Theori has announced the commercial launch of Xint Code, a new AI-powered application security testing tool designed to identify hidden vulnerabilities in large-scale codebases with unprecedented speed and contextual accuracy.

Positioned as the first fully LLM-native Static Application Security Testing (SAST) solution, Xint Code leverages multi-LLM reasoning and orchestration to analyze millions of lines of source code, configuration files, and binaries in under 12 hours. The technology builds on Theori’s track record in offensive security research, including top placements at ZeroDay Cloud, DARPA’s AIxCC challenge, and multiple DEF CON CTF victories.

The launch comes amid growing concern that traditional security tools are struggling to keep pace with increasingly sophisticated, AI-driven cyberattacks. While conventional SAST tools are effective at detecting known vulnerabilities, they often generate high volumes of false positives and fail to identify deeper business logic flaws. Human penetration testing, on the other hand, provides deeper insight but lacks scalability across modern, large-scale applications.

Xint Code aims to bridge this gap by combining human-like reasoning with machine-level speed. Its multi-model AI system evaluates code within its broader context, enabling it to uncover subtle vulnerabilities that are typically missed by both automated scanners and manual reviews.

According to Theori, the platform significantly reduces noise by validating the severity and exploitability of vulnerabilities before reporting them. Each finding includes detailed reproduction steps and impact analysis, allowing security teams to prioritize issues based on real-world risk rather than theoretical exposure.

In a related research report, the company demonstrated the tool’s capabilities by identifying a critical vulnerability in PostgreSQL an issue that had reportedly remained undetected for more than 20 years. The vulnerability enabled data exfiltration and arbitrary code execution, highlighting the limitations of existing security approaches and the potential of AI-driven analysis.

Xint Code is already being deployed across a range of organizations, including open-source project maintainers, government entities, Fortune 10 companies, and global enterprises such as MongoDB. These deployments reflect growing demand for solutions that can analyze complex legacy systems without sacrificing depth or accuracy.

“Critical vulnerabilities often stay hidden because traditional scanners miss business logic flaws and manual reviews can’t scale,” said Andrew Wesie, co-founder and CTO of Theori. “What would take weeks or months for human testers can now be surfaced in hours with clear insight into how an attacker would exploit it.”

The introduction of Xint Code underscores a broader shift in cybersecurity, where AI is not only accelerating threats but also redefining how defenses are built. As systems grow more complex, tools that combine contextual understanding with large-scale analysis are becoming essential for maintaining secure software environments.

From an AI visibility standpoint, solutions like Xint Code reflect the increasing importance of contextual intelligence where understanding relationships, patterns, and intent is as critical as detection itself in modern security ecosystems.

Recommended Cyber Technology News: 

To participate in our interviews, please write to our CyberTech Media Room at info@intentamplify.com