Microsoft and Palo Alto Networks have separately reported this week that they have seen significant results after turning AI on their own code to find vulnerabilities.
Advanced AI models such as Claude Mythos have sparked debate in the cybersecurity industry about what the vulnerability discovery landscape will look like going forward. While some organizations have confirmed that these AI models are a game-changer, others are skeptical of their actual performance.
Microsoft said on Tuesday that more than a dozen of the 137 vulnerabilities fixed with its latest Patch Tuesday updates were found by a new AI system called MDASH (multi-model agentic scanning harness) built by its Autonomous Code Security team.
Palo Alto Networks revealed on Wednesday that it has used Claude Mythos and other frontier AI models to conduct a deep scan of its product portfolio, which resulted in the discovery of dozens of vulnerabilities.
Microsoft MDASH finds 16 vulnerabilities
Microsoft’s MDASH system, which orchestrates more than 100 specialized AI agents across multiple frontier and distilled AI models, has been used to find vulnerabilities in the tech giant’s own codebases.
MDASH is designed to run a structured pipeline that moves findings through several distinct stages: preparation, scanning, validation, deduplication, and proof construction. Different agents handle different roles: some identify candidate vulnerabilities, others argue for or against their exploitability, and a final stage attempts to construct inputs that actually trigger the bug. This multi-stage debate architecture means that a finding must withstand scrutiny before it reaches a human engineer.
According to Microsoft, MDASH was used to discover 16 of the vulnerabilities fixed with the latest Patch Tuesday updates. Four of them were rated critical, including unauthenticated remote code execution flaws in components such as the Windows kernel TCP/IP stack and the IKEv2 service.
Microsoft also tested MDASH against pre-patch snapshots of two heavily audited Windows components, and the AI recovered 96% and 100% of the confirmed vulnerabilities found over the past five years. In addition, on the public CyberGym benchmark (which includes 1,507 real-world vulnerability tasks), the AI system achieved an 88% rating.
MDASH is currently in limited private preview, with Microsoft inviting security teams to apply for early access.
Palo Alto Networks patches 75 vulnerabilities
Palo Alto Networks typically publishes 5-10 advisories per month. However, on Wednesday it published 26 new advisories, a record credited to its early access to frontier AI models such as Mythos.
The company used AI to analyze more than 130 products across SaaS-delivered and customer-operated environments, including products obtained via the recent acquisitions of CyberArk, Chronosphere, and Koi.
The 26 new advisories cover 75 vulnerabilities. While some were attributed to external researchers, the majority were detected internally using AI.
The cybersecurity giant pointed out that none of the 75 vulnerabilities are critical and there is no indication that they have been exploited in the wild.
Three high-severity vulnerabilities were detected, but their exploitation requires highly specific configurations to be weaponized.
Palo Alto Networks said it anticipates a surge in vulnerability discovery and patching as AI scanning becomes more widespread. The security firm believes organizations should act with urgency, as they have only a 3-5-month window to outpace adversaries.
The company noted that while its immediate priority is remediation of vulnerabilities, the long-term shift involves incorporating AI models directly into the software development lifecycle to prevent flaws from reaching production code.
“Releasing 26 security advisories in a single day is a direct result of our internal security research utilizing Frontier AI models,” said Palo Alto Networks CISO Marc Benoit. “Volume does not equal severity; rather, it reflects our commitment to finding issues while their exploitation status remains ‘none known.’”
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