Microsoft has launched its first cybersecurity-specific model inside MDASH, its multi-model vulnerability identification and remediation harness. The company says MDASH, using MAI-Cyber-1-Flash and GPT-5.4, scored 95.95% on CyberGym. It also claims the configuration costs 50% less than its current best MDASH combination of GPT-5.4, GPT-5.4 mini, and GPT-5.3 Codex. Access is limited to approved
A maximum-severity security flaw impacting on-premises versions of Arista VeloCloud Orchestrator (VCO) has come under active exploitation in the wild. The vulnerability, tracked as CVE-2026-16812 (CVSS score: 10.0), is a case of operating system command injection that could pave the way for arbitrary code execution. "VeloCloud Orchestrator (VCO) on-prem has a security issue where this issue
Microsoft opened a new front in the AI security wars on Monday, unveiling its first custom-built cybersecurity model and a sweeping agentic defense platform — and making an argument that could reshape how enterprises buy AI: the future belongs not to the biggest model, but to the cheapest one that's good enough, routed intelligently. The company announced MAI-Cyber-1-Flash , a compact security model developed in-house by its Microsoft AI (MAI) division, embedded inside MDASH , Microsoft's multi-agent harness for finding and fixing software vulnerabilities. Together, the company says, the system scores 96% on CyberGym — a benchmark measuring how well AI systems reason over large codebases to find real vulnerabilities — beating frontier models including Mythos , Gemini , and GPT , while cutting costs roughly in half compared to Microsoft's own current production configuration. Alongside the model, Microsoft introduced Project Perception , an agentic security system that coordinates "red team" agents that hunt for paths to compromise, "blue team" agents that investigate and triage risk, and "green team" agents that remediate and harden defenses. Project Perception enters public preview on August 3. In a wide-ranging interview with VentureBeat, Microsoft AI CEO Mustafa Suleyman made clear the company sees Monday's announcement as the opening move in a much longer campaign. "We really do have a pretty significant data and harness and expertise moat, and that is enabling us to train models which are faster, better, cheaper, and I think this is genuinely the tip of the iceberg," Suleyman said. "We haven't been working on this for long. The next model is going to be pretty phenomenal." Inside the 90/10 architecture that still depends on OpenAI's GPT-5.4 The most technically revealing detail in the announcement is not the model itself but how Microsoft deploys it. MAI-Cyber-1-Flash was designed to handle up to 90% of security tasks efficiently, while MDASH escalates the remaining 10% of exceptionally difficult problems to a larger frontier model — which, notably, is OpenAI's GPT-5.4 . In other words, Microsoft's flagship security AI still leans on its longtime partner-turned-rival for the hardest work. Asked to explain that relationship, Suleyman pointed to the harness, the orchestration layer that routes each incoming problem to the right model. "The harness is like a router," he told VentureBeat. "It's kind of like guardrails and a rule set of an organizing logic, which matches queries to... incoming problems to a model that suits the problem." The system has three components, he explained: the harness, the small and fast MAI-Cyber-1-Flash handling the bulk of queries, and GPT-5.4 sitting alongside as "just a generalist coding model." Pressed on how a system reliant on OpenAI's model can outperform frontier competitors, Suleyman argued the performance comes from the whole system, not any single model. "These are very complicated, long, agentic loops which require storing state, drawing on another database, consulting best practice... handing back to a small model, writing a bunch of code, validating that that was correct," he said. "There's like hundreds of steps to solve that, and that's why it's really the system together that delivers the better performance." And why GPT-5.4 specifically for the escalation tier? Cost, again. "GPT-5.6 is expensive. GPT-5.4 is incredibly good relative to its cost," Suleyman said. "The whole game here is to reduce the costs. Mythos and so on are extremely expensive models... we want to be able to deliver better performance for cheaper. That's what customers want." The arrangement captures Microsoft's evolving posture toward OpenAI: still a customer of the partnership that drew regulatory scrutiny in Brussels and Washington in 2024, but increasingly determined to own the layers of the stack where it believes it holds durable advantages. Why token costs — not model quality — are becoming the real barrier to enterprise AI adoption The economics may matter more than the benchmark. Microsoft says the new configuration delivers roughly 50% cost savings against the current MDASH setup, which runs a blend of GPT-5.4, 5.4 mini, and 5.3 codex. In security — an always-on workload processing enormous volumes of signals — token costs compound relentlessly, and Microsoft argues they have become the binding constraint for defenders. Suleyman frames the cost issue as downstream of a harder physical limit. "The key barrier to adoption is access to chips, and cost is a function of chips," he said. "No matter how much money you've got, there's actually a limited supply of chips. Then trying to squeeze more model output on fewer chips is clearly super valuable." He also described a broader enterprise backlash against frontier-model pricing. Companies initially maxed out on the best available models, he said, but "then they realize they're sort of paying... a phenomenal amount of money, and people are absolutely token maxing everywhere across their business. So there's a massive pushback to reduce cost everywhere." That positions Microsoft to ride a market trend rather than fight it. Cost-efficient, near-frontier models have proliferated over the past year — from xAI's recent Grok release to a wave of Chinese models built on the same premise — and Microsoft is betting that as a platform company it can align itself with enterprise cost pressure. "The top model providers want you to use the most expensive model continuously, whereas because we are a platform, we're on the side of the enterprise," Suleyman said. "There's no point asking... Mythos what the capital of France is." The 100-trillion-signal data moat Microsoft says no competitor can replicate Every AI lab claims differentiation. Microsoft's claim in security rests on something genuinely hard to copy: telemetry. The company processes more than 100 trillion security signals daily — a figure consistent with its 2025 Digital Defense Report , which also cited 4.5 million new malware files blocked and 5 billion emails screened per day — and draws operational insight from 1.6 million customers. "We have trillions and trillions of data points going back decades," Suleyman said. "It is, I think, the largest longitudinal cybersecurity dataset around," in part because Microsoft's customer base includes governments "who have been consistently attacked for years, and we have been consistently attacked." Asked directly whether this constitutes an advantage no competitor can match, Suleyman didn't hedge: "That is definitely a moat for us. Both the data and the expertise, and just the experience in the institution of going through that process." The strategic logic is that cybersecurity functions as a live reinforcement-learning loop: defenders act, outcomes are observed, models improve. Microsoft argues that connecting actions to outcomes — what was exploited, what was contained, what was blocked — yields training signal that pure model labs simply cannot buy or manufacture. There is real substance here, but the usual caveats apply. The CyberGym results come from Microsoft's own evaluation, the fine print shows the headline "96%" is actually 95.95%, and vendor-run benchmarks that pit an entire tuned agentic system against competitors' base models are not apples-to-apples comparisons. What Microsoft has measured is a full harness-plus-models configuration against what customers might otherwise assemble — arguably the commercially relevant comparison, but not a controlled model-versus-model test. The dual-use dilemma: how Microsoft plans to keep a vulnerability-hunting AI out of the wrong hands A model built to find challenging vulnerabilities in complex codebases is, by definition, a model that could find vulnerabilities for attackers. This is not a theoretical concern. Microsoft's own threat intelligence team, in joint research with OpenAI published in February 2024, documented nation-state actors from Russia, North Korea, Iran, and China probing large language models for reconnaissance, scripting, and vulnerability research. Its 2025 Digital Defense Report went further, warning that AI agents could eventually automate the entire attack lifecycle. Suleyman said Microsoft is gating access accordingly. "We're very strict about who gets access to the model, and we're very careful about that," he said. "We constantly monitor the API and usage." An approved user, he added, "has to be seen to be having good intent, but also have technical competence." The rollout will be deliberately staged: "It's not going to be thousands next week. There will be tens, and then hundreds, and then thousands." Microsoft says the model was evaluated by its AI Red Team, subjected to automated and expert-led adversarial exercises, and independently assessed by a third party, with deployment wrapped in tenant isolation, auditing, and sandboxed execution environments with no internet access. Suleyman also offered a candid acknowledgment of Microsoft's positioning relative to the bleeding edge — one that doubles as a pitch to risk-averse buyers. "Even though we might be a few months behind the absolute cutting edge at any given moment... it matters that we're doing it very carefully and thoughtfully, and we have a track record of doing that," he said. For a company that spent 2024 absorbing hard security lessons — from delaying its Recall feature over privacy concerns to convening an industry summit after the CrowdStrike outage disabled some 8.5 million Windows devices — that trust-first framing is both strategy and necessity. What Microsoft's superintelligence roadmap signals about the future of enterprise AI Suleyman described a rapidly accelerating MAI roadmap, roughly nine months after Microsoft stood up its superintelligence team. "We have the compute that we need. We certainly have the data we need. We have the talent," he said. "Our momentum is accelerating rapidly." The top enterprise demand he's hearing is for "agents that can produce arbitrary code to solve whatever problem they direct them at," as vibe-coded internal tools graduate from experiments into production. The next phase, he said, pulls voice, transcription, image, and coding models "all integrated into the same harness." Notably, Suleyman expressed skepticism about the industry's default assumption that everything eventually converges into one giant unified model. "It remains to be seen whether one giant model that is fully multimodal is actually able to deliver additional transfer learning benefit because of the integration," he said, "or whether it's just a big lumbering expensive giant." That skepticism is the through line of the entire announcement. Microsoft is wagering that the unit of competition in enterprise AI is no longer the model at all — it's the system: the router, the specialized small models, the frontier fallback, and the proprietary data feeding the loop. In security, where Microsoft controls both the telemetry flowing in and the products that act on it, that wager is at its strongest. Whether it holds in domains where the company's data advantage is thinner remains the open question hanging over the MAI roadmap. For now, though, Microsoft has offered the industry a preview of how it intends to fight the next phase of the AI race: not by building the biggest brain, but by building the best machine around it. As Suleyman put it, this is the tip of the iceberg — and Microsoft is betting everything on what sits below the waterline.
Dysphoria, an Internet of Things (IoT) botnet line tracked by CNCERT and XLab, has adopted blockchain-based name services and infected-device relays after a March law-enforcement operation against JackSkid infrastructure. The researchers say the design makes the botnet harder to disrupt. CNCERT, China's national computer emergency response team, and XLab, the threat-intelligence lab of Chinese
Public exploit details released on July 27 show how an unauthenticated request can reach PHP's eval() function inside vBulletin and execute code on an unpatched forum server. The attack requires no account, administrative access, or interaction from another user. SSD Secure Disclosure lists vBulletin 6.2.1 and earlier, and 6.1.6 and earlier, as affected, but does not give a lower version
n8n has patched a high-severity expression-sandbox escape that could let an authenticated workflow editor execute operating-system commands on the server running the automation platform. Security Joes found the flaw while probing n8n's February fix for CVE-2026-27577 for another bypass. The affected ranges are =2.32.0,<2.32.1. n8n fixed the flaw in versions 2.31.5 and
Cybersecurity researchers have flagged a Microsoft Teams-themed phishing campaign that employs "secure document" lures to deliver legitimate remote monitoring and management (RMM) tools. "The victim was directed through compromised web infrastructure to a counterfeit Microsoft Store page claiming that Microsoft Teams had to be updated before the shared document could be opened," ZeroBEC said in
The China-linked cybercrime group behind the use of income tax-related phishing lures targeting Indian taxpayers, tax professionals, and corporate finance teams has been observed using a sophisticated crypter service called Cruciferra. According to a new analysis by Proofpoint, Cruciferra has been utilized by various unrelated cybercriminal threat clusters to deliver a wide array of remote
Open source software is a critical pillar of the global economy. It underpins cloud computing, financial services, manufacturing, telecommunications, government and internet services by making technology accessible and observable to communities of experts. Cybersecurity is among the top three beneficiaries of open source software. The Open Secure AI Alliance — building on the leadership of […]
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“A critical zero-day is being actively exploited in the wild”
