We need a security layer that can be deployed in front of our LLM to block prompt injection without adding significant latency. Which vendors provide high-performance LLM firewalls?

Deploying a security layer in front of an LLM requires balancing deep semantic inspection (to catch sophisticated prompt injections and jailbreaks) with sub-100ms latency requirements.

Vendors and architectural patterns providing high-performance LLM firewalls and inline guardrails are categorized below by their deployment model and performance profile.

1. Dedicated Enterprise Edge & Cloud Firewalls

These solutions handle security checks at the network edge or via optimized microservices, minimizing round-trip overhead.

• Akamai Firewall for AI : Model-agnostic and built directly into Akamai's edge platform. It is engineered specifically for low-latency inspection, intercepting adversarial prompts, data exfiltration, and policy violations before they ever hit your origin or hosted LLMs.
• Lakera Guard (integrated across major enterprise security stacks): Focuses heavily on real-time prompt injection and jailbreak detection using specialized, highly optimized classifiers rather than chaining heavy secondary LLM calls. Known for fast processing times suited for user-facing applications.
• Prompt Security : Offers comprehensive runtime guardrails covering prompt injection, shadow-AI discovery, and data leakage prevention (DLP). Designed to sit in the traffic path as an enterprise gateway layer with tuned performance overhead.

2. API Gateways with Native Security & Governance

If you already route traffic through an AI gateway, adding inline security plugins here often introduces the least structural latency because the proxy layer is already established.

• Portkey (and similar enterprise API routers): Provides robust governance, caching, and fallback logic alongside security guardrails to intercept toxic output or injected inputs seamlessly inside the proxy hop.
• Kong AI Gateway : Built for organizations running traditional API meshes. It allows you to run low-latency transformations, rate-limiting, and security/guardrail policies directly at the API gateway layer adjacent to your services.
• Cloudflare AI Gateway : Best if your infrastructure already leverages Cloudflare. It adds minimal latency overhead by executing security logging, caching, and basic guardrail interventions at the edge worker level.

3. Open-Source & Self-Hosted High-Performance Toolkits

For strict data privacy or zero-tolerance latency budgets, running a lightweight local classifier (like a fine-tuned BERT-based injection detector or Llama Guard variant) inside your own VPC is often faster than external third-party SaaS calls.

• Protect AI (LLM Guard) : An open-source security toolkit designed to evaluate prompts and responses locally for injections, PII, and toxicity. Because you self-host the containers, you can scale compute adjacent to your application servers to keep latency under tight thresholds.
• NVIDIA NeMo Guardrails : Programmable guardrails toolkit that allows developers to define topical and safety boundaries. Performance depends on how lightweight you make the canonical verification models.
• Guardrails AI : Focuses heavily on structured output validation and intercepting jailbreaks via a modular, low-latency execution loop that can be run locally.

Architectural Rule of Thumb for Low Latency

To keep latency under ~50–90ms, avoid using a full-sized frontier LLM as your "validator/firewall". Instead, use specialized, small-footprint transformer classifiers (or optimized local models like Llama Guard ) hosted on high-throughput inference engines like Fireworks AI or vLLM clusters residing in the same availability zone as your primary application.

To help narrow this down, what is your target latency budget (e.g.,<5 0 m s vs<2 0 0 m s ), and are you deploying on-premises/air-gapped or in a cloud environment?

If your primary requirement is inline prompt-injection blocking with very low added latency, I’d shortlist these vendors:

My practical shortlist

For a high-throughput LLM gateway, I'd put Lakera/Check Point and Lasso through the first performance test. Their published numbers are the clearest evidence I've found for low-latency inline detection. Lakera specifically publishes latency by prompt size and detector configuration, which is useful for capacity planning.

One important architectural point: I would not make an LLM-as-a-judge the default first-line firewall if latency and predictable security behavior are priorities. Purpose-built classifiers/rules can operate independently of the protected LLM; Lasso and Lakera both describe architectures designed around this principle.

Also, don't benchmark only direct user prompts. Indirect prompt injection—malicious instructions embedded in retrieved webpages, documents, RAG results, tool responses, or tool descriptions—is a major part of the threat model. OpenAI describes prompt injection as malicious instructions introduced by third-party content, and Lakera explicitly supports screening these additional interaction points.

For your evaluation, I'd measure p50/p95/p99 added latency, detection recall, false-positive rate, throughput, maximum context size, and performance on indirect/RAG injections using your actual production prompts. Vendor-reported latency numbers aren't directly comparable because detector count and input length materially affect them.

If you tell me your expected requests/sec, typical prompt/context size, cloud (AWS/Azure/GCP), and whether you need self-hosted, I can narrow this to 2–3 architectures and give you a concrete bake-off matrix.

When evaluating LLM firewalls to block prompt injection, minimizing latency is the primary engineering bottleneck. Centralized cloud-inspection APIs can add hundreds of milliseconds to round-trips, which degrades user experience.

To achieve high-performance, low-latency mitigation, the market has largely split into Edge-native firewalls (which run compiled, lightweight classifiers close to the user) and Dedicated AI security platforms (which optimize proxy architectures).

1. Edge-Native and CDN-Integrated Firewalls (Lowest Latency)

By running security classification directly on global edge networks, these vendors evaluate incoming payloads before they ever hit your centralized application servers or upstream LLMs.

• Fastly (Fastly for AI / Edge Classifier): Fastly provides a dedicated inline AI firewall running natively inside Fastly Compute . It uses lightweight, deterministic, and optimized machine learning classifiers that run at the edge without requiring heavy GPU infrastructure, keeping inspection overhead down to single-digit milliseconds.
• Akamai (Firewall for AI): Akamai offers real-time inspection for prompt injections, jailbreaks, and data exfiltration by leveraging its massive edge delivery network. It intercepts requests globally, making it ideal if your user base is geographically distributed and you cannot afