SI-10(3): Predictable Behavior

SI-10(3) requires you to verify and document that your system responds to invalid inputs in a predictable way (for example, rejects the input safely, returns consistent error handling, logs appropriately, and does not crash or expose data). Operationalize it by defining “invalid input” scenarios per interface, implementing standardized error-handling patterns, and retaining test evidence that proves behavior matches documentation. ¹

Key takeaways:

• Define and document expected “invalid input” behavior per interface, not just “input validation” in general. ¹
• Prove predictability with repeatable tests (negative testing/fuzzing where appropriate) and stable error-handling standards. ¹
• Keep assessor-ready evidence: interface inventory, documented behaviors, test cases/results, and change records tying code to requirements. ¹

The si-10(3): predictable behavior requirement tends to fail in real programs for one reason: teams validate inputs, but they do not define and verify how the system behaves after validation fails. Assessors are looking for predictable outcomes under bad input conditions, and they want those outcomes documented and tested, not implied by “secure coding” claims. ¹

For a Compliance Officer, CCO, or GRC lead, the fastest route is to treat SI-10(3) as an engineering-and-evidence problem with a clear boundary: pick the system interfaces that accept input, define invalid input classes, standardize the response pattern, and test it. “Predictable” means consistent, safe, and aligned to documentation across releases. “Documented” means an auditor can trace from requirement to specification to test results without reverse-engineering your codebase. ¹

This page gives requirement-level implementation guidance you can hand to an engineering owner, then use to drive control design, testing, and evidence retention for assessment readiness under NIST SP 800-53 Rev. 5. ²

Regulatory text

Requirement (SI-10(3)): “Verify that the system behaves in a predictable and documented manner when invalid inputs are received.” ¹

What the operator must do:

• Verify: You need demonstrable verification activities (testing, review, or analysis) showing the system’s response to invalid inputs is consistent and safe. ¹
• Predictable behavior: For each input channel, define expected outcomes for invalid inputs (reject, sanitize, normalize, rate-limit, error code, user message, log event, alerting thresholds where applicable). Then confirm the system consistently does that. ¹
• Documented manner: The expected behavior must be written down in specifications/runbooks/standards so an assessor can compare “what should happen” to “what did happen in testing.” ¹

Plain-English interpretation (what SI-10(3) really means)

If someone (or another system) sends bad input, your system must fail safely and consistently. It should not crash, hang, leak sensitive information in error messages, or behave differently depending on subtle variations that attackers can exploit. You also need written expectations and proof that engineering tested those expectations. ¹

A useful mental model: SI-10(3) is “negative-path reliability plus security.” It is not limited to classic injection attacks. It includes malformed JSON, oversized payloads, missing required fields, wrong encodings, broken file headers, invalid state transitions, and unexpected sequences of API calls. ¹

Who it applies to (entity and operational context)

SI-10(3) applies when you implement NIST SP 800-53 controls for:

• Federal information systems, including systems operated on behalf of an agency. ¹
• Contractor systems handling federal data, including SaaS and managed services used to process, store, or transmit federal information. ¹

Operationally, it applies anywhere your environment accepts input:

• Public or internal APIs (REST, GraphQL), web forms, mobile endpoints.
• File ingest pipelines (CSV uploads, document processing, SFTP drops).
• Message queues and event streams.
• Admin consoles, CLI tools, batch jobs, and integration connectors from third parties. ¹

What you actually need to do (step-by-step)

Use this as a control implementation procedure you can assign to an engineering owner and track in your GRC system.

Step 1: Assign ownership and define scope

1. Name a control owner (usually AppSec lead, platform engineering, or service owner) and a GRC owner responsible for evidence quality.
2. Define the system boundary and list in-scope services/components.
3. Create an interface inventory: every entry point that accepts structured or unstructured input. ¹

Practical tip: scope interfaces by “where untrusted data crosses a trust boundary,” including internal callers if they are not strongly authenticated/authorized or if payloads can be influenced by external parties. ¹

Step 2: Define “invalid input” classes per interface

For each interface, define invalid input categories that matter for your system. Keep it consistent and testable:

• Type/format: wrong data type, invalid enum, malformed JSON/XML, invalid encoding.
• Size/limits: too large payload, too many fields, oversized file, deep nesting.
• Missing/extra fields: required fields absent, unknown fields present.
• Business rules: invalid state transitions, conflicting parameters.
• Security-relevant strings: control characters, template expressions, path traversal sequences, unexpected delimiters. ¹

Output: an “Invalid Input Matrix” that maps interface → invalid class → expected behavior.

