SI-10(6): Injection Prevention

SI-10(6): Injection Prevention requires you to prevent untrusted data from being interpreted as commands or queries in your systems (for example, SQL/NoSQL injection, OS command injection, LDAP injection, and template injection). Operationalize it by standardizing safe input handling, enforcing parameterized interfaces, adding server-side validation and output encoding, and proving coverage through testing, code review, and runtime monitoring. ¹

Key takeaways:

• Treat every external input as untrusted, including third-party integrations, internal services, and admin consoles.
• Build injection resistance into engineering standards: parameterization first, validation and encoding as required, plus guardrails in CI/CD.
• Audits turn on evidence: secure coding standards, SAST/DAST results, remediation records, and production monitoring.

The si-10(6): injection prevention requirement is short, but the operational scope is wide: anywhere your systems accept untrusted input and pass it to an interpreter, you have injection risk. “Untrusted” means more than internet traffic. It includes partner and third-party payloads, file uploads, message queues, APIs called by mobile apps, data from internal microservices, and even inputs from privileged users through admin tooling.

For a Compliance Officer, CCO, or GRC lead, the fastest path to execution is to translate SI-10(6) into a small set of enforceable engineering rules, then collect repeatable evidence that those rules are followed. You do not need to prove perfection. You need a defensible, systematized program that reduces injection likelihood and impact, and that reliably finds and fixes injection defects before deployment.

This page gives requirement-level implementation guidance you can hand to engineering leadership: what “prevent” means in practice, who owns what, what artifacts to retain for assessments, and what auditors commonly challenge.

Regulatory text

Requirement (excerpt): “Prevent untrusted data injections.” ¹

Operator interpretation: You must stop untrusted inputs from being executed or interpreted in a way that changes control flow, queries, commands, or templates. In practice, that means you implement technical and procedural controls that (1) reduce introduction of injection defects during development, (2) detect injection defects through testing and review, and (3) block or contain injection attempts at runtime. ¹

Plain-English interpretation (what “injection” means)

Injection happens when data is treated as code. Common patterns:

• Database injection: SQL/NoSQL queries built from concatenated strings.
• OS command injection: User input passed to shell execution.
• Directory/path injection: Input used to build file paths, causing traversal or unauthorized access.
• LDAP/XPath injection: User input embedded in directory or XML queries.
• Template injection / expression injection: Untrusted values rendered by a template engine or expression language in unsafe mode.
• Header injection / log injection: Untrusted strings written into headers or logs without canonicalization, causing downstream parsing issues.

The compliance objective is consistent prevention. If your teams rely on “developer caution” as the control, you will struggle to demonstrate reliable operation.

Who it applies to (entity and operational context)

Entities: Federal information systems and contractor systems handling federal data commonly adopt NIST SP 800-53 controls, including SI-10(6). ²

Operational contexts to scope in immediately:

• Public-facing web apps, APIs, mobile backends
• Internal business apps (ERP extensions, HR systems) where “internal” users still provide untrusted inputs
• Data processing pipelines (ETL jobs, batch imports, CSV ingestion)
• Integrations and third-party connections (webhooks, file transfers, EDI, iPaaS)
• DevOps and platform surfaces (CI/CD variables, build scripts, IaC templating, admin consoles)

Systems most likely to be challenged in an assessment: Anything that processes regulated data or provides privileged capabilities (admin functions, payment/refund operations, identity workflows, configuration changes).

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

1) Assign a control owner and define “done”

Make SI-10(6) an owned control with a named accountable leader (often AppSec or Security Engineering) and identified stakeholders (Engineering, SRE, QA, Product). Your “done” definition should be measurable:

• A secure coding standard exists and is adopted.
• High-risk injection sinks are gated (query builders, shell calls, template renderers).
• Pre-release testing is required and evidenced.
• Production monitoring and response exist for injection attempts.

