You test an MCP server by chatting with it. Ask for the open bugs, get the open bugs, ship it. The model never sends limit: "ten", an empty path or a 200 KB query while you’re watching, so those paths stay dark until a real session hits one. Say it’s the limit. The handler throws, the framework wraps the exception in a 40 KB stack trace, and the model reads all of it, adjusts, and retries with the same bug in a new shape.

LLM routing usually starts as if statements. One service picks the cheap model for ticket summaries. Another hardcodes the strong model because it needs tool calling. A third has a comment reading “never send this to the cloud” directly above a fallback that sends it to the cloud when the local server times out. Changing a model, a price or a provider means editing all three.

The router worth building is a small one: ordered rules over properties you can compute before the request leaves, written like an nginx config in a single file, with no database and no UI. Input size, whether tools or images are present, a data classification tag, a cost ceiling for the single request, which upstreams are healthy. One binary on a cheap VPS reads the file, accepts the OpenAI request shape and picks where each request goes. The rule that justifies the exercise is that privacy routing fails closed.

Appdome has released six major upgrades to its MobileBOT Defense product, repositioning it as what the company calls the industry’s first full-suite Identity-First Mobile API Protection solution. The update moves the product’s security model away from probabilistic behavioral inference and toward deterministic cryptographic proof — a distinction that has become commercially significant as AI-generated attack tooling has made legacy bot detection increasingly easy to defeat.

The core architectural shift is the introduction of a multi-tiered identity model that governs every API session before access is granted. Prior generations of mobile bot defense relied on web application firewall heuristics and session cookies to infer whether an incoming request was legitimate. That model has a structural weakness: session cookies can be captured and replayed, and applications instrumented with WAF anti-bot SDKs can be repackaged and run inside automated environments. Appdome’s new approach requires that the identity of the application, the device, and the session be cryptographically verified before any API response is issued.

The OWASP API Security Top 10 is updated periodically based on analysis of real API vulnerabilities in production systems. The list is not theoretical. The vulnerabilities it documents are the ones that security researchers find in bug bounty programs, that appear in breach disclosures, and that affect applications built by teams that considered security during development. Their persistence on the list across multiple editions reflects the difficulty of eliminating them in complex systems, not a lack of awareness that they exist.

Rate limiting is one of the few API design decisions where the failure mode is existential rather than merely inconvenient. An API without rate limiting is an API that can be brought down by a single misbehaving consumer, whether that consumer is a customer with a buggy retry loop, a competitor running a data extraction operation, or an attacker attempting a denial of service. The argument for implementing rate limiting is not about fairness or monetization tiers, though it serves both. It is about operational survival.

API authentication is the area where implementation decisions have the most direct security consequences and where the choice of mechanism is most frequently driven by familiarity rather than fit. Teams that have used JWTs extensively reach for JWTs. Teams that have configured OAuth2 once and survived it reach for OAuth2. Teams that want something simple reach for API keys. Each choice reflects a different problem being solved, and using the wrong mechanism for the problem introduces either unnecessary complexity or genuine security gaps.