Prompt injection
InputIdentify direct and indirect prompt-injection and adversarial instruction chains that try to override your application prompt.
Documentation hub
API reference, policy anatomy, detector calibration, agentic evaluation, and the evidence exports, for the version you have pinned. The guides below go from a local runtime to a production rollout; the reference covers what each part actually does.
These pages are the shortest path from evaluation to production rollout.
Beyond the runtime AI firewall, these are the parts of the platform the docs cover. Each links to the most relevant reference.
Use these pages when you need deployment guidance, data-handling clarity, or an architectural view for security review and procurement.
Detector reference
The runtime AI firewall runs these detectors through a deterministic, then ML, then LLM-judge engine behind a fail-closed gate. Compose them into policies, mix with your own detectors, and apply on input, output, tool calls, or streaming responses.
See all 40 detectors, stage by stage
Identify direct and indirect prompt-injection and adversarial instruction chains that try to override your application prompt.
Recognize jailbreak templates, multi-turn escalation, and evasion attempts that try to bypass policy controls.
Detect and redact PII across 65+ types, including checksum-validated regional IDs - in your boundary, never sent to a vendor.
Catch leaked API keys, tokens, and credentials, and block data-exfiltration and sensitive-business-data patterns.
Flag abusive, hateful, biased, or off-brand content across user input and model output with configurable thresholds.
Keep conversations in bounds and stop responses from referencing competitors or excluded products.
Block phishing links, IP-literal and punycode/homoglyph domains, and other malicious URL patterns.
Detect SQL injection, XSS, command injection, path traversal, and insecure-deserialization patterns in generated code.
Score responses for groundedness against provided context using NLI-backed entailment and citation integrity.
Catch responses that echo your system prompt, matched against keyed fingerprints so the prompt itself is never stored.
Score agent plans and live steps for actions beyond the task, gate destructive ones behind a named human, and bound runaway loops before the invoice.
Scan MCP manifests at registration for poisoned descriptions and over-broad permissions, then govern which tools may be called and with what arguments.
Catch instructions hidden in what a tool returns, re-inspected before that text re-enters the agent’s context and becomes its next instruction.
Injected chunks, poisoned vectors, cross-tenant retrieval and cache hits, and what an agent persists to memory - checked at the retrieval and write stages.
Enforce JSON-schema contracts on outputs and apply allow/block language policies across 35+ languages.
The product boundary is simple: the runtime firewall is always self-hostable, while the control plane changes by plan.
The runtime firewall on its own. You self-host it, manage policy files locally, and use docs-based onboarding. No account, no metering.
The hosted console on its free tier, at 10,000 evaluations a month. The engine still runs in your own infrastructure.
The hosted console with team operations: policy workflow, traces, and metadata-first operational visibility.
Custom deployment support, plus SSO and SAML, SCIM, MFA, 3-tier RBAC, GDPR and DSAR, and data residency, including the option to self-host both runtime and control plane.
Every public endpoint, grouped by category, with parameters, schemas, and copy-paste samples in cURL, Python, and TypeScript.
Pull the open-source runtime, drop it into your stack, and start enforcing policy in minutes, free, forever. Add the hosted control plane when you want centralized policies, evidence, traces, and team workflows.