This mapping and evidence plan is explicitly called out as a recommended control approach. ¹

2) Build and publish an injection-safe coding standard

Create a short, enforceable standard that engineering can follow and reviewers can check. Minimum content:

• Approved patterns: parameterized queries/ORM parameter binding; safe query builders; allowlisted command execution wrappers; safe template engine configurations.
• Prohibited patterns: string concatenation for queries; direct shell execution with user-controlled strings; dynamic evaluation functions (language-specific “eval” classes).
• Input validation rules: server-side validation; canonicalization; allowlists for format and length; reject unexpected encodings.
• Output encoding guidance: context-aware encoding for HTML, JSON, XML, and logs where relevant.
• Secrets and config handling: disallow input-driven selection of connection strings, file paths, or interpreter flags.

Keep the standard short enough that teams will follow it. Put details in appendices.

3) Identify injection “sinks” and enforce guardrails

Inventory the places where untrusted input can become executable:

• Database access layers
• OS process execution libraries
• Template engines
• Deserialization routines and expression evaluators
• File system access that composes paths from input

Then enforce guardrails:

• Approved libraries/wrappers only (internal package or shared module).
• Code review checklist items for injection sinks.
• Static analysis rules tuned to your languages and frameworks.
• Build-time policy: block merges for critical injection findings or missing remediation tickets (define thresholds internally and apply consistently).

4) Add testing that proves prevention, not intent

You need testing evidence that detects injection weaknesses before release:

• SAST for injection patterns (query concatenation, unsafe exec calls).
• DAST for web/API endpoints to find runtime injection behaviors.
• IAST/RASP where available for interpreter-level detection.
• Unit tests around input validation and query parameterization for critical flows.
• Security-focused code review for high-risk services (auth, payments, admin, integrations).

Tie findings to tickets and show closure evidence. Auditors respond well to a clean “finding → owner → fix → verification” chain.

5) Add runtime detection and response hooks

Prevention includes being able to identify and stop attempted injections:

• WAF or API gateway rules where applicable, with tuning to reduce false positives.
• Application logging that captures rejected inputs safely (avoid log injection by encoding/escaping).
• Alerting on repeated injection-like payloads and anomalous error patterns.
• Incident response playbooks for suspected injection exploitation, including containment and credential rotation if needed.

6) Document scope, exceptions, and compensating controls

You will have legacy applications and edge cases. Document:

• Systems in scope and rationale.
• Approved exceptions (time-bounded) with risk acceptance, compensating controls, and an end date.
• Third-party components: if a third party hosts or operates the affected service, require evidence of injection prevention practices via contractual security requirements and periodic attestations.

7) Package evidence for assessment readiness

Turn engineering activity into auditor-ready artifacts:

• Control narrative: what you do and how you know it works.
• Evidence set: last scan results, review samples, ticket closure, and monitoring proof.

If you use Daydream to manage your control library, map SI-10(6) to an owner, an implementation procedure, and recurring evidence artifacts so you can produce consistent audit packets without rebuilding them each cycle. ¹

Required evidence and artifacts to retain

Retain evidence that shows design and operating effectiveness:

Governance

• Control statement for SI-10(6) and RACI (owner, approvers, operators)
• Secure coding standard covering injection prevention
• Engineering policy for code review and security testing requirements
• Exception register with approvals and expiration

Technical configuration

• Approved database access patterns (ORM configs, parameterization examples)
• Secure wrappers for OS execution and template rendering (where applicable)
• CI/CD policy configuration (merge gates, required checks)
• WAF/API gateway configuration baseline (if used)

Operational evidence (repeatable)

• SAST/DAST outputs and trend views (raw exports plus summarized reports)
• Sample pull requests showing injection checks in review
• Remediation tickets with timestamps, code diffs, and verification results
• Monitoring alerts or dashboards for injection attempt signatures
• Post-release incident records (if any) and lessons learned

Common exam/audit questions and hangups

Auditors and assessors commonly press on:

• “Define untrusted.” If you only treat internet inputs as untrusted, expect pushback. Include internal and third-party sources.
• “Show me prevention, not detection.” SAST findings alone do not prove prevention. Show standards, enforced guardrails, and safe-by-default libraries.
• “What about legacy apps?” If you have exceptions, they must be explicit, time-bounded, and tracked with compensating controls.
• “How do you know developers follow the standard?” Provide training records, code review checklists, and gated pipeline controls.
• “What’s your coverage?” You should be able to list in-scope apps and show testing evidence per app or per tier.

