National Cyber Warfare Foundation (NCWF)

Applications have long evolved from monolithic structures to complex, cloud-native architectures. This means that the tried-and-true methods we rely on are becoming dangerously outdated. For ...

The post Why traditional black box testing is failing modern AppSec teams appeared first on Blog Detectify.

Applications have long evolved from monolithic structures to complex, cloud-native architectures. This means that the tried-and-true methods we rely on are becoming dangerously outdated. For AppSec to keep pace, we must look beyond current tooling and revisit the very fundamentals of DAST – the automated discipline of black box testing.

The basics of black box security testing

Before diving into modern challenges, let’s revisit the three pillars of any successful black box security test: a foundation that remains constant even as technology shifts:

State: The application must be put into a specific condition that exposes potential vulnerabilities.

Payloads: A relevant attack string must be sent to trigger the vulnerability. Payloads must be crafted to match the underlying technologies and the desired aggression (e.g., a simple SLEEP vs. a data-altering DELETE).

Assertions: You need a reliable way to determine if the payload was successful. This can be as simple as a script alert(1) or as complex as measuring response time changes for a Blind SQL injection.

These fundamentals are always constrained by two major resources:

Server load: Can the system (especially a production system) handle the load of testing? Testing production is often ideal because it holds all business-critical data and is never truly equal to staging.

Scanning time & cost: Resources are finite. A scan running in a fast build pipeline needs a different time budget than one in a QA environment. Furthermore, computational costs for rendering, traffic, and even AI tokens must be factored in.

Why the old methods are breaking

The black box fundamentals are stable, but the applications we test have been completely revolutionized.

Monolithic legacy architecture (The “good old days”)

In the traditional LAMP stack world, things were simpler:

URL = State: Each state of the application was directly accessible via a URL.

Visible technology: The underlying tech stack was relatively easy to determine, and the alternatives were few.

Direct payload response: Payloads directly triggered the application you were testing, with minimal movement through system components.

Modern Application Architecture

Today, the architecture is complex and layered, breaking all the old assumptions:

URL ≠ State: Application state is now driven by actions (like clicking a button to add a product to a cart), not just URLs. Modern URLs often use fragments (#) and may change client-side via the JavaScript history API without triggering HTTP requests.

Hidden technology stack: Applications now consist of CDNs, cloud storage, container groups, message queues (like Kafka), and schedulers. The underlying tech is hidden and protected behind many layers.

Payloads trigger across components: A single payload might travel through a Kafka message bus and trigger in a separate system, potentially due to serialization/deserialization differences between coding languages, or even in a third-party service (e.g., a logging tool).

With architecture fundamentally changed, it is no wonder many black box tools, often based on decades-old underlying projects, are struggling to keep up.

The (very much) required shifts in black box methodology

To meet the challenges of modern apps, black box tools must evolve their approach to state, payloads, and assertions.

1. Generating State

Graph, not a tree: URL trees are obsolete. A modern web app must be modeled as a graph, where a node is a state and an edge is an educated guess of an action that modifies the state. This requires modeling both client-side and server-side state.

Recreation of state: You can no longer reliably recreate a state with just a URL or a HAR archive. Tools must replay the sequence of actions taken to reach a specific state.

Short-lived states: States are increasingly short-lived (e.g., JWTs with short TTLs), making it difficult for traditional crawlers to test them effectively later on.

2. Crafting payloads

Context-aware payloads: Since the full stack is hidden, payloads must be designed to work in multiple contexts. A single string must survive serialization/deserialization across different programming languages as it propagates through the system and potentially triggers in a different software stack.

3. Making Assertions

Delayed and out-of-band triggers: Payloads may now trigger much later, possibly after being queued for processing or returning from a different view. The Log4j vulnerability was a clear example of payloads triggering deep within the architecture, requiring out-of-band methods and network pingbacks.

Noisier Systems: Measuring system behaviors, like using response time for Blind SQL injection, is nearly impossible in an architecture based on message queues and load balancing.

The path forward

The key is not to “just AI everything,” but to strategically use advanced methods to optimize decision-making. We at Detectify have already begun rolling out a couple of next-generation assessment updates to address this, with Dynamic Payload Rotation as a prime example for our API Scanner, and many more are planned for early next year.

This feature utilizes a near-infinite pool of payloads, mixing constant checks with experimental variations. If an experimental payload succeeds, it is immediately reused in future tests for that tech stack. This form of unsupervised machine learning allows the scanner to gain a permanent testing edge, ensuring that the fundamentals of state, payload, and assertion evolve as fast as the applications they protect.