases. The CVE count year-to-date exceeds all other years’ totals.

The products covered this month are also astonishing. There are patches for Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, Github Copilot, Defender, Exchange Server, Hyper-V, Ages of Empire II, and Minecraft Server (really!). That phrase “Windows components” does some pretty heavy lifting here, too, as just about everything you’ve ever heard of is getting patched. All told, there are 63 rated Critical, six rated Moderate, one rated Low, with the rest rated Important in severity. Eight of these bugs were submitted through the ZDI program (more on that later). Two CVEs are listed as under active exploit while one other is listed as publicly known.

So how do we eat this elephant? One byte at a time (pun intended). Let’s start by looking a closer look at some of the more interesting updates for this month, starting with the bugs being exploited in the wild.

-    CVE-2026-56155 - Active Directory Federation Services Elevation of Privilege Vulnerability
This is one of several AD FS being patched this month, but it’s the only one being actively exploited. It stems from insufficient access-control granularity and does require local access and low privileges to start, but AD FS is exactly the kind of identity infrastructure attackers love to pivot through once they're in. It can also be paired with an RCE as we often see in ransomware. Test and deploy this patch quickly.

-    CVE-2026-56164 - Microsoft SharePoint Server Elevation of Privilege Vulnerability
The other bug being exploited in the wild this month is a modest CVSS 5.3 – but it shows why Moderate severity bugs still matter. It's a missing-authentication flaw, meaning an unauthenticated attacker can hit it over the network with no user interaction required. When something this reachable is being actively abused, patch it now and worry about the score later.

-    CVE-2026-57092 - Microsoft Windows VMSwitch Elevation of Privilege Vulnerability
This patch rates the highest CVSS score for the month: a solid 9.9. It’s a use-after-free that lets a low-privileged attacker escalate to full host compromise across a VM boundary. We saw something like this demonstrated at Pwn2Own Berlin on ESXi, but it clearly isn’t alone. If you’re using VMSwitch in your Hyper-V deployments (and you likely are), test and deploy this one quickly.

-    CVE-2026-50522/58644 - Microsoft SharePoint Remote Code Execution Vulnerability
This matching pair of CVSS 9.8 bugs results from the deserialization-of-untrusted-data and are reachable without authentication or user interaction. CVE-2026-50522 was demonstrated during Pwn2Own Berlin, so it’s odd to see Microsoft list it as “Exploit Maturity Unknown” since we literally handed them a working exploit. Just another reason to do your own risk assessment and not rely 100% on the vendor. If you have any Internet accessible SharePoint servers, test and deploy this patch quickly.

-    CVE-2026-56190 - Remote Desktop Protocol Remote Code Execution Vulnerability
This patch covers an unauthenticated, network-reachable, no user interaction required bug. The root cause is a classic one: use of uninitialized resource (CWE-908), meaning specially crafted RDP traffic can interact with memory that was never properly initialized, letting an attacker corrupt memory and potentially steer code execution. RDP Servers are a common target, so audit your systems to see which are internet accessible and start from there.

-    CVE-2026-55008 - Microsoft Exchange Server Spoofing Vulnerability
Ignore the title here and treat this like the XSS bug it is. The vulnerability is listed as a CVSS 9.6 since it’s a stored cross-site scripting flaw in Outlook Web Access, with a scope-changed impact that lets it break out of the web app context entirely. An attacker sends a specially crafted email, and if the victim simply opens it in OWA, arbitrary JavaScript executes in their browser session — no attachment needed, no macro warning, just viewing the message does it. If you’re using OWA, test and deploy this one quickly.

-    CVE-2026-50518 - Windows DHCP Server Remote Code Execution Vulnerability
There are a couple of these DHCP RCE patches in this release, but the other has caveats while this one does not. Both are heap-based buffer overflows scoring CVSS 9.8, both unauthenticated and network-reachable. If you're running DHCP Server role on anything Internet-adjacent (you're not, right?), these move to the top of the list.

-    CVE-2026-56188 - Windows Server Network driver Remote Code Execution Vulnerability
Another Critical-rated bug, this one is caused by a race condition. It’s always fun to see a TOCTOU bug rated this high, since race conditions are notoriously finicky to exploit reliably. While it may prove tricky to exploit, this bug could allow an attacker to execute privileged code over the network without user interaction. Don’t let the race condition lull you to sleep on a wormable bug.

-    CVE-2026-55010 - Minecraft Bedrock Dedicated Server Remote Code Execution Vulnerability
File this in the “why not” category. This bug is a heap-based buffer overflow in Minecraft Bedrock Dedicated Server, also CVSS 9.8 and also unauthenticated RCE. Yes, your kid’s Minecraft server (it is your kid’s server, right?) is exposed to the same class of bug as your DHCP infrastructure. Patch it anyway.

Here’s the full list of CVEs released by Microsoft for July 2026:

** Indicates this CVEs has already been resolved by Microsoft, and no further action is needed by the end user.

I’ll do my best to summarize everything else in this release, but no promises. I’m only human after all.

Looking at the remaining Critical-rated patches, Office is its own weather system: fourteen Word/Excel/PowerPoint/Office RCEs clustered at CVSS 7.8, plus five Windows Media Foundation RCEs. Outside of the Preview Pane attack vector, they are individually unremarkable; collectively, patch Office and reboot. Always reboot. We’ve already mentioned DHCP some, but DHCP Server can't catch a break. Beyond the one already covered, add CVE-2026-56159, CVE-2026-48564, CVE-2026-50370, and DHCP Client cousin CVE-2026-54128. Five DHCP RCEs in one release. Rounding things out, Print Spooler (CVE-2026-58608), Windows TCP/IP (CVE-2026-54999), and a SQL Server RCE pair (CVE-2026-54117/54118) all receive patches, and all are rated a CVSS 8.8. VE-2026-55944 (Dynamics NAV/Business Central On-Prem RCE, 9.8) is the same deserialization flavor as the SharePoint pair; it’s unauthenticated, network-reachable, and easy to overlook since it's not SharePoint. CVE-2026-48561 (Microsoft Copilot RCE, 9.6) and CVE-2026-50380 (Windows GDI+ RCE, 9.6) round out the near-top tier. Don't forget CVE-2026-55040, a SharePoint Security Feature Bypass (9.1) — patch it in the same pass as the SharePoint RCE pair since it's the same product family. Identity and infrastructure get hit too: CVE-2026-54121 (AD Certificate Services EoP, 8.8) and CVE-2026-50444 (WSUS EoP, 8.8). The obscure Reliable Multicast Transport Driver (RMCAST) takes two RCEs (CVE-2026-54982, CVE-2026-54995), and CVE-2026-50474 gives Remote Desktop Client its own RCE, separate from the RDP one already covered. The rest is a long tail: Defender RCE x2, GDI+ again, Windows Media x2, Secure Kernel Mode EoP x2, and a second Hyper-V EoP. You can consider these “normal” as far as patch cadence goes.

That leaves us with 95 RCE to discuss. I would explain, but there is too much, so let me sum up. CVE-2026-55944 (Dynamics NAV/Business Central On-Prem, 9.8) is the same deserialization flavor as the SharePoint pair: unauthenticated, easy to miss since it's not SharePoint. CVE-2026-54990 (Remote Desktop Client), CVE-2026-49172 (Windows FTP Service), and CVE-2026-50447 (MSMQ) all hit 9.8 too, proof severity labels lag CVSS sometimes. CVE-2026-48561 (Copilot) and CVE-2026-50380 (GDI+) sit at 9.6.

The pattern worth watching: 14 Windows NTFS and 7 ReFS RCEs/ That makes 21 filesystem-driver bugs, an unusually large cluster suggesting a shared root cause. Microsoft Edge (Chromium-based) contributes 21 more that are genuinely Microsoft's to patch, not Chromium re-listing noise. Remote Desktop Client racks up a second and third RCE (CVE-2026-50474, CVE-2026-58594), and Windows Admin Center picks up two (CVE-2026-56196/56197) — WAC exposure keeps creeping into these releases. Exchange Server (CVE-2026-55005) and AD Domain Services (CVE-2026-49178) both land at 8.8.

And because this release wouldn't be complete without it: CVE-2026-50663, an RCE in Age of Empires II: Definitive Edition. Yes, really. Patch your civilization anyway.

There are close to 260 EoP bugs in this month’s release. Microsoft could have just published the EoPs and still had a record-setting month. As usual, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. What’s really frustrating is that 94 have no explicit privilege statement at all. Microsoft just says “elevate privileges” with no detail. By my count, that leaves around 25 bugs to consider. Some don’t elevate at all. The FAQ literally says the attacker just gets “the rights of the user running the affected application.” That covers Win32k, Clip Service, Search Service, MSMQ, and SharePoint. A few get a Low-to-Medium integrity bump. There are also a couple that lead to downgraded service accounts or arbitrary file deletion, but nothing else I’ve seen really stands out too much.

There are 20 Security Feature Bypass (SFB) bugs this month, and it's a genuinely mixed bag. CVE-2026-55040 leads at Critical, CVSS 9.1 as it’s weak authentication in SharePoint Server. Patch it in the same pass as the SharePoint RCE pair since it's the same product. The AI-coding-tool trend continues: GitHub Copilot and Visual Studio Code and Visual Studio all land SFB bugs, mostly injection or path-traversal flavored. BitLocker is this month's lone publicly disclosed bug. It’s not exploited yet, but public disclosure is a countdown clock, not a free pass. It requires physical access, as does the bug in Microsoft XML. The firmware/boot cluster is worth a second look: Secure Boot, Boot Loader, and Key Guard all touch the trust chain below the OS. Meaning, despite a low CVSS score, “if this fails, nothing above it can be trusted” stakes. Rounding out the SFB patches, there are two .NET SFBs, two Windows Kernel SFBs, and a DNS/Cryptographic Services bringing up the rear.

The July release includes 31Spoofing bugs this month, and we’ve already covered the most important (Exchange). SharePoint Server accounts for another ten with almost all the same root cause: stored XSS letting an authenticated attacker spoof content in the browser. Microsoft Edge (Chromium-based) contributes fifteen more spanning access-control failures, SSRF, type confusion, and UI misrepresentation. All genuinely Microsoft's to patch, not re-listed Chromium noise. The remaining six round out the usual suspects: a Windows NAT spoofing bug reachable from an adjacent network, a Bing app flaw on iOS, a PowerBI Report Server XSS issue, a .NET output-encoding bug, and an AD FS spoofing flaw. None publicly disclosed, none exploited, but with SharePoint's history this year, don't let "just Spoofing" lull you into deprioritizing the patch cycle.

Of 111 Information Disclosure bugs, the overwhelming majority of these simply result in info leaks consisting of unspecified memory contents or memory addresses. GitHub Copilot is the standout. Here, the bug insufficiently protected credentials, meaning actual secrets leak, not memory scraps. The Windows Admin Center flaw discloses data via improper authentication. A management console leaking to an unauthorized party is a bigger deal than it sounds. SharePoint uses SSRF to pull data server-side, and the Event Logging Service is a protection-mechanism failure, not a memory bug at all. Edge picks up three genuinely file-system-flavored disclosures — improper authorization, files/directories accessible to external parties, and link-following — plus Edge for Android exposing “private personal information” twice and two path-traversal bugs. The remaining 40+ are mostly one-line “exposure of sensitive information to an unauthorized actor” entries scattered across File Explorer, Push Notifications, Cryptographic Services, and Win32k.

Only 8 Tampering bugs this month, the smallest bucket, but a couple stand out. The top of the list is a WSUS bug, caused by an uncaught exception that lets an unauthenticated attacker tamper with the update service over the network. That’s your patch-management infrastructure itself being the target, which always deserves extra attention. Windows CNG (the crypto API) picks up a missing-cryptographic-step flaw, and Windows DNS Client shows up three separate times across the list, twice for improper access control and once for missing authentication on a critical function. DNS resolution having this many tampering paths in one release is worth flagging as a pattern rather than three unrelated bugs. The one genuinely different entry is Outlook Copilot, described simply as vulnerable to “malicious uses” enabling tampering over the network. That’s a fantastically vague phrasing for an AI-assistant feature, continuing this year's running theme of Copilot-branded features showing up somewhere in every release. Finally, a .NET link-following bug and a WSL2 kernel race condition receive patches. Both require local/authorized access to trigger.

Still with me? Good, because we have 35 DoS bugs to cover, and this is really an identity-infrastructure story more than a grab-bag. Active Directory Federation Services alone accounts for seven of them, all sitting at CVSS 7.5, all stack-based buffer overflows or infinite loops that let an unauthenticated attacker knock the service over the network. The .NET ecosystem is the other big cluster: .NET, .NET Framework, and ASP.NET Core/OData contribute nine bugs combined, almost all “allocation of resources without limits or throttling”.  HTTP.sys and HTTP/2 pick up the same flavor. LSASS shows up twice, which is always worth a second look given what that process actually holds. Rounding out the list are patches for Windows DHCP Server, SMB Server, Secure Channel, Hyper-V, and IKE Protocol each take a single hit, mostly requiring authorized or adjacent-network access rather than being wide open to the internet.

No new advisories are being released this month.

Looking Ahead

The next Patch Tuesday will be on August 11, just after Hacker Summer Camp in sunny Las Vegas. Should I survive the heat, I’ll be back then to give you my full thoughts on the release – no matter how large it may be. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

Providing a public, open way to browse the anonymous, aggregated device data we collect was always part of the plan, and this preview is our first step toward it.

You can already use it to search and filter devices to see aggregated community insights, starting with a deliberately focused set of specifics, such as whether a device requires an internet connection, and which protocols and integrations it works with. We've kept that initial scope narrow on purpose, giving us a solid foundation we can build on together with you, our community, as the database grows.

LWN looked at Home Assistant in May 2025.

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.

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.

⚡ It's time to power up the build! In this episode of my Beginner PC Build Series, we're installing the power supply (PSU), connecting motherboard and CPU power, routing SATA cables, and tackling one of the most satisfying parts of any PC build - cable management.

I'll explain what a power supply actually does, why wattage matters, what "fully modular" means, how to identify each cable, and my favorite cable management tips that make future upgrades and troubleshooting much easier.

Whether you're building your very first gaming PC or just need a refresher, this step-by-step guide will help you wire everything correctly and keep your build clean.

A huge thank you to ASUS and Kingston for partnering on this PC build series! ❤️

👍 If you're enjoying the series, don't forget to subscribe so you don't miss the next episode where we install the graphics card and finish wiring the entire system!

#PCBuild #GamingPC #CableManagement #PCBuilding #PowerSupply #ASUS #Kingston #CustomPC #DIYPC #BeginnerPCBuild

https://pcpartpicker.com/user/snubsie/saved/#view=Htk84D

📺 Watch the Full PC Build Series: https://www.youtube.com/playlist?list=PLeYHKbaShxTHQVUHZfM8_44pjyI9LLzfe

CPU: AMD Ryzen 9 9950X 4.3 GHz 16-Core Processor ($519.00 @ Amazon)
Amazon: https://amzn.to/3O70PIU
Best Buy: https://bestbuycreators.7tiv.net/YRWkZq
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CPU Cooler: Asus ROG Ryujin III ARGB Extreme 89.73 CFM Liquid CPU Cooler ($389.99 @ Amazon)
Amazon: https://amzn.to/4bkdodD
Best Buy: (similar option) https://bestbuycreators.7tiv.net/DyDM4q
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Motherboard: Asus ROG STRIX X870-A GAMING WIFI ATX AM5 Motherboard ($234.99 @ Amazon)
Amazon: https://amzn.to/3NI9tO0
Best Buy: (similar option) https://bestbuycreators.7tiv.net/bORgn6
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Memory: Kingston FURY Beast RGB 64 GB (2 x 32 GB) DDR5-6400 CL32 Memory ($1359.99 @ Newegg - OOS) x 2
Amazon: https://amzn.to/49SZug7
Best Buy: (similar option) https://bestbuycreators.7tiv.net/2anB4G

Storage: Kingston NV3 2 TB M.2-2280 PCIe 4.0 X4 NVME Solid State Drive ($311.99 @ Amazon)
Amazon: https://amzn.to/4bSOyBO
Best Buy: https://bestbuycreators.7tiv.net/vPeg7N
B&H: https://bhpho.to/4bpm9m9

Storage: Kingston FURY Renegade G5 2.048 TB M.2-2280 PCIe 5.0 X4 NVME Solid State Drive ($424.99 @ iBUYPOWER)
Amazon: https://amzn.to/4pTv8QB
Best Buy: https://bestbuycreators.7tiv.net/N9AYrN
B&H: https://bhpho.to/40dqbYK

Video Card: Asus TUF GAMING OC GeForce RTX 5080 16 GB Video Card ($1699.99 @ B&H)
Amazon: https://amzn.to/3NNmKos
Best Buy: https://bestbuycreators.7tiv.net/GKrYLB
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Case: Asus A31 ATX Mid Tower Case ($64.98 @ Amazon)
Amazon: https://amzn.to/4bTH8yd
Best Buy: (similar option) https://bestbuycreators.7tiv.net/7a3BZO
B&H: https://bhpho.to/4d3Fyu8

Power Supply: Asus TUF Gaming 1000G 1000 W 80+ Gold Certified Fully Modular ATX Power Supply ($179.99 @ Amazon)
Amazon: https://amzn.to/4qSDWHG
Best Buy: (similar option) https://bestbuycreators.7tiv.net/LKeYQO
B&H: https://bhpho.to/3N1pqPq

Case Fan: Asus TUF GAMING TF120 ARGB White 76 CFM 120 mm Fan ($14.99 @ Amazon)
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Case Fan: Asus TUF Gaming TR120 ARGB 77.4 CFM 120 mm Fans 3-Pack ($68.54 @ Amazon)
Amazon: https://amzn.to/4rzKNpN
Best Buy: https://bestbuycreators.7tiv.net/55OBVD
B&H: https://bhpho.to/46KcvYO

Turning Cable Chaos Into Cable Management Dreams
00:42 What Does a Power Supply Do?
02:04 Why This Build Uses a 1000W PSU
03:12 Fully Modular Power Supplies Explained
04:10 Identifying Every Power Cable
05:16 Installing the PSU
06:42 Subscribe & Patreon Shoutout
07:33 Connecting the 24-Pin Motherboard Cable
08:36 CPU Power Connectors
09:53 Cable Routing Tips
11:02 SATA Power & Accessories
11:54 Cable Management Basics
13:07 Next Episode Preview


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Read the full article on GamingOnLinux.

The post How Attackers Can Force AI Agents to Trust Them—and Rewrite Agents Reality appeared first on Cyber Security News | Exploit One | Hacking News.

The company launched Inflection AI Labs, a public-facing research and experimentation arm, alongside Pi Journeys, the lab's first product experiment — an AI experience designed to adapt to a user's life stage, whether that's becoming a parent, taking on caregiving duties, changing careers, or aging. The announcement arrived with a research report on consumer AI habits and a substantial update to Pi, the company's flagship chatbot, adding improved voice, memory, and new agentic tools for reminders, to-do lists, and shopping.

"Inflection AI is the company. Pi is our flagship consumer product. Inflection AI Labs is where we experiment, explore personal intelligence and share more publicly. Pi Journeys is the first public experiment from Inflection AI Labs," CEO Sean White told VentureBeat in an exclusive interview.

Behind the tidy org chart is a far more interesting story: a company attempting one of the more unusual second acts in the AI industry, powered by an argument that the entire market is optimizing for the wrong thing.

Why Inflection AI believes the chatbot era's biggest flaw is that it's transactional

White's central claim is that today's AI assistants — including the industry's most capable models — are fundamentally transactional. You ask, they answer, the session ends. He believes that architecture misses most of what people actually need from artificial intelligence in their daily lives.

"One of the things that really struck us in particular, and this showed up in the research, was that a lot of the work is very transactional, and you'll hear me say a lot that we've been shifting all this from transactional to relational systems," White said. "Not everything is going to be: I do a single turn, I utter a question, I get a search response back."

White frames the industry's evolution as a progression through four kinds of intelligence. First came raw IQ — the foundation model race. Then emotional intelligence, which Inflection made its signature with Pi's famously warm conversational style. Then agentic intelligence — AI that acts rather than just talks — which White says Inflection absorbed from its enterprise work. The fourth, and the one Inflection is now staking its future on, is what the company calls relational intelligence: AI that understands not just you, but the web of people around you.

"There's so much fear about these things pushing people into loneliness,” White said. “If we design these pro-social systems as another design criteria, that actually makes a huge difference."

That design philosophy is a pointed counter-narrative to one of the loudest anxieties in consumer AI right now: that emotionally engaging chatbots deepen isolation by substituting for human contact. Inflection argues the opposite is possible — that an AI with structured knowledge of your relationships can push you back toward people rather than away from them.

Inside Pi Journeys, the AI companion that maps your relationships and life stages

Pi Journeys makes that idea concrete. When users first open the product, it asks about their life stage — caregiver, household manager, midlife transition — and then builds what White describes as specially structured memory around the people who matter in that context. From there, the system becomes proactive.

"It starts to build up memories around that, and it acts as a memory prosthetic — but in a pro-social way," White said. "It doesn't get in the way of your interactions with other people; it really helps facilitate them." The system might remind a user, for example, that a friend deserves a call, or resurface what was last discussed with a family member involved in a parent's care.

White, who spent years as chief R&D officer at Mozilla before taking Inflection's helm, was quick to flag the obvious privacy implications of an AI that maps your social graph. "We've built a lot of privacy systems into this," he said, noting users can delete and manage the people recorded in their profile. Whether consumers will trust a venture-backed AI company with a structured database of their most important relationships remains one of the biggest open questions hanging over the product — and one that enterprise buyers evaluating Inflection's technology will watch closely.

Asked why this was the first Labs experiment, White was direct: "Pi Journeys takes into account people's life stages and experiences because we have heard from users that we can provide more value in helping them navigate their lives. Pi Journeys lets us experiment with the early stages of prosocial and relational intelligence because life isn't single-player."

The product has been tested internally and with small closed groups, White said, and is now being released more broadly as an experiment rather than a finished product — a posture the Labs branding is designed to make explicit.

What Inflection's consumer AI research reveals about how people actually use chatbots

Inflection Labs' first publication, the State of Consumer AI Research Report, offers the empirical scaffolding for the strategy. The average consumer now uses roughly two different AI tools every day and three per week, the company found — evidence, in Inflection's reading, that no single assistant has locked up consumer loyalty and that the market remains contestable.

More telling is why people choose the tools they do. Respondents cited personalization, style and tone, context awareness, and — notably — emotional understanding as deciding factors. They also said they want AI to be more than a productivity engine: a coach or mentor to motivate them, a chef to suggest recipes, a DJ to curate playlists.

"One thing we're certainly finding is that a lot of that also is in work, not so much in everyday life," White said. "That's our focus right now — the everyday life part."

This is a shrewd reading of the competitive map. The best-funded AI labs are pouring resources into coding tools, enterprise agents, and developer platforms, leaving everyday consumer use cases comparatively underserved. White sees the gap clearly. "We see a lot of products that are being aimed more and more at the enterprise," he said. "As a computer scientist by training, I kind of love the IDEs as this tool, but it's not really great for everybody. There's so much regular everyday use from folks that is either purely voice or that is purely mobile."

He recalled a conversation with a conference staffer who told him she owned only a phone, no laptop — exactly the kind of user, he argued, that the industry's developer-centric product roadmaps have left behind.

How the $650 million Microsoft deal hollowed out Inflection — and set up its second act

To understand why any of this is remarkable, you have to rewind to March 2024. Inflection was then one of the hottest startups in AI, having raised $1.3 billion in mid-2023 in a round backed by Microsoft, Nvidia, Bill Gates, and Reid Hoffman — more than $1.5 billion in total. Pi had crossed one million daily active users, per Reuters.

Then, in a deal that reshaped how the industry thinks about acqui-hires, Microsoft hired away co-founder and CEO Mustafa Suleyman, chief scientist Karén Simonyan, and most of the company's roughly 70 employees, paying Inflection about $650 million largely to license its technology, as Reuters reported. Suleyman now runs Microsoft's consumer AI business. The structure of the deal drew scrutiny from the FTC and Britain's competition regulator, though the UK's Competition and Markets Authority cleared it in September 2024 and EU regulators declined to act.

White, installed as CEO in the aftermath, steered the remnant company hard toward enterprise, acquiring three startups in late 2024 — Jelled.AI, BoostKPI, and the European consulting firm Boundaryless — and telling TechCrunch that November that Inflection had no intention of competing with companies building 100,000-GPU frontier systems.

Tuesday's announcement doesn't reverse that position so much as complicate it. Asked how to think about the company today, White called it "a consumer-first strategy that bridges both consumer and enterprise efforts" — and he insists the two sides feed each other.

Enterprise deployments, including a partnership with Intel that is among the few he can name publicly, taught Inflection how to run models inside complex infrastructure. Consumer products, meanwhile, let the company iterate at speed. "The part I also like about the consumer side, and this has always been true, is that we can move faster, experiment faster, and try and learn faster," White said.

The six-month prediction: relationship-aware AI is coming to the enterprise

Buried in White's consumer pitch is the claim that should matter most to technical decision-makers. "Normally I'd say like a year, but let's call it six months," he said. "You're going to start to see a bunch of enterprises care a lot more about the relationships that are inside the enterprises and what that picture is, not just the workflows."

If White is right, the wave of workflow-automation agents currently flooding the enterprise market is only the first phase of business AI adoption — with relationship-aware systems, tested first on consumers, following close behind. Inflection is essentially using its consumer products as a live laboratory for capabilities it plans to sell into companies. It's a capital-efficient strategy for a firm that can no longer outspend rivals on training runs, and a risky one, since it depends on consumers showing up in numbers large enough to generate the learning.

The technical substance underneath is equally pragmatic. Pi today runs not on a single proprietary frontier model but on an orchestration layer routing across many models — some descended from Inflection's original fully trained cores, some fine-tuned, some open source, including work with Nvidia that White says gives Inflection access to unreleased cutting-edge models. He also took a swipe at the industry's loose vocabulary around ownership: "When people say that the model is their own, most of the time nowadays — I guess I won't name names — a lot of companies will actually take a checkpoint, and then they will fine-tune from that checkpoint. But very few people actually start from that beginning core."

That candor extends to open source, where White carefully hedged. "We're not ready to promise what I think of as true open source, and by that I mean everything," he said, invoking his Mozilla years overseeing genuinely open projects like Rust and WebAssembly.

Weights without training data and pipelines, he argued, often leave developers unable to do anything meaningful with a supposedly "open" model. "We are a PBC, and there's still a C in there," he added — a reminder that public benefit corporations still have businesses to protect. The Labs will collaborate with academic researchers, including Stanford professors who visited the company's Palo Alto office this week, and continue contributing to open projects such as PyTorch.

Can a diminished Inflection compete with AI giants spending billions?

Reid Hoffman, the LinkedIn co-founder who co-founded Inflection and stayed on through the Microsoft upheaval, framed the announcement in the sweeping terms of his recent writing on AI and human agency. "Humans should be amplified by AI, not replaced. That's the principle Pi was built on," Hoffman said in the announcement. "When that kind of agency is available to everyone, you get superagency."

The skeptic's case is easy to make. Inflection is a fraction of its former size, competing for consumer attention against products from companies spending tens of billions of dollars a year. Pi's model was state of the art in 2023; it is not in 2026. And "relational intelligence" is, for now, a brand claim awaiting proof.

But the bull case is not crazy either. Inflection's own research shows consumers already juggle multiple AI tools and choose them for qualities — tone, emotional understanding, personalization — that frontier labs treat as afterthoughts. The company kept its technology, its Microsoft licensing windfall, and a defensible enterprise niche in on-premise, emotionally intelligent deployments. And it is targeting the one consumer segment — everyday, mobile-first, voice-first life management — that the coding-obsessed giants have largely ignored.

Asked what success looks like twelve months from now, White declined to talk numbers. "It's less about scale for scale's sake and more about scaling for impact by empowering people and improving their lives," he said. "Over the next year, success means leading the market towards relational intelligence and transforming AI interactions from transactional to relational."

Two years ago, Microsoft walked away with Inflection's founders, its staff, and its shot at the frontier — but it left behind the one idea the giants still haven't figured out how to build: an AI that knows the people in your life matter more than the tasks on your list. Inflection is betting the company, again, that the idea was the valuable part all along.

Read more of this story at Slashdot.

Read the original post: Galaxy Z Fold 8 Pre-Order Specials: $1,200 Off and $200 or $350 Gift Cards

In the lab we’re going to build today, we’ll talk about JWTs and how they can affect the security of your website. This is a lab that’s been requested, and I could not be happier that the community is actually suggesting things for me to build. It’s a privilege to do this.

JWT As Security Measure

JSON Web Tokens are used a lot by developers. They provide a way to send ‘data’ with them (e.g., a role from a user), to check for authorization. A JWT consists of 3 parts, separated by a ..

Let’s take this JWT as an example.

eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiYWRtaW4iOnRydWUsImlhdCI6MTUxNjIzOTAyMn0.KMUFsIDTnFmyG3nMiGM6H9FNFUROf3wh7SmqJp-QV30

The first part eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9 is called 'the header'. It holds information on the type of the JWT. The second part eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiYWRtaW4iOnRydWUsImlhdCI6MTUxNjIzOTAyMn0 is the payload, which holds the claims. This is where that 'data' lives. If you decode this with Base64url you'll learn that this token belongs to John Doe and that he is an administrator. The last part is the important part. This is the signature. Each JWT should be signed with a strong password. Ideally that password is saved in a .env file on the server and not in the code.

So now you might wonder, well if it’s signed, nothing can go wrong right? Well, if that were true, I wouldn’t spend my evenings writing these kinds of blog posts now would I 😅.

Let’s Build The Lab Already

You’re right to think this by now. Enough theory, let’s do some hands on keyboard. As always in this series, I’ll provide you with the lab structure and some code to get us started.

Lab Tree

/mediumLabs
└── /JWT
    ├── server.js
    ├── login.html
    ├── index.html
    ├── admin.html
    ├── /node_modules
    ├── /package-lock.json
    └── /package.json

Code

server.js

const express = require('express');
const jwt = require('jsonwebtoken');
const path = require('path');
const Database = require('better-sqlite3');
const app = express();
const JWT_SECRET = 'secret';
const db = new Database(':memory:');
db.exec(`
  CREATE TABLE users (
    id INTEGER PRIMARY KEY,
    username TEXT,
    password TEXT,
    role TEXT
  )
`);
db.prepare("INSERT INTO users (username, password, role) VALUES ('user', 'password', 'user')").run();
app.use(express.urlencoded({ extended: false }));

app.get('/', (req, res) => res.sendFile(path.join(__dirname, 'login.html')));
function getToken(req) {
  const match = (req.headers.cookie || '').match(/token=([^;]+)/);
  return match ? match[1] : null;
}
app.post('/login', (req, res) => {
  const { username, password } = req.body;
  const user = db.prepare('SELECT * FROM users WHERE username = ? AND password = ?').get(username, password);
  if (!user) return res.redirect('/?error=Invalid+credentials');
  const token = jwt.sign({ username: user.username, role: user.role }, JWT_SECRET);
  res.setHeader('Set-Cookie', `token=${token}; Path=/`);
  res.redirect('/index.html');
});
app.get('/index.html', (req, res) => {
  try {
    res.sendFile(path.join(__dirname, 'index.html'));
  } catch {
    res.redirect('/');
  }
});
app.get('/admin', (req, res) => {
});

app.listen(3000, () => console.log('Listening on http://localhost:3000'));

login.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Login</title>
</head>
<body>
    <h1>Login</h1>
    <form method="POST" action="/login">
        <div>
            <label>Username: <input type="text" name="username" required></label>
        </div>
        <div>
            <label>Password: <input type="password" name="password" required></label>
        </div>
        <button type="submit">Login</button>
    </form>
</body>
</html>

index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Home</title>
</head>
<body>
    <h1>Welcome!</h1>
    <p>You are logged in as <strong id="username"></strong>.</p>
    <p><a href="/admin">Go to Admin Panel</a></p>
    <br>
<script>
        function getCookie(name) {
            return document.cookie.split('; ').find(r => r.startsWith(name + '='))?.split('=')[1];
        }
        function decodeJWT(token) {
            return JSON.parse(atob(token.split('.')[1]));
        }
        const token = getCookie('token');
        const payload = decodeJWT(token);
        document.getElementById('username').textContent = payload.username + ' (role: ' + payload.role + ')';
    </script>
</body>
</html>

admin.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Admin Panel</title>
</head>
<body id="page">
<h1>Admin Panel</h1>
    <p>Welcome, admin. Here is the secret flag:</p>
    <p><strong>FLAG{JWT}</strong></p>
    <br>
    <a href="/index.html">Back to Home</a>
</body>
    <script>
        function getCookie(name) {
            return document.cookie.split('; ').find(r => r.startsWith(name + '='))?.split('=')[1];
        }
        function decodeJWT(token) {
            return JSON.parse(atob(token.split('.')[1]));
        }
        const token = getCookie('token');
        const payload = decodeJWT(token);
        if (payload.role !== 'admin') {
            alert('Access denied. You need to be an admin to view this page.');
        }
    </script>
</html>

Let’s Add Some Vulnerabilities

Ok, so now we have our base code, and we need to implement the functionality that will make sure only people with the role admin can navigate to /admin. First off, run npm run dev to see your code actually start.

In a very, very wrong way we already tried implementing security in admin.html. When you look inside the <script> tags, you'll see that if the role is not admin, an alert will pop up. I added this because this was a real-life finding of mine. Some developer did not realise that even if your role isn't admin, you will be able to navigate to the page. The only 'annoying' thing is that you'll have to click OK on an alert box.

It’s important to realise that these mistakes happen more often than you think. Developers with a lot on their plate, little sleep and not enough coffee can truly believe this is ‘secure’.

So let’s now add some real security to the backend! Frontend ‘security’ is ridiculous to start with, so let’s do our very best.

server.js

app.get('/admin', (req, res) => {
  const token = getToken(req);
  if (!token) return res.redirect('/');
  const payload = jwt.decode(token);
  if (!payload || payload.role !== 'admin') return res.status(403).send('Forbidden');
  res.sendFile(path.join(__dirname, 'admin.html'));
});

Alright, let’s walk through this piece of code. First we look for the token — if there isn’t one, we redirect to /, which is a good strategy. Then we decode the JWT and check if the role is admin. If there's no token, or the role isn't admin, we get a 403.

Sounds solid, right? If everything checks out, the backend trusts you, because the token says you’re an admin.

So what’s the problem? The code does check if the role is admin, which is good. But it never checks whether that role actually belongs to you. That check should happen by verifying the JWT’s signature. This code doesn’t, because jwt.decode() only reads the payload, it doesn't verify anything. When the server originally signed this token, it signed it with role: user. If you change the payload to role: admin without re-signing it, the signature no longer matches what's in the token, but since nothing here checks the signature, that mismatch goes completely unnoticed.

That’s the bug: because that check never happens, we can just change the payload in Burp Suite and see the admin page.

There is a very easy way to solve this though. Instead of using jwt.decode(token) a developer should always use jwt.verify(token, JWT_SECRET). This code makes sure that function will create a signature and check that signature with the signature that has been sent. If there was any tampering with the JWT, this signature won't check out and your access to the restricted endpoint is denied.

So let’s update our code.

server.js

app.get('/admin', (req, res) => {
  try {
    const payload = jwt.verify(getToken(req), JWT_SECRET);
    if (payload.role !== 'admin') return res.status(403).send('Forbidden');
    res.sendFile(path.join(__dirname, 'admin.html'));
  } catch {
    res.redirect('/');
  }
});

If you now try the same thing, you’ll end up in the catch block, which will redirect you to / (our login page) because the signature will not check out.

So all safe now, right? Well let’s try something else… If you ever want to crack a secret, I can recommend hashcat. It's available on all platforms and easily installable.

For this attack we’ll need mode 16500 (this is just a way to tell hashcat what we want to do). We'll also need the flag -a 0, which will tell hashcat to use a wordlist we're gonna provide. Lastly, you'll see -d 1 — that's because I'm on a Mac with an M-chip.

The complete command looks like this: hashcat -m 16500 -a 0 <JWT> rockyou.txt -d1.

Don’t forget to use the untampered token for this!

➜  wordlists hashcat -m 16500 -a 0 eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6InVzZXIiLCJyb2xlIjoidXNlciIsImlhdCI6MTc4MzEwNDc0OX0.myFrub4u8yG3IeItDYKO-2Vci6sMfLaJ1OrlQDeuQfc rockyou.txt -d1
hashcat (v7.1.2) starting
<skip>
Dictionary cache hit:
* Filename..: rockyou.txt
* Passwords.: 14344384
* Bytes.....: 139921497
* Keyspace..: 14344384
eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6InVzZXIiLCJyb2xlIjoidXNlciIsImlhdCI6MTc4MzEwNDc0OX0.myFrub4u8yG3IeItDYKO-2Vci6sMfLaJ1OrlQDeuQfc:secret
                                                          
Session..........: hashcat
Status...........: Cracked
Hash.Mode........: 16500 (JWT (JSON Web Token))
Hash.Target......: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZS...DeuQfc

As you can see, at the end of our JWT there is a colon with our JWT_SECRET written behind it.

Now let’s abuse this knowledge we have gained. In Burp Suite we’ll use functionality that will sign our tampered JWT so that the code will verify it, and the signature will check out.

In Burp Suite, click on the JWT editor on the top right corner, and choose the option ‘New Symmetric Key’. Once you have that, click specify secret and fill in the cracked JWT_SECRET in there. Give it an ID and click OK.

Once you have that, go back to Repeater, change the role back to ‘admin’, click sign and select the ID you just made.

And now, we have successfully evaded the security that was in place.

Lessons We Should Learn About This

As you saw, correctly implementing a JWT is only the first step of coding securely. Having a strong password policy, even when it’s not really enforceable, is very important. Don’t use ‘secret’ as your JWT_SECRET

One thing I already mentioned, but what I want to stress again is NEVER EVER store your JWT secret as a hardcoded variable, like we did with JWT_SECRET = 'secret'. Your code will probably live on GitHub where all kinds of stupid things can happen. A secret belongs in a .env file that is in your .gitignore and lives only on your dev machine and on the server, NOT in your code.

I break web apps for fun, make vulnerable labs to learn, and write about it so you can too.

Two Ways To Mess Up Your JWT Safety Net In Your Own Lab. was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

The post A Hidden Line of Website Text Can Turn AWS Kiro Into a Remote Code Execution Tool appeared first on Cyber Security News.

Read more of this story at Slashdot.

Hinweis: Dieser Artikel erschien im März 2007 auf pcwelt.de. Anlässlich unseres 20‑jährigen Jubiläums haben wir mittels der Wayback Machine des Internet Archive das zeitgeschichtliche Dokument restauriert. Wir haben alle Hyperlinks im Text belassen – sofern sie noch auf historische Inhalte führen.

Hier geht es direkt zum Gewinnspiel der aktuellen Höllenmaschine HMX 6 im Gesamtwert von 40.000 Euro. Bestens informiert bleiben Sie mit unserem HMX-6-Newsletter – aber vergessen Sie nicht, die Anmeldung via E-Mail zu bestätigen. Und nun viel Spaß mit der vierten Höllenmaschine:

Die PC-WELT Höllenmaschine 4 repräsentiert den Stand der PC-Technik. Die beste Hard- und Software ist gerade gut genug für den ultimativen Rechnertraum. Auch die vierte Generation im Wert von 13.333 Euro können Sie wieder gewinnen.

Das Rückgrat des 22 Kilogramm schweren Cooler Master Cosmos II bildet eine massive Stahlkonstruktion. Zu den Besonderheiten des 300 Euro teuren Big-Towers gehören eine zentrale Lüftersteuerung, Kabelmanagement, Flügeltüren und massig Platz: Das Gehäuse besitzt allein elf Laufwerksschächte für 3,5-Zoll-Festplatten und nimmt Hauptplatinen bis zum E-ATX-Formfaktor auf.

Airbrush der Höllenmaschine 4

Für das lodernde Airbrush und das seitliche Sichtfenster der Höllenmaschine 4 zeichnen sich die Casemodder Martin und Stefan Blass verantwortlich. Sein höllisch gutes Aussehen verdankt das Gehäuse den international bekannten Casemoddern Martin und Stefan Blass. Auf ihrer Website können Sie sich von den Fähigkeiten der kunstfertigen Casemodder überzeugen. Das preisgekrönte Brüderpaar kümmert sich nicht nur um das teuflische Airbrush und bauliche Veränderungen wie das seitliche Sichtfenster, sondern zeichnet sich auch für die Innenbeleuchtung der Gehäusemodifikation verantwortlich.

Die Gebrüder haben für den schönen Schein einen Multidimmer von Richter eingebaut, der LED-Ketten im Gehäuseinnenraum und im Bodenbereich ansteuert. Über die beiliegende Fernbedienung wählen Sie zwischen acht verschiedenen Grundfarben aus, regulieren die Helligkeit und definieren individuelle Farbwechsel.