Frequent implementation mistakes (and how to avoid them)

1. Relying on client-side validation. Fix: require server-side validation and parameterization; treat client checks as UX only.
2. Treating WAF as the primary control. Fix: WAF is a backstop; auditors want secure coding patterns and SDLC controls.
3. Scanning without remediation discipline. Fix: define SLA expectations internally, assign owners, and require verification evidence on closure.
4. Ignoring non-web injection surfaces. Fix: cover batch jobs, imports, message consumers, admin tools, and integrations.
5. Logging raw malicious payloads. Fix: encode/escape logs and restrict access; log safely to preserve forensics without creating new injection risks.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement, so this page does not cite enforcement outcomes. Practically, injection weaknesses can lead to unauthorized access, data exfiltration, integrity loss, and system compromise. Those outcomes commonly trigger broader regulatory, contractual, and incident notification obligations depending on your environment.

Practical 30/60/90-day execution plan

First 30 days (stabilize and define)

• Name SI-10(6) control owner and publish a one-page control narrative aligned to “Prevent untrusted data injections.” ¹
• Identify top risk applications and integration points (internet-facing, regulated data, privileged actions).
• Publish an injection-safe coding standard with approved/prohibited patterns.
• Turn on baseline SAST rules for injection sinks; start collecting results.

By 60 days (enforce and prove)

• Add CI/CD guardrails: required security checks, required reviewers for high-risk repos, and a standard exception workflow.
• Implement DAST for highest-risk web/API services and document triage/remediation flow.
• Create secure shared libraries/wrappers for common sinks (DB, exec, templates) and require new code to adopt them.
• Build an evidence binder template (policy + scans + samples + tickets).

By 90 days (scale and sustain)

• Expand coverage to remaining in-scope apps and non-web ingestion surfaces (imports, queues, batch).
• Add runtime monitoring for injection attempts and define incident response steps for suspected exploitation.
• Formalize metrics for operational management (open findings by severity, aging, exception counts), without turning them into vanity reporting.
• Use Daydream (or your GRC system) to schedule recurring evidence collection and map SI-10(6) to owners and artifacts so audits become repeatable. ¹

Frequently Asked Questions

Does SI-10(6) require a WAF?

The text requires preventing untrusted data injections, but it does not prescribe a specific technology. A WAF can help as a compensating or supplemental control, but secure coding, testing, and enforcement guardrails are the usual primary evidence set. ¹

What counts as “untrusted data” in internal microservices?

Treat all cross-boundary inputs as untrusted, including service-to-service calls, message queues, and batch imports. Trust is an explicit security property; unless you cryptographically validate content and enforce strict schemas, treat it as untrusted for injection purposes. ¹

How do we handle legacy apps that build SQL strings?

Create a time-bounded exception with compensating controls (WAF rules, stricter monitoring, segmented network access, focused testing) and a remediation plan to move to parameterization. Keep the exception register and evidence of progress for assessment readiness.

What evidence is strongest in an audit for injection prevention?

Assessors typically respond best to a combination of (1) secure coding standards, (2) enforced CI/CD checks, (3) SAST/DAST results with remediation tickets, and (4) code samples showing parameterized queries and safe sink wrappers. ¹

Do third parties fall under SI-10(6)?

If a third party builds, hosts, or operates systems in your authorization boundary or processes your federal data, you need assurance that injection prevention is addressed. Contractual requirements, security questionnaires, and evidence requests can support that assurance, but you still own the control outcome for your system boundary.

How do we avoid drowning engineering in false positives from scanners?

Start with high-signal rules for known injection sinks, tune over time, and require developers to use approved safe libraries that scanners recognize. Track recurring false positives and either tune rules or fix the underlying patterns that trigger noise.

Footnotes

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

2. NIST SP 800-53 Rev. 5