AI-generated code is riddled with security flaws, yet enterprises are shipping more of it than ever before. Why? Perhaps they’re over-confident, lack true visibility into security risks, or are simply choosing to ignore the problem and hope it goes away.
It’s a dangerous game to play at the dawn of the agentic AI era, as underscored in a exposes an underlying naivete about AI-built code and its vulnerabilities, even as tools like Anthropic’s Mythos are uncovering security flaws orders of magnitude faster than any human security team could ever hope to.
“Mythos-class models collapse the window between a vulnerability existing and a working exploit being available from months to minutes,” the report notes. Enterprises relying on traditional security tools and methods, it says, “cannot survive this reality.”
Security as an afterthought
Checkmarx’s survey of 2,350 CISOs, AppSec managers, and developers across 14 countries focused on how much AI-developed code enterprises are deploying, the vulnerabilities it introduces, how it impacts developer workflows, and overall sentiment about AI code and security posture.
Today, nearly half of production code is AI-generated, and the majority of enterprises also report that at least half their codebase is made up of open-source components, according to the report.
But the more AI-generated code that is pushed out, the more vulnerabilities are exposed. Enterprises who said 81% – 100% of their code is built by AI ship vulnerable code 3.4 times more often than businesses using AI more conservatively, relying on 20% or less AI code.
Additionally, 70% of developers said that AI code generation created vulnerabilities in 2025, and almost all enterprises surveyed (93%) had at least one security breach as a direct result of in-house developed apps.
Still, risk is becoming “normalized,” the report notes, with three-quarters of enterprises knowingly deploying vulnerable code as they face increased pressure for ROI. Startlingly, about 30% of respondents admitted they ship compromised code and hope the vulnerability won’t be found. Similarly, more than a third of organizations leave half of their known vulnerabilities unfixed for 90 days or more.
The report points out that the organizational bottleneck isn’t detection, “it’s the human decision to ship anyway, suppress the finding, or defer to the next sprint.”
Along with this, AppSec teams are often limited to reactive incident response as they deal with tool sprawl. And developers only continuously secure code a small percentage of the time (18%), even though nearly all are equipped with security tooling.
Ultimately, developers are “set up to fail,” the report contends. They face significant pressure to deliver, and are forced to choose quantity and speed over security. Yet, even as they face significant consequences when it comes to post-mortems, performance reviews, escalation, and blocked releases, the tools that contribute to security issues, delivering low-value findings, unclear guidance, or late feedback, continue to go unfixed.
“Developers remain accountable for outcomes, even when systems and workflows are not aligned to support them,” the report notes.
Overconfidence, outdated practices
Alarmingly, many enterprises seem to be deluded when it comes to their security posture. Of those that rate themselves as “highly mature” AI organizations, 42% often ship the most vulnerable code, and have breach rates “barely distinguishable” from other enterprises.
“Confidence isn’t protecting them,” the report notes. “It’s blinding them.”
Underscoring this, only 22% of organizations have formal AI governance, and developers still rely on manual code reviews to ensure their code meets compliance standards.
The result is a mismatch between the speed of software creation and the speed of governance, the report notes. “Compliance frameworks are evolving, but many organizations are still attempting to govern AI-scale development with processes designed for a slower era of software delivery.”
Strategic imperatives for enterprises
Enterprises do seem to have wised up (a bit) after almost immediately surfaced thousands of previously-unidentified security flaws.
Checkmarx’s survey, which, it should be noted, was conducted a month prior to Mythos’ arrival, found that enterprises are finally taking proactive measures, focusing more heavily on AI security threats overall, and investing more in DevSecOps practices, automation, and developer training.
The report emphasizes the importance of prioritizing risk over code volume; vulnerabilities should not be considered isolated incidents. Also, it’s critical to .
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