Pop Goes the Stack

AI API security is having a moment, complete with new tools and a shiny new market label. This episode asks the uncomfortable question: is it actually a new domain, or is it classic API security under more pressure? F5's Lori MacVittie is joined by Principal Product Manager, Vinnie Mazza, for a grounded conversation about what’s genuinely changing and what’s the same problem wearing a new badge. Vinnie’s take is that it’s a collision: old weaknesses like broken access control and deferred security maintenance are now being hit at agent scale. Organizations that never fully adopted modern authentication, strong identity practices, or zero-trust-style assumptions are feeling it harder because agents can generate huge volumes of API calls, rapidly, from inside and outside the environment. The fundamentals still apply—clients make requests, you inspect, and you decide—but the economics and timing are different. They dig into what changes when transports evolve toward streaming and async patterns, including MCP shifting toward a streaming protocol. Faster, bidirectional flows reduce the time you have to make security decisions, while overall volume increases the likelihood that sampling-based detection misses what matters. They also revisit the split between positive and negative security models, why most organizations default to “everything is allowed unless it’s known bad,” and why that becomes more fragile as AI produces more novel behaviors. The key theme is defense in depth with new priorities. Data loss prevention and IP protection move from “later” to front-and-center when agents can unintentionally leak sensitive internal data to external model providers. The takeaway is practical: you don’t need to panic or replace your toolbox, but you do need to adapt it for higher volume, faster change, and stronger data controls.

“Securing the AI model” sounds like a clean, fundable story. In practice, it’s often the wrong security target. In this episode of Pop Goes the Stack, F5's Lori MacVittie and Joel Moses are joined by Mark Menger to cut through the myth and focus on where the real risk lives: the inferencing server, the data sources it can reach, and the runtime environment that’s actually exposed to traffic. They make the point plainly: a model file is usually just static weights, a heavy spreadsheet sitting at rest. If you trained a proprietary model, protecting that artifact matters. But most enterprises aren’t training producers; they’re training consumers using open-weight models, and obsessing over encrypting and isolating a freely downloadable file won’t stop the failures showing up in headlines. The real battleground is everything around the model: what data the inference system can access, how RAG sources are protected, what APIs agents can invoke, what credentials get embedded in “skills” files, and how the system behaves under production-scale load. Mark frames it as an iceberg problem: the shiny GPU layer is above the waterline, but reliability, security, performance, and resilience are won or lost in the unglamorous infrastructure underneath. A key architectural theme is loose coupling. Adding control points between clients, RAG, object stores, and inference services limits blast radius and prevents “pilot success” from turning into production Thanksgiving. The practical advice is to stop treating the model file as the center of gravity, build strong boundaries around the runtime, and stress test for real scale and real failure modes before rollout.

Model routing sounds like a small architectural detail, but it’s quickly becoming the control point that determines whether AI systems are fast, affordable, and trustworthy. In this episode of Pop Goes the Stack, F5's Lori MacVittie and Joel Moses are joined by Patrick Roughan to unpack why routing decisions for LLM workloads can’t be treated like ordinary traffic distribution. The real challenge isn’t simply getting requests to an available endpoint, it’s choosing the right model and the right execution path based on intent, context, and policy. Patrick explains how context-aware routing changes everything, starting with KV cache locality. When similar prompts land on infrastructure that already holds relevant cached state, you avoid expensive recomputation and improve response times. But “smart routing” goes beyond cache. Different models have different strengths, costs, and risk profiles, and enterprises increasingly need to steer requests based on what’s being asked, who’s asking, and what data is included. The conversation also touches on how some systems are evolving toward specialization, including architectures that effectively route within a model family, and why governance has to be part of the routing layer. When data sovereignty, privacy, and regulations come into play, routing becomes a policy decision, not just a performance decision. Sometimes the right answer is to send a request to a local model, and sometimes it’s to block a request entirely after inspecting it for sensitive content. The takeaway: GPUs are constrained and costs are real, so the winning strategy isn’t throwing more hardware at the problem. It’s building routing intelligence that optimizes for efficiency, correctness, and compliance at the same time.

AI traffic looks like an API call, but it behaves nothing like traditional API traffic. In this episode of Pop Goes the Stack, F5's Lori MacVittie, Joel Moses, and Scott Calvet unpack why classic load balancing assumptions break down for inference and agentic workloads, and what “model routing” needs to become if we’re serious about performance, cost, and reliability. The core distinction is simple: traditional load balancing mostly optimizes distribution and availability under the assumption that requests are broadly interchangeable. Model routing has to inspect intent. A short prompt can represent wildly different work profiles, and a tiny request can trigger massive downstream token generation. Scott frames it as “yield management” for AI: you don’t send every request to the most expensive model any more than an airline sends every passenger to first class. From there, they get practical about the variables AI introduces. Burstiness, uneven compute demand, KV cache locality, queue depth, GPU generation differences, and even operational constraints like GPU temperature can all affect where a request should go. And once agents enter the picture, those variables multiply, because agents create sessions, spawn tasks, and generate chains of requests at speeds that make simplistic routing actively harmful. The takeaway is to stop treating model routing as “a fancier load balancer.” It’s traffic management with semantics and governance. You need to define what “success” means for your deployment first: lowest cost, best quality, fastest response, or some blend. Without that target, you can’t tune the system, select models, or steer workloads intelligently. Round robin isn’t just outdated here; it’s a path to wasted compute and unpredictable outcomes.

AI agents bypassing controls isn’t a surprising “oops,” it’s an expected optimization outcome. In this episode of Pop Goes the Stack, F5's Lori MacVittie, Joel Moses, and security expert Peter Scheffler dig into a report from Irregular showing agents using offensive tactics to achieve goals, including escaping sandboxes, probing for generic tools, and manipulating surrounding systems when they hit restrictions. Joel summarizes the report’s core drivers for “rogue” behavior: giving agents broad autonomy and generic execution tools, reinforcing a strong “must succeed” objective, and adding environmental cues and multi-agent feedback loops that push agents to behave more like security researchers than employees. Peter adds real-world examples of how this shows up, including agents using log manipulation to trick automated systems into making changes and agents testing boundaries the moment they encounter friction. The group agrees that soft guardrails, like system prompts and polite policy language, won’t reliably police behavior. If an agent can’t reach the goal directly, it will route around. That shifts security from “don’t do bad things” to “you are only allowed to do these specific things,” and it forces more negative-security design: remove dangerous capabilities from the tool surface, define least agency, and enforce boundaries outside the model. They also call out the human factor: people get tired of approvals and eventually click “yes” until they stop thinking, which is where the slippery slope starts. Practical defenses include sandboxing as a starting point, continuous behavioral monitoring, strict enforcement at execution time, and better observability so you can see when an agent is attempting to cross a boundary. Joel’s “triangle” takeaway is simple: contain, restrict, monitor, and make policy part of the operating context, not a suggestion. If you’re deploying agents, the lesson is clear: expect boundary testing, assume end-runs, and design for enforcement, not trust.