Bei der Asus Rampage IV Extreme paart sich eine erlesene Hauptplatinen-Ausstattung mit einem detailverliebten UEFI-Bios, das keine Wünsche offen lässt: Die Sockel-LGA2011-Hauptplatine basiert auf dem Intel-Chipsatz X79. Das UEFI-Motherboard besitzt je acht Speicherbänke, SATA- und USB-Ports sowie fünfmal 16x PCI-Express 3.0. Zur Sonderausstattung gehören CMOS-, Start- und Reset-Knopf, Diagnose-LED, Bluetooth-Unterstützung und umfassende Übertaktungsoptionen, die sich zudem in Echtzeit überwachen lassen.

Prozessor: die schnellste Desktop-CPU der Welt

Der Intel Core i7-3960X ist der aktuell schnellste Desktop-Prozessor im Test. Das Intel-Flaggschiff besitzt sechs CPU-Kerne und arbeitet dank Hyperthreading als virtueller 12‑Kern‑Prozessor. Der Werkstakt liegt bei 3,3 Gigahertz, in der Höllenmaschine 4 läuft der Core i7 mit 4 GHz. Berechnungen puffert der 3960X in einem dreistufigen Cache-System: So ist die dritte Cache-Stufe, die alle Prozessorkerne dynamisch nutzen können, 15 MB mächtig. Auf die erste und zweite Cache-Stufe mit 64 beziehungsweise 256 KB darf hingegen jeder CPU-Kern exklusiv zugreifen.

Eine der wichtigsten exklusiven Funktionen der LGA2011-Baureihe ist der integrierte Speicher-Controller, der vier DDR3-Kanäle gleichzeitig ansteuert. Ebenfalls in der CPU verbaut sind 40 PCI-Express-3.0-Kanäle. Zur weiteren Ausstattung gehören der Schutz vor Angriffen durch einen Puffer-Überlauf (XD-Bit), zusätzliche Befehlssätze wie SSE 4.2 und das Advanced Encryption Standard Instruction Set (AES IS) sowie die Advanced Vector Extensions (AVX). Zudem unterstützt der Prozessor die Virtualisierungs-Technik Intel VT sowie 32- und 64-Bit-Betriebssysteme – so lassen sich etwa virtuelle Workstations mit unterschiedlichen Betriebssystemen einrichten.

Die Wasserkühlung Cooler Master Eisberg 240L Prestige gibt die CPU-Abwärme über einen Dual-Radiator an die Außenwelt ab, der zwei 120-Millimeter-Lüfter beherbergt. Das Rotationstempo der mit knapp 21 dB(A) sehr leisen Lüfter liegt bei 1600 Umdrehungen pro Minute. Im Microchannel-Kühlblock arbeitet deutsche Pumpentechnik, die bis zu 400 Liter pro Stunde durch das Kühlsystem schleudert und für mindestens 50 000 Betriebsstunden ausgelegt ist. Der keramikbeschichtete Pumpenantrieb dreht sich mit bis zu 3600 Rotationen pro Minute.

Grafik: 2x ASUS GTX690-4GD5 im Quad-SLI

Um die Grafikdarstellung kümmert sich in der Höllenmaschine 4 die derzeit leistungsfähigste Grafikkarte der Welt, die ASUS GTX690-4GD5. Kostenpunkt: knapp 1000 Euro pro Stück. Die Karte beherbergt mit der Nvidia Geforce GTX 690 gleich zwei 915 Megahertz schnelle Grafikprozessoren, die gemeinsam die 3D-Berechnung übernehmen.

PC-WELT

In der Höllenmaschine 4 sind gleich zwei der Asus-Doppeldecker, die über eine SLI-Brücke (Scalable Link Interface) verbunden sind. So arbeiten insgesamt vier Grafikprozessoren parallel im Quad-SLI-Modus. In der Summe stehen damit die Rechen-Power von 6144 Shader- und 512 Textur-Einheiten sowie 128 Rasteroperatoren bereit. Dabei kann der Karten-Verbund auf 4 Gigabyte GDDR5-Videospeicher zugreifen, der mit einem effektiven Datentakt von 6000 MHz arbeitet. Jede Grafikkarte besitzt mit einem Mini-Displayport und dreimal DVI vier digitale Ausgänge.

Ausstattung der Höllenmaschine 4 im Überblick

PC-WELT

Monitore: PC-Spiele in 3D auf 3 Panels gleichzeitig

Der Höllenmaschine 4 stellen wir den 27-Zoll-Bildschirm ASUS VG278H zur Seite. Die physikalische Auflösung des 120-Hertz-Monitors beträgt 1920 x1080 Pixel, die Reaktionszeit 2 Millisekunden. Die LED-Hintergrundbeleuchtung erzielt eine Helligkeit von 300 cd/m2 und unterstützt die Lightboost-Technik und damit natürlich auch Nvidia 3D Vision 2 – eine Technologie, die das Spielen in der dritten Dimension möglich macht. Im Lieferumfang des Asus-LCDs ist das “Nvidia 3D Vision”-Set aus Infrarot-Sender (integriert im Bildschirmrahmen) und Shutter-Brille enthalten. Der VG278H bietet mit DVI, HDMI und D-Sub drei Videoeingänge sowie einen Kopfhöreranschluss.

Ein großer 27-Zoll-Monitor ist gut, doch drei ASUS VG278H sind besser. Die gebündelte Auflösung des Bildschirm-Trios von 5760 x 1080 Pixeln erlaubt einen Desktop, auf dem sich bequem arbeiten lässt: Da kann man zahlreiche Fenster in ihrer vollen Größe geöffnet lassen, und behält dennoch den Überblick. Interessant ist diese große Arbeitsumgebung auch für Grafiker und Video-Bearbeiter, die dadurch gleich mehrere Projekte am Laufen halten, ohne eines schließen zu müssen.

Ernsthaften PC-Spielern kann dagegen die Bildschirmgröße nicht groß genug sein – denn sie bedeutet größere Übersicht und ein realistischeres Spielerlebnis. So füllt etwa die wunderschöne Landschaft in Skyrim die volle Breite der drei Monitore aus und wirkt dadurch schon fast wie ein echter Horizont, der Lust auf das Erkunden macht. Dank der brachialen Leistung der Höllenmaschine 4 und Nvidia 3D Vision Surround geht das auch flüssig in stereoskopischem 3D.

Arbeitsspeicher: Quad-Channel mit 64 Gigabyte

Speicher kann man nie genug haben! Die acht Module Adata XPG X Series PC3-17066U mit jeweils 8 Gigabyte Kapazität steuert der Controller in der Intel-CPU im Vierkanal-Modus an. Die Module laufen mit 10-11-11-30er-Zugriffszeiten. Der mit dem Extreme Memory Profile (XMP) effektiv 2133 Megahertz schnelle Arbeitsspeicher beschert der Höllenmaschine 4 eine Speicherbandbreite von über 45 Gigabyte pro Sekunde. Da macht Virtualisierung so richtig Spaß.

