Choose LLM Guard when
- You need many mature input and output scanner types.
- Your team wants to compose sanitization, PII, toxicity, secret, URL, and injection controls.
- A scanner-level validity and risk contract fits your application.
Vendor-authored · configuration and limits disclosed
PrismGuard vs LLM Guard: enforcement contract or scanner toolkit?
LLM Guard is a broad, established collection of input and output scanners. PrismGuard is a layered firewall built around an explicit allow/block decision, domain alignment, and a named audit trail. The published numbers below cover one small law holdout—not universal product superiority.
Decision summary. Choose LLM Guard when you need a broad library of prompt and response scanners for PII, toxicity, secrets, URLs, prompt injection, and related controls. Choose PrismGuard when your primary requirement is a domain-aligned prompt firewall whose allow/block response records which policy layer decided.
What was measured
The authoritative 9 July 2026 run compares PrismGuard CPL with LLM Guard CGL. CPL is not a bare classifier: it uses PrismGuardGate, the law domain, the prism-pi-v1 ONNX artifact, an authored seed and legal overlay, plus selective escalation. CGL is the LLM Guard PromptInjection scanner. Both are measured on the same law-domain holdout and normal cases.
This is feature-asymmetric by design. It supports a claim about that configured pipeline on that artifact. It does not establish that PrismGuard generally detects prompt injection better than LLM Guard, nor does it compare every scanner and sanitization capability in LLM Guard.
| Metric | PrismGuard CPL | LLM Guard CGL |
|---|---|---|
| Law attack holdout | 14/14 blocked | 9/14 blocked |
| Normal holdout | 25/25 allowed | 25/25 allowed |
| Mean request latency | 211.16 ms | 352.73 ms |
| Generative judge escalation | 7.01% | 0% |
The PrismGuard result depends on the documented law configuration. The law artifact must not be enabled blindly for general FAQ, healthcare, or finance traffic.
Architecture and buyer fit
| Factor | LLM Guard | PrismGuard |
|---|---|---|
| Product shape | Extensive input/output scanner toolkit | Layered prompt firewall and output scan path |
| Injection path | Fine-tuned DeBERTa classifier; ONNX option | Rules, taxonomy/fusion, domain ONNX, optional judge |
| Runtime result | Sanitized text, validity, and risk score | Allow/block plus resolution_gate and decision_source |
| Deployment | Self-hosted Python library with composable scanners | Self-hosted Python, ChorusGraph node, licensed HTTP sidecar |
| Coverage strength | Broad sanitization and security scanner catalog | Auditable policy enforcement and shadow-to-calibration workflow |
Choose PrismGuard when
- You require a named policy gate in each decision log.
- You will shadow, collect feedback, and train or calibrate for your domain before enforcement.
- You need the same contract as a Python library, ChorusGraph node, or sidecar.
Limitations and next test
Fourteen attacks are too few for a universal accuracy statement. The published artifact is law-specific; healthcare and finance packs are not production-ready claims. PrismGuard’s paid Team, Business, and $25,000 design-partner pilot prices are pre-validation estimates. LLM Guard also publishes its own infrastructure benchmarks, including ONNX-accelerated paths; those are not directly comparable to this law workload.
Before choosing either product, build a held-out set from your own benign prompts, attacks, languages, tools, and indirect prompt-injection paths. Hold the policy and false-positive budget constant. Measure attack recall, normal allow rate, p95 latency, escalation cost, audit completeness, and operational effort separately.