Step-by-step: Creating custom ignore rules for generated code (protobuf, OpenAPI).

A perennial challenge in implementing SAST and SCA tooling within a mature CI/CD pipeline is the management of false positives originating from machine-generated code. This is particularly acute in polyglot microservices architectures where protobuf definitions and OpenAPI specifications are commonplace, and their generated clients, servers, and data transfer objects are frequently flagged for non-issues such as "hard-coded credentials" (often placeholder strings in examples) or "insecure deserialization" (a fundamental pattern in gRPC stubs). The cumulative cost of manual triage for these findings is non-trivial, both in engineering hours and in the risk of alert fatigue leading to genuine issues being overlooked.

I have conducted an analysis across several major SAST/SCA platforms (including Snyk Code, Checkmarx, and SonarQube) to systematize the approach to suppressing these false positives. The objective is to create a set of custom ignore rules that are precise, maintainable, and portable across projects. The following step-by-step methodology is derived from that analysis, with a focus on minimizing noise while preserving the security posture for human-authored code.

**Step 1: Identify the Fingerprint of Generated Code**
First, establish a consistent and identifiable pattern for the output directories of your code generators. This is the foundational layer for all exclusion rules.
```yaml
# Example project structure patterns
codegen/
├── protobuf/
│ ├── python/ # Generated Python gRPC/protobuf stubs
│ └── go/ # Generated Go gRPC/protobuf stubs
└── openapi/
├── typescript-axios/ # Generated API client
└── java-spring/ # Generated server interfaces
```

**Step 2: Implement Tool-Specific Exclusion Mechanisms**
Each tool provides a different syntax for path-based exclusions. It is critical to apply these at the most granular level possible—avoid disabling entire rules globally. Below are configurations for three common tools.

*For Snyk Code (via `.snyk` file or UI policy):*
```
exclude:
- codegen/protobuf/**/*
- codegen/openapi/**/*
- **/__pb2*.py # Pattern matching common Python protobuf suffix
```

*For Checkmarx (via `CxProject.bat` or CxSAST CLI):*
```bash
--cx-exclude-folders codegen/protobuf,codegen/openapi
--cx-exclude-files "**/*pb2*.py"
```

*For SonarQube (via `sonar-project.properties`):*
```properties
sonar.exclusions=codegen/protobuf/**/*,codegen/openapi/**/*,**/__pb2*.py
```

**Step 3: Augment with Inline Suppressions for Leakage**
Generated code may occasionally appear outside your primary `codegen` directory, perhaps due to vendor plugins or legacy build systems. For these cases, inline suppression comments are a necessary secondary measure. The efficacy of these varies by tool support.

```go
// Example in a generated Go file from protobuf
// snyk:ignore: ruleId=go-insecure-ignore This is a generated struct for protobuf communication.
type UnsafeUserCredentialsMessage struct {
Password string `protobuf:"bytes,1,opt,name=password,proto3" json:"password,omitempty"`
}
```

**Step 4: Validate Exclusion Coverage and Calculate Reduction**
After implementing the rules, execute a full scan and compare the results against a baseline scan with no exclusions. The key metrics to track are:
* Total finding count reduction percentage.
* Number of remaining findings in excluded paths (should be zero).
* Engineering time saved per scan cycle (estimated triage time per false positive * number of suppressed findings).

In a recent implementation for a suite of 12 microservices, this methodology reduced the weekly triage burden from approximately 3.5 person-hours to under 15 minutes, by suppressing an average of 87 false positives per scan. The precise configuration, however, is highly dependent on the code generation toolkit and the SAST engine's parsing fidelity.

I am interested in the community's data on this matter. Specifically, what percentage reduction in false positives have you observed after implementing structured exclusions for generated code? Furthermore, have you encountered scenarios where path-based exclusions were insufficient, requiring a more complex rule-based suppression logic?

Show me the bill.