Beware the Invisible APIs: The Serious Threat Posed by AI Coding Assistants

AI coding assistants are changing the application security game, but many security teams are not prepared for the challenge.

While generative AI tools speed the development of software, they simultaneously accelerate the genesis of new application programming interfaces (APIs). More and more, these APIs show up with no inventory processes, formal design reviews or documentation.This creates a group of shadow APIs in production that security controls can’t see.

It’s not a matter of speed alone; it’s also about visibility. Behind the scenes, generativeAI coding assistants are radically altering the efficacy of application security. In addition to accelerating the writing of code, they are creating new API endpoints quicker than teams can document them. That’s shadow API creation at speeds never seen before.

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Old assumptions in a new landscape

 Security teams have historically relied on manual documentation, traffic monitoring and post-deployment discovery to locate theAPIs that live in their environments.  This approach functions on the assumption that people create software and that security tools are capable of observing the pace of this creation. These assumptions don’t hold true for AI-driven development, though.

API endpoints go undetected because standard API discovery methods depend on manual documentation or traffic analysis – and these endpoints were not cataloged to begin with. In AI-accelerated development environments, there’s just one true method for maintaining a thorough API inventory: find APIs directly within the source code and locate each endpoint as it’s created rather than when it appears in a breach report.

This change reveals the serious inadequacy of standard application security solutions in the face of AI-based development practices. Vendors created dynamic application security testing tools when release cycles were slower. These tools typically take hours or days to complete a scan – but when teams deploy several times a day, that’s not a workable scenario. And though static analysis is faster, it often doesn’t capture the business logic flaws and runtime behavior flaws that only show up when an application is live and dealing with real data. Standard DAST tools just won’t work here.

Ending reliance on legacy security tools

 Takeaway: As companies move from AI proof of concept to practical implementation in 2026, they can no longer rely on bygone security solutions.

AI systems scale code generation, and those who depend on legacy security tools are unlikely to be able to keep track of what they are exposing to the internet. Bad actors don’t have to waste time breaching complex defenses when they can just find an unknown endpoint with weak controls.

How NightVision helps:

• Continuous DAST reduces risk, protects sensitive data and enables modern application development at scale, with confidence.
• Provides complete visibility into APIs in production, including undocumented and unmanaged endpoints.
•  Empowers security teams to proactively identify and prioritize the API and web application risks that matter most.

Bottom line

This is the year to end reliance on legacy security tools. Otherwise, companies will have no visibility into their attack surface and no ability to secure the release velocity. Security leaders must acknowledge that application security cannot be an afterthought post-deployment or viewed as an intermittent audit function. As AI continues to drive development, organizations must have visibility the second code is written. If not, risks will pile up faster than any person or legacy tool can address them.

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