Layered Defenses: Addressing Insecure AI Code, Business Logic Flaws, and Exploit Chaining

In this Expert Insights interview, Stuart McClure, CEO of Qwiet AI, details how evolving attacker methods, zero-day weaponization, and AI-generated code are reshaping application security.

A pioneer in offensive security, McClure has shaped the industry through leadership at McAfee, founding Cylance, and guiding Qwiet AI’s mission today.

He underscores API logic flaws, runtime defense integration, and effective validation to reduce false positives while helping developers anticipate threats instead of reacting late in the kill chain.

McClure warns that AI-driven code generation produces highly insecure output and that while the kill chain remains valid, attackers use AI tools and faster, multi-layer exploit chaining to breach their target.

Vishwa: You were an early voice on offensive security. How have attacker methodologies against application code shifted since the traditional seven-step kill chain model?

Stuart: The “kill chain” enumerates a series of steps that typical hackers take to identify, enumerate, and specifically attack their targets. While much of this process remains the same today as it always has been, the specificity and bespoke targeting are far greater today. 

It's not just about finding a vulnerability and exploiting it anymore. It's about leveraging AI tools and chaining exploits together in all layers of the application and DevOps stack to get to the target system, service, or data. 

The kill chain is still valid, but the tools and the speed are way different now.

Vishwa: Many development teams embrace AI-driven code generation. What blind spots in secure coding do you see expanding because of this shift?

Stuart: Well, to start, AI-driven code generation leverages generalized LLMs, which are trained on generalized code, which is generally very, very insecure. So what developers are getting today when they vibe code is probably the most insecure form of coding possible. 

Only when you can infuse AI in every step and validate each line of that code in the context of a secure development lifecycle can a developer be even remotely certain that their code is being delivered in a secure way. 

Remember the three categories of “security” vulnerabilities: 

• Execution
• Identity
• Denial-of-Service

AI Appsec solutions must understand the full complexity of the ways that attackers compromise systems and applications and remediate those gaps before the developer even needs to think about it.

Vishwa: Application Programming Interfaces (APIs) are now a top attack vector. What is the most underappreciated way adversaries are exploiting API logic flaws at scale?

Stuart: Business logic flaws are critical gaps in almost every application’s defenses. The problem of security is not just a cyber-centric technical one, but instead a business consideration as well. 

The flaws in how the business logic of the application is implemented can be one of its core downfalls. They are the hardest thing to find because they require a deep understanding of the application's intent and user usage, not just its code.

Vishwa: Runtime protection often lags behind exploitation. What practical strategies can embed runtime defense earlier in the software development lifecycle?

Stuart: You embed it by making security an integral part of the development workflow. It's not about adding a separate step at the end. It's about using tools that provide real-time feedback. 

For example, using an IDE plugin that can analyze code as you write it and flag potential vulnerabilities in the developer’s workflow (e.g., Qwiet). It's about shifting security left, so it becomes a part of the development process, not a burdensome separate activity.

Vishwa: In application security testing, false positives still exhaust developers. What validation techniques most reliably separate exploitable flaws from noise?

Stuart: This is one of the biggest burdens for developers and translates to huge problems overall. The key is to use advanced techniques beyond simple grepping for code vulnerabilities. 

You need a more advanced approach that incorporates AI and vector databases of your code, then feather in the contextual insights of reachability and exploitability of the finding.

Vishwa: As attackers accelerate zero-day weaponization, what indicators can security teams realistically monitor to anticipate rather than just react?

Stuart: The journey begins with the health of your development pipeline itself, including your DevOps infrastructure as well. Look for things like code quality, the number of vulnerabilities being introduced, and the speed of development. A healthy, secure development process is the best indicator that the resulting code will be resilient. 

It's not about monitoring millions of logs in hopes of seeing an attack and then reacting; it's about going to the root of the problem: the system and developers that produce the code.

Vishwa: For the rising threat of application-layer attacks, what cybersecurity tools or steps would you recommend for resilience?

Stuart: You need a multi-layered approach: 

• First, you need a secure coding framework or platform that analyzes code in real-time as developers build. 
• Second, you need an AI-powered application security platform that fixes the flaws found before they become zero-days. 
• And third, you need a runtime protection tool to defend against attacks that do make it through, such as a dynamic application security tool (DAST). 

It's about building a resilient system, not just a secure one. Layers are your friend.