Why GraphQL and gRPC Break Rule Based WAFs

GraphQL and gRPC are increasingly adopted to address the limitations of traditional REST based APIs. They enable more flexible data access, reduce network overhead, and support high performance, service to service communication. As a result, they are now common in modern digital platforms, internal microservices, and partner facing systems.

This shift has security implications that are often underestimated. Industry research consistently shows that API related risk is growing faster than traditional web application risk, even in organizations with mature perimeter defenses. Gartner has repeatedly identified APIs as one of the most difficult attack surfaces to secure, noting that many incidents occur without triggering traditional web application firewall alerts because the requests involved are valid at the protocol level.

The operational reality behind this trend is reflected in how APIs are used. According to recent State of the API reports from Postman, organizations now operate hundreds or thousands of APIs, with increasing adoption of GraphQL for flexible client driven queries and gRPC for internal service communication. These APIs are consumed not only by human facing applications, but also by automated systems and services that operate continuously and at scale.

At the same time, the consequences of undetected API abuse are becoming more severe. IBM’s Cost of a Data Breach studies consistently show that incidents involving misuse of legitimate access paths and exposure of sensitive data through authorized interfaces are among the most expensive to identify and remediate. These incidents rarely involve exploit payloads or malformed traffic. They involve normal looking requests that are allowed to execute as designed.

GraphQL and gRPC amplify this challenge because they break assumptions that rule based WAFs depend on. GraphQL collapses many operations into a single endpoint and allows clients to shape queries dynamically. gRPC encapsulates request semantics inside binary payloads and is often deployed in trusted internal environments. In both cases, the surface area exposed to abuse is no longer defined by static endpoints and parameters.

This creates a growing gap between how modern APIs behave and how rule based WAFs evaluate traffic. Requests that cause excessive data exposure, computation, or unauthorized access frequently remain compliant with schemas, contracts, and authentication requirements. From the perspective of a WAF, nothing appears wrong.

What Makes GraphQL and gRPC Incompatible with Rule Based Inspection

Rule based WAFs rely on stable request boundaries, predictable parameters, and visible semantics. GraphQL and gRPC remove or obscure each of these elements. As a result, requests that are fully compliant at the protocol level can still cause excessive data exposure, computation, or unauthorized access without triggering WAF controls.

The issue is not insufficient rules. It is that the inspection model no longer matches the execution model.

How GraphQL and gRPC Break Core WAF Assumptions

Rule based WAFs make decisions before execution. They evaluate requests as static objects.

GraphQL and gRPC defer meaning until execution:

• What data is accessed
• How many objects are resolved
• Which downstream services are invoked
• How much computation is performed

None of this is reliably visible at the perimeter.

As a result, WAFs may successfully validate protocol compliance while completely missing abuse that occurs during query resolution or service execution.

How Rule Based WAFs Are Designed to Work

Rule based WAFs assume that applications expose a finite and well defined set of endpoints. URLs and HTTP methods are expected to map to specific operations, allowing rules and thresholds to be associated with individual endpoints.

They assume that request structure is constrained and predictable. Parameters, payload fields, and data types are expected to follow stable patterns. This enables static validation and signature matching to identify anomalous input.

They assume that execution cost is relatively uniform for a given endpoint. Rate limiting and threshold based controls rely on the idea that a request represents a bounded amount of work. Excessive volume is therefore treated as a reliable signal of abuse.

They assume that request semantics are visible at the edge. Security relevant meaning must be inferable from URLs, parameters, headers, and payload content. If the WAF can parse and interpret the request, it can apply rules effectively.

Finally, rule based WAFs assume that risk is detectable at request time. The allow or block decision is made once, before execution, with no subsequent reassessment based on how the request is processed.

These assumptions align well with traditional web applications and many REST based APIs. They break down when request structure, execution behavior, or data exposure are determined dynamically during execution.

Why GraphQL Breaks Rule Based WAFs

GraphQL breaks rule based WAFs by collapsing many operations into a single request surface and deferring meaning until query execution.

Rule based WAFs rely on endpoints and parameters to infer intent. GraphQL exposes a single endpoint and allows clients to define the shape, depth, and scope of each request dynamically. As a result, request intent is not visible at the boundary.

Where the inspection model fails

In GraphQL, the same endpoint can be used to:

• Retrieve a single object
• Traverse multiple related objects
• Aggregate large datasets
• Trigger complex resolver chains

From the WAF’s perspective, these requests are indistinguishable. They target the same URL, use the same HTTP method, and often include no suspicious payload patterns.

Schema validation does not resolve this gap. A query can fully comply with the schema while still causing excessive data exposure or computation. The schema defines what is allowed, not what is appropriate for a given identity or context.

Example

Consider a GraphQL API that exposes users, orders, and transactions through a single endpoint.

• A legitimate query retrieves the current user and their recent orders.
• A malicious query, issued by the same authenticated user, traverses nested relationships to retrieve orders and transactions belonging to other users.

Both queries:

• Target the same endpoint
• Are syntactically valid
• Comply with the schema
• Use valid authentication

The difference in impact only becomes apparent during execution, when resolvers access data beyond the intended scope. A rule based WAF cannot detect this distinction because it has no visibility into query resolution or data access.

Why gRPC Breaks Rule Based WAFs

gRPC breaks rule based WAFs by encoding request semantics inside binary payloads and shifting execution logic into service contracts that are opaque at the edge.

Rule based WAFs depend on parsing and inspecting request content. gRPC serializes requests using protocol buffers and communicates over HTTP/2. This makes request intent difficult or impossible to infer without deep protocol awareness and service level context.

Where the inspection model fails

In gRPC, URLs and parameters no longer describe operations in a meaningful way. Method names and request fields are encoded inside binary messages. Even when decrypted, the semantic meaning of a call depends on service definitions and runtime state.

As a result:

• Static rules cannot reason about method behavior
• Rate limits cannot account for execution cost
• Parameter validation does not reflect downstream impact

Inspection occurs before execution, but execution defines risk.

Example

Consider an internal gRPC service used to retrieve account data for downstream systems.

A legitimate service call retrieves a small subset of account information for reconciliation.

A malicious or misused call, issued by a trusted service identity, invokes the same method repeatedly with different identifiers to extract a large volume of sensitive data.

Both calls:

• Use the same service and method
• Are authenticated and authorized at the transport level
• Conform to protobuf definitions

The abuse is not visible in the request itself. It emerges only when access patterns are evaluated over time. A rule based WAF has no visibility into this behavior because the protocol and execution context are outside its inspection model.

Why API WAFs Improve Coverage but Still Struggle

API WAFs improve visibility into GraphQL and gRPC traffic by extending inspection beyond basic HTTP constructs. They introduce protocol awareness, structured parsing, and schema level validation. These capabilities address some of the limitations of traditional WAFs, but they do not resolve the core execution time blind spot.

What API WAFs improve

API WAFs can parse GraphQL queries and gRPC messages at the protocol level. This enables them to recognize query structure, validate payloads against schemas or service definitions, and enforce basic constraints such as query size, depth, or field usage.

For GraphQL, API WAFs can:

• Detect malformed or syntactically invalid queries
• Enforce maximum query depth or complexity thresholds
• Restrict introspection or specific fields at the boundary

For gRPC, API WAFs can:

• Decode protocol buffer messages
• Apply method level allowlists
• Enforce basic rate limits per service or identity

These controls reduce exposure to obvious misuse and raise the baseline level of protection for modern API protocols.

Where the limitation remains

Despite deeper parsing, API WAFs still evaluate requests before execution. Their decisions are based on static properties of the request rather than on how the request behaves once processed.

For GraphQL, schema compliance does not equate to safe execution. A query can satisfy every schema rule while still traversing relationships that expose excessive data or trigger expensive resolver chains. Query complexity limits help, but they cannot account for business context, identity specific access patterns, or cumulative behavior across requests.

For gRPC, decoding the payload does not reveal intent. Service methods often encapsulate complex logic, and execution cost varies widely depending on runtime conditions. Repeated calls that extract sensitive data may remain compliant with service definitions and rate limits while still resulting in unauthorized exposure.

Structural constraint of boundary enforcement

API WAFs operate at the API boundary. They do not observe:

• Which data is returned by downstream services
• Whether authorization checks were applied consistently
• How access patterns evolve over time
• How multiple requests combine into a larger access pattern

As with traditional WAFs, once a request passes inspection, the API WAF’s role ends.

Why this matters for GraphQL and gRPC

GraphQL and gRPC defer critical security relevant decisions to execution time. Data exposure, computation cost, and authorization scope are determined during query resolution or service execution, not at request parsing.

API WAFs narrow the gap between request inspection and protocol complexity. They do not close the gap between request inspection and execution behavior.

Where GraphQL and gRPC Abuse Occurs in Practice

Abuse of GraphQL and gRPC APIs typically occurs within normal operational boundaries. The traffic involved is authenticated, conforms to protocol specifications, and often aligns with documented usage patterns. The distinguishing factor is how these requests behave over time and across systems.

Excessive data exposure through valid GraphQL queries

In GraphQL environments, abuse frequently manifests as overexposure rather than outright exploitation. Clients issue valid queries that traverse relationships more deeply or broadly than intended.

These queries:

• Comply with the schema
• Use permitted fields and resolvers
• Execute within configured depth or complexity limits

The resulting data exposure exceeds what is appropriate for the requesting identity, but the request itself remains indistinguishable from legitimate usage at the boundary.

Computation amplification without traffic spikes

GraphQL abuse can also take the form of computation amplification. A small number of carefully constructed queries can trigger expensive resolver chains, repeated database lookups, or large fan out operations.

Because request volume remains low, rate based controls are ineffective. The cost is incurred during execution, not at ingress.

gRPC misuse by trusted services

gRPC abuse is often associated with internal or partner facing services that operate under elevated trust assumptions. Calls are authenticated using service credentials and routed over internal networks.

Misuse occurs when:

• A service calls methods outside its intended scope
• Data is accessed for purposes beyond the original design
• High privilege methods are exercised more broadly than expected

These calls are valid from a protocol and authentication standpoint. The abuse is semantic, not syntactic.

Distributed and low noise access patterns

Both GraphQL and gRPC abuse tend to be distributed over time. Access is spread across requests, identities, or services to avoid triggering thresholds.

There is no single request that appears suspicious. The risk becomes visible only when access patterns are aggregated and analyzed collectively.

Delayed detection through secondary indicators

In many cases, detection occurs indirectly. Indicators include unexpected data appearing in downstream systems, audit discrepancies, or customer reports. By the time these signals surface, the activity has already passed through accepted API paths.

This operational reality underscores the limits of boundary only inspection. Abuse is not hidden because it is clever. It is hidden because it occurs where perimeter controls do not observe behavior.

How Enterprises Should Secure GraphQL and gRPC APIs

Securing GraphQL and gRPC APIs requires controls that align with how these protocols behave at runtime. Boundary enforcement alone is insufficient because critical security-relevant decisions occur during query resolution and service execution, not at request ingress.

Effective protection requires a layered approach that combines edge controls with runtime visibility and behavioral analysis.

Boundary enforcement as a baseline

WAFs and API WAFs remain necessary controls at the API boundary. For GraphQL, they can enforce basic limits on query depth, size, and field access. For gRPC, they can restrict exposed services, apply transport-level authentication, and enforce coarse rate limits.

These controls reduce exposure to obvious misuse, but they do not address abuse that complies with schemas, service contracts, and authentication requirements.

Runtime API discovery and inventory

GraphQL and gRPC environments often expose APIs that are not fully documented or centrally tracked. New queries, services, and methods emerge as systems evolve, particularly in internal and partner-facing deployments.

Capabilities such as API Discovery and API Inventory are required to establish an accurate view of what is actually running in production. This includes:

• Active GraphQL queries and mutations
• gRPC services and methods exercised at runtime
• Previously unknown or deprecated APIs still receiving traffic

Without runtime discovery, security teams are forced to defend an incomplete picture of the API surface.

Behavioral monitoring and abuse detection

For GraphQL and gRPC, abuse is behavioral rather than syntactic. It manifests through query patterns, execution paths, and access scope over time.

API Monitoring at runtime makes it possible to:

• Observe how queries and service calls behave in production
• Correlate identities with accessed data and methods
• Detect abnormal query traversal or service usage patterns
• Identify low-volume, distributed abuse that bypasses thresholds

This level of monitoring shifts detection from request inspection to behavior analysis, which is essential for these protocols.

Runtime protection based on observed behavior

Detection alone is not sufficient when abuse persists over time. Runtime controls are required to respond when abnormal behavior is identified.

API Protection capabilities enable enforcement based on observed execution patterns rather than static rules. This allows security teams to:

• Block or throttle abusive GraphQL queries after behavioral deviation is detected
• Contain gRPC services that exhibit unexpected data access patterns
• Respond to misuse that remains compliant with schemas and contracts

Protection decisions are informed by runtime context, not just request structure.

Why Levo fits this layer

Platforms such as Levo are designed specifically for this runtime layer. Levo’s API Security capabilities focus on:

• Continuous API Discovery and Inventory from live traffic
• Runtime API Monitoring to establish behavioral baselines
• API Protection driven by observed abuse patterns rather than static signatures

This approach complements existing WAF and API WAF deployments rather than replacing them. It addresses the execution-time blind spots exposed by GraphQL and gRPC without overloading boundary controls with responsibilities they cannot fulfill.

Conclusion: Protocol Complexity Exposes the Limits of Rule Based Security

GraphQL and gRPC do not introduce new categories of risk. They expose existing limitations in how security controls reason about APIs.

Rule-based WAFs were designed for environments where request intent, execution cost, and data exposure could be inferred from static properties at the boundary. GraphQL and gRPC break these assumptions by deferring meaning until execution. Queries and service calls that appear compliant at ingress can still result in excessive data exposure, unauthorized access, or disproportionate computation once processed.

API WAFs improve protocol awareness and reduce certain classes of misuse. However, they remain constrained by the same architectural boundary. They evaluate requests before execution, without visibility into how those requests behave across services, identities, and time.

For modern API protocols, security-relevant signals emerge at runtime. They are reflected in query traversal patterns, service invocation behavior, and cumulative data access rather than in request syntax alone.

This is why effective protection for GraphQL and gRPC requires more than tighter rules or deeper parsing. It requires visibility into what APIs actually do in production and the ability to respond when behavior deviates from intended use.

As enterprises continue to adopt flexible query models and high-performance service protocols, security models must evolve accordingly. Rule-based inspection remains a necessary baseline, but it is no longer sufficient on its own.