# AI Code Generation Vulnerabilities Persist at 44% in 2026 GenAI Security Report

> Veracode's 2026 GenAI Code Security Report lands on an uncomfortable number: 44% of the AI-generated code it tested introduced a security vulnerability. The average security pass rate across the models studied held flat at 56%, essentially unchanged from the prior year, even as the sheer volume of AI-written code flowing into production pipelines has surged. The weak spots were specific. Around 86% of samples failed to defend against cross-site scripting, and 88% were vulnerable to log injection. Veracode says at least 35 new CVEs in March 2026 alone traced back to AI-generated code. The report's blunt takeaway is that working code is not the same as trustworthy code. A snippet that compiles and runs can still carry a serious flaw, and as teams lean harder on models to ship faster, the security debt compounds. Veracode calls this a scale problem rather than a theoretical one, arguing the fix is verification and testing, not trust in how confident the output looks.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: Veracode · Published Friday, September 4, 2026_

## Wortins' read

Veracode's 2026 GenAI Code Security Report lands on an uncomfortable number: 44% of the AI-generated code it tested introduced a security vulnerability. The average security pass rate across the models studied held flat at 56%, essentially unchanged from the prior year, even as the sheer volume of AI-written code flowing into production pipelines has surged. The weak spots were specific. Around 86% of samples failed to defend against cross-site scripting, and 88% were vulnerable to log injection. Veracode says at least 35 new CVEs in March 2026 alone traced back to AI-generated code. The report's blunt takeaway is that working code is not the same as trustworthy code. A snippet that compiles and runs can still carry a serious flaw, and as teams lean harder on models to ship faster, the security debt compounds. Veracode calls this a scale problem rather than a theoretical one, arguing the fix is verification and testing, not trust in how confident the output looks.

## Source

[Read the full story at Veracode](https://www.veracode.com/blog/2026-genai-code-security-report-ai-risk/)

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