PCI-Express-SSD, 4 WD VelociRaptor und 4 WD Caviar Green

Das Betriebssystem und alle Programme dürfen auf der Solid State Drive OCZ RevoDrive 3 X2 480GB Platz nehmen. Die Flashspeicher-Festplatte arbeitet mit vier Sandforce-SF-2281-Controllern intern als Raid-0-Verbund. Dadurch erreicht die SSD eine Datentransferrate von in der Spitze fast 1400 Megabyte pro Sekunde und einen Befehlsdurchsatz von bis zu 230.000 IOPS beim zufälligen Schreiben von 4-KB-Blöcken. Eine herkömmliche SATA-Schnittstelle würde das OCZ-Modell ausbremsen, deswegen setzt das RevoDrive auf einen PCI-Express-Anschluss.

OCZ/Kioxia

Das interne Backup übernimmt ein RAID-5‑Verbund aus vier 1-Terabyte-Festplatten. Dabei kommt das derzeit schnellste 3,5-Zoll-Laufwerk im Test zum Einsatz, die Western Digital VelociRaptor WD1000DHTZ. Die VelociRaptor rotiert mit 10.000 Umdrehungen pro Minute und puffert Zugriffe in einem 64 MB großen Cache. Die maximale Datenrate der Festplatte liegt bei 210 Megabyte pro Sekunde, die durchschnittliche Zugriffszeit bei 3,7 Millisekunden. Als RAID-5‑Verbund kommt das Platten-Quartett auf bis zu 560 Megabyte pro Sekunde und stellt dabei noch knapp 2,7 Gigabyte nutzbare Kapazität zur Verfügung.

Die Aufgabe des klassischen Massenspeichers für umfangreiche Foto-, Musik- und Videosammlungen übernehmen die beiden 3,5-Zoll-Festplatten Western Digital Caviar Green 3000GB WD30EZRX. Die besonders stromsparenden Laufwerke stecken in den zwei abschließbaren Hot-Swap-Schächten der Höllenmaschine 4. So lassen sich schnell und bequem knapp 5,5 Terabyte nutzbarer Speicherplatz im laufenden Betrieb an- und abstöpseln oder auch mal unkompliziert von A nach B tragen.

Stromversorgung: bis zu 105 Ampere auf der 12-Volt-Schiene

Keine Kompromisse machen wir bei der Stromversorgung der Höllenmaschine 4. Das Cooler Master Silent Pro Hybrid 1300W bietet genügend Reserven, um alle Komponenten zuverlässig mit Energie zu versorgen. Das vollständig modulare Netzteil stellt auf der 12-Volt-Schiene in der Spitze bis zu 105 Ampere bereit und federt damit auch locker die Lastspitzen der stromhungrigen Grafikkarten ab.

Aufgrund der “80 PLUS Gold“-Zertifizierung arbeitet das ATX-2.3-Kraftpaket mit einem Wirkungsgrad von über 90 Prozent trotzdem sehr energieeffizient. Dank der semipassiven Kühlung ist das 1300-Watt-Netzteil aber auch flüsterleise. Zudem erlaubt die im Lieferumfang enthaltene 5,25-Zoll-Lüftersteuerung neben dem automatischen Modus ein stufenlos regulierbares Drehtempo für den 130-Millimeter-Lüfter – bei harter Dauerbelastung drehen Sie den Regler einfach voll auf.

Um optische Massenspeicher kümmert sich der Blu-ray-Brenner Asus BW-12B1ST. Das Multi-Norm-Laufwerk versteht sich aber auch auf alle gängigen CD- und DVD-Formate. Bei maximal 12-fachem Schreibtempo füllt das Asus-Modell einen einlagigen 25-GB-BR-Rohling in rund 11 Minuten. BD-REs beschreibt das SATA-Laufwerk mit 2-fachem Tempo, Blu-ray-Medien liest er mit 8-facher Geschwindigkeit. Im Lieferumfang des Asus BW-12B1ST ist Software für das Backup, Brennen und Abspielen von Blu-rays enthalten.

Peripherie: Luxus-Eingabegeräte und 5.1-Raumklang

Die Logitech G9x Laser Mouse kommt mit einem 4-Wege-Scrollrad und löst mit bis zu 5700 dpi auf. Die USB-Maus besitzt eine anpassbare Griffschale sowie ein bis auf 28 Gramm aufrüstbares Gewichtsmagazin. Der interne Speicher sichert bis zu fünf Profile für individuelle Tastatur-Makros und Abtastraten.

Das Logitech G19 Gaming Keyboard besitzt ein integriertes Farb-LCD, das in Echtzeit Spiele-Informationen wie die Server-IP-Adresse oder den Punktestand für über 35 Titel anzeigt. Die G19 erlaubt verschiedene Beleuchtungsfarben und hat 12 voll programmierbare G-Tasten sowie Sondertasten für den schnellen Multimedia-Zugriff. Das Logitech-Modell fungiert aber auch als aktiver 2-fach-USB-Hub und überrascht mit einer Kabelführung auf der Unterseite für Maus und Headset.

Logitech

Das THX-zertifizierte Lautsprecherset Logitech Z906 bietet 5.1-Raumklang mit digitaler Dolby- und DTS-Decodierung. Bis zu sechs digitale und analoge Audioquellen lassen sich gleichzeitig an die Bedienkonsole anschließen. Alternativ bedienen Sie das 500-Watt-Lautsprechersystem kabellos über die Fernbedienung.

Wer nachts die Nachbarn nicht stören darf, greift zum Logitech G930 Wireless Gaming Headset. Das funkt digital mit 2,4 Gigahertz zehn Stunden lang über eine Reichweite von bis zu 12 Metern. Das Headset kommt mit drei programmierbaren Tasten, Geräuschunterdrückung und Kondensatormikrofon. Dank der Dolby-Technik Headphone und Pro Logic II kann das G930 sogar 7.1-Surround-Sound simulieren.

Kreativ-, Spiele- und Office-Paket für 4000 Euro

Auf der Höllenmaschine 4 vorinstalliert haben wir das 64-Bit-Betriebssystem Windows 7 Ultimate. Für das Microsoft-Betriebssystem haben wir uns auch wegen der DirectX-11-Unterstützung entschieden. Das laut Windows-Experte Hermann Apfelböck “beste Windows aller Zeiten” verwöhnt in der multilingualen Ultimate-Variante mit den exklusiven Funktionen Bitlocker-Verschlüsselung und dem nützlichen VHD-Boot.

Dazu gibt es drei fette Software-Pakete frei Haus. Das Highlight für Kreative ist die Adobe Creative Suite 6 Design & Web Premium. Die „Rundum glücklich“-Lösung für professionelle Bildbearbeitung, Layout und Website-Design integriert die Digital Publishing Suite und enthält folgende Programme und Werkzeuge: Acrobat X Pro, Bridge, Dreamweaver, Flash Builder, Flash Professional, Fireworks, Illustrator, InDesign, Media Encoder und Photoshop Extended.