Security experts warn that AI is dramatically accelerating both software development and the pace of attacks, forcing DevSecOps teams to adopt new verification strategies across the entire software lifecycle.
According to data from Solar, AI-assisted attackers have reduced the window for exploiting vulnerabilities from 63 days in 2019 to only a few hours in 2025. Anton Prokofiev, director of the secure software development control center at Solar, links this acceleration to two additional factors: heavy reliance on third-party libraries and the rise of vibe coding. Attackers now target supply chains rather than individual applications, since compromising one widely used library can affect up to 10,000 organizations.
Dmitry Evdokimov of Luntry notes that AI models behave differently from human developers. Instead of reusing existing libraries, models frequently generate unique code that has never been tested or reviewed by security teams. This creates fresh risk sets because the resulting code lacks coverage from traditional testing phases.
Another concern arises when developers treat AI suggestions as trusted output. Models can produce syntactically correct code that contains outdated cryptography or missing input validation. If these recommendations are accepted without further review, vulnerabilities may persist undetected.
Open-source projects already contain far more vulnerabilities than human reviewers can address. While AI is being applied to vulnerability discovery, experts caution that libraries may carry false “checked” marks when part of the analysis was performed by models of unknown quality.
The volume of generated code, dependencies, and scan results is growing rapidly. Without corresponding acceleration in analysis, backlogs expand faster than security teams can process them. In addition, 41% of confidential information leaked into large language models now consists of source code.
AI is already used for requirements preparation, task distribution, code and test generation, triage of findings, automated code fixes, and dependency management. The Solar appScreener AI plugin, trained on seven years of data from more than 1,000 companies, delivers over 90% accuracy in triage and up to 85% accuracy in preparing fixes, increasing AppSec team capacity tenfold.
Nevertheless, experts stress that critical vulnerabilities and proposed fixes must still be rechecked by humans. Dmitry Chastukhin of Hexway states that LLM-generated code is inherently untrusted and requires verification, ultimately increasing the workload for security specialists.
With the emergence of AI agents, static checks performed before deployment are no longer sufficient. Decisions are made at runtime under the combined influence of the model, prompt, external data, memory, available tools, and infrastructure permissions. New attack surfaces include prompt injection, malicious automation scenarios, and sandbox escapes.
The Hexway ASOC platform aggregates results from static, dynamic, and composition analysis, normalizes findings, removes duplicates, and prioritizes issues using application criticality and exploitability data. Luntry focuses on container and Kubernetes runtime security, enabling prioritization of AI-generated code vulnerabilities and detection of unknown threats during execution.
Experts predict that further AI adoption will bring both more sophisticated attacks and more advanced defensive techniques, urging organizations to address these challenges early rather than attempting to catch up later.