Step 3: Document expected predictable behavior (standardize it)

Write a short, enforceable standard. Example elements:

• Response behavior: reject with consistent HTTP status codes; normalize to a safe internal error; do not attempt partial processing unless explicitly designed.
• User-facing message rules: no stack traces; no sensitive fields; consistent error format.
• Logging rules: record validation failure category, request correlation ID, and actor identity where available; avoid logging secrets.
• Availability safeguards: rate limit repeated invalid inputs where appropriate; cap parsing depth and payload sizes; set timeouts to prevent hangs.
• Security behavior: treat parsing/validation failures as non-success events; do not open privileged workflows based on malformed input. ¹

Keep documentation close to engineering: API error contract, service README, runbook, or a secure coding standard referenced in the SDLC. ²

Step 4: Implement predictable error-handling patterns

Engineering actions you should expect to see:

• Centralized request validation (schema validation, type checks, canonicalization).
• Centralized error mapping (internal exception → stable error code and message).
• Defensive parsers and safe libraries configured with limits (depth, recursion, file size).
• Uniform logging middleware with redaction rules.
• Consistent input rejection for unknown fields or explicitly documented tolerance rules. ¹

GRC hook: require a design review artifact for any interface that intentionally accepts “best-effort parsing” or partial processing. Those are common sources of unpredictable behavior. ¹

Step 5: Verify with repeatable tests (this is the “verify” in SI-10(3))

Build verification into your SDLC:

• Negative unit tests for validators and parsers.
• Integration tests that send malformed payloads and confirm consistent error contracts.
• Regression tests for prior invalid-input bugs.
• Fuzz testing where parsers are complex (file formats, language interpreters, protocol handling), with results recorded and triaged. ¹

For each interface, your tests should assert:

• The response is consistent (status code, error code, message format).
• The system stays available (no crash, no resource exhaustion).
• Logs are created as documented and do not include secrets.
• No sensitive data appears in error messages. ¹

Step 6: Tie verification to change control

Require that new endpoints, new file ingest types, or major parsing changes cannot ship without:

• Updated Invalid Input Matrix entries.
• Updated expected behavior documentation.
• Passing negative-path tests.
• Evidence captured in the release artifact (CI run, test report, ticket). ¹

Step 7: Operationalize recurring evidence

This requirement often fails during assessment because evidence is scattered. Set a recurring cadence for collecting:

• Latest interface inventory export.
• Current error-handling standard/version.
• Test suite list and latest run results (from CI).
• Sample logs demonstrating redaction and correlation IDs.
• Exceptions register for known deviations and compensating controls. ¹

Daydream fit: teams commonly track SI-10(3) with an owner, a procedure, and a recurring evidence bundle so assessments do not become an archaeology project. That mapping aligns with recommended control hygiene for SI-10(3). ¹

Required evidence and artifacts to retain

Keep artifacts in an assessor-friendly structure (system → control → evidence):

• Control implementation statement for SI-10(3) (what you do, where it’s documented, who owns it). ¹
• Interface inventory (endpoints, queues, file ingest points, admin inputs). ¹
• Invalid Input Matrix (interface → invalid class → expected predictable behavior). ¹
• Error-handling standard (response format rules, logging/redaction rules, safe defaults). ¹
• Verification evidence: test cases, CI results, fuzz findings summaries and triage records, defect tickets, and closure evidence. ¹
• Change management linkages: PRs, approvals, release notes referencing updates to validation/error handling. ¹

Common exam/audit questions and hangups

Assessors tend to ask:

• “Show me how you define invalid inputs for this API and what the documented behavior is.” ¹
• “Demonstrate test evidence that malformed payloads produce predictable error responses.” ¹
• “Do error messages ever include stack traces, SQL errors, object IDs, or secrets?” ¹
• “How do you prevent resource exhaustion from oversized inputs?” ¹
• “What happens if a third party integration sends unexpected fields or invalid encodings?” ¹

Hangups that slow audits:

• Teams show secure coding training instead of verification evidence.
• Documentation exists, but no proof it is enforced in CI.
• Behavior differs between services because each team invented its own error contract. ¹

Frequent implementation mistakes (and how to avoid them)

1. Mistake: Treating SI-10(3) as only “validate inputs.”
   Fix: document and test the post-failure behavior: error codes, messages, logs, and availability safeguards. ¹

2. Mistake: Inconsistent error responses across endpoints.
   Fix: standard middleware and a shared error contract; add contract tests in CI. ¹

3. Mistake: Logging sensitive payloads during validation failures.
   Fix: implement structured logging with redaction rules; test redaction with negative cases. ¹

4. Mistake: “Works in dev” but not in production due to proxies/WAF/API gateways.
   Fix: test invalid inputs through the same ingress path used in production; document gateway behavior (size limits, content-type enforcement). ¹

5. Mistake: No traceability.
   Fix: map SI-10(3) to an owner, implementation procedure, and recurring evidence artifacts so you can answer assessments fast. ¹

Enforcement context and risk implications

No public enforcement cases were provided in the source data for this requirement. From a risk standpoint, unpredictable handling of invalid inputs increases the chance of availability incidents, data exposure via verbose errors, and exploitable parser edge cases. Your control objective is to make negative paths boring: stable, documented, and tested. ¹

Practical 30/60/90-day execution plan

You asked for speed and operational clarity. Use this phased plan.

First 30 days (stabilize scope and standards)

• Assign SI-10(3) control owner and back-up owner; confirm system boundary. ¹
• Build an interface inventory and identify high-risk parsers (file ingest, complex serialization, external-facing endpoints). ¹
• Publish an error-handling and logging standard for invalid inputs, including redaction rules and “no stack traces” requirements. ¹
• Draft the Invalid Input Matrix for the most important interfaces and get engineering sign-off. ¹

Next 60 days (verify and harden)

• Implement centralized validation and consistent error mapping in priority services. ¹
• Add negative-path integration tests for priority interfaces; require them in CI for merges. ¹
• Run targeted fuzzing for complex parsers where feasible; triage findings and create defect tickets with owners. ¹
• Stand up an evidence folder structure (or Daydream control record) to collect artifacts per release. ¹

Next 90 days (scale, govern, and keep it audit-ready)

• Expand Invalid Input Matrix coverage to remaining interfaces; capture exceptions with compensating controls and expiry dates. ¹
• Add release gating: new interfaces require documented invalid-input behaviors and tests before production. ¹
• Run an internal “assessment-style walkthrough” where engineering demonstrates predictable behavior evidence end-to-end for sampled interfaces. ¹
• Operationalize recurring evidence collection so future assessments do not depend on specific individuals. ¹

Frequently Asked Questions

What counts as “invalid input” for SI-10(3)?

Anything outside documented expectations for type, format, size, required fields, encoding, or allowed state transitions. Define invalid classes per interface and write down expected responses so the behavior is testable. ¹

Do we need fuzz testing to satisfy SI-10(3)?

SI-10(3) requires verification, not a specific test method. Fuzzing is a strong fit for complex parsers or file ingest paths, but negative unit and integration tests can also provide acceptable verification evidence if they are scoped and repeatable. ¹

Is returning a generic “400 Bad Request” enough?

Sometimes, but assessors will still expect predictable and documented behavior, including consistent error formats and safe logging. If different invalid conditions produce different side effects or messages, you need to document and test those outcomes. ¹

How do we document predictable behavior without writing a huge spec?

Use a concise Invalid Input Matrix plus an error-handling standard shared across services. Keep detailed interface notes only where behavior differs from the standard. ¹

How does SI-10(3) apply to third party integrations?

Treat third party payloads as untrusted input unless you control the full chain. Document what happens when the third party sends malformed or unexpected data, and keep tests that simulate those payloads. ¹

What evidence do auditors want to see most often?

Documented expected behavior per interface and test results proving the system behaves that way for invalid inputs. Provide traceability from the requirement to the documentation to verification outputs captured from CI or approved testing records. ¹

Footnotes

1. NIST SP 800-53 Rev. 5 OSCAL JSON

2. NIST SP 800-53 Rev. 5