The Defense Advanced Research Projects Agency (DARPA) selected Xint to research the use of autonomous AI application security for deep analyses of internally and externally developed messaging applications that are used throughout the Department of War.
DARPA was established in 1958 following the USSR’s launch of Sputnik in 1957. The purpose was to ensure the US would never again be surprised by the technological achievements of foreign countries. DARPA consequently partners with and supports private industry in the development of cutting edge new technology to keep the US ahead of rivals.
This is a big deal for Xint. Previous DARPA engagements with private industry have led to the development of the internet (via ARPANET), GPS and Siri.
Xint (which emerged within Theori) was selected following its performance (it was one of the three winners) in DARPA’s Artificial Intelligence Cyber Challenge (AIxCC), a two-year, $29.5 million competition. It is now tasked with analyzing source code, whether internally developed by messaging apps such as Signal or open source projects. It will also analyze compiled binaries using a new service launched in September 2026. The service is designed to assess software supply chain risk across compiled code running in environments such as agents, on-premises software, appliances, network daemons, and other services.
“Messaging and communications applications are unique in that an attacker needs read-only access to compromise the entire point of the app,” said Andrew Wesie, CTO and co-founder at Xint. “Third-party SDKs and libraries embedded in these apps can create hidden data risks, where even seemingly minor leaks may expose a user’s location or other personally identifiable information during sensitive communications – often without the user or even the developer knowing.”
He explained Xint’s operation to SecurityWeek. DARPA is seeking to ensure that Department of War messaging is secure against external, and especially foreign, eavesdropping. Xint technology, using the latest frontier LLM models (he describes Xint as “really just a harness and a workflow around frontier elements”) is able to examine all the source code involved in the apps concerned, reverse engineering binaries as necessary. So, for messaging from Android, Xint would look at the app itself, the Linux kernel, and any other components of the Android ecosystem that sit between the app and the kernel.
Having access to every aspect of the system being analyzed, it can then detect and investigate any vulnerability across the entirety of the attack surface, automatically triage the vulnerabilities based on accessibility to an attacker, and then generate patches for any vulnerability that could be useful to attackers.