Ship AI features—minus the backdoors
Guard prompts, tools, and outputs using controls mapped to OWASP LLM Top 10 (2025) so product and security speak the same language.
Who it's for: AI Platform Leads · Product Engineers · Security Architects
What we defend
Prompt injection
Isolate untrusted input; sanitize and constrain.
Output injection
Enforce schemas and validate outputs before they reach sinks.
Tool/agent abuse
Allowlist tools with scoped permissions and rate limits.
Risky retrieval
Restrict external sources; redact secrets/PII.
Auditability
Log prompts, tool usage, policy hits for incident review.
Tool-call controls (JS pseudocode)
Tool/agent abuse protection
Transform unrestricted agent access into policy-gated, validated tool usage.
// BEFORE: any tool, any time
const result = agent.run(userInput);// AFTER: policy-gated tools + output schema
const result = agent.run(
sanitize(userInput),
{ tools: allowlist(['search','db-read']),
outputSchema: invoiceSchema,
rateLimit: 'per-user' }
);How it works
Define
Define policies as code (prompt isolation, output schemas, tool scopes).
Scan
Scan LLM flows in code and CI to detect missing controls.
Enforce
Enforce at runtime with guard middleware and checks.
Report
Report policy events for audits and post-mortems.
Why it matters (proof)
OWASP LLM Top 10 (2025)
Codifies concrete classes of AI risks and mitigations → guardrails aren't optional for production AI.
Veracode/IBM context
Long remediation cycles and high breach costs make prevention and fast fixes the winning strategy.
FAQs
Do we need a specific LLM vendor?
No—controls are model-agnostic.
Will this slow responses?
Policies are lightweight; apply where risk warrants (e.g., tool-enabled steps).
Can we roll our own rules?
Yes—policies are editable in-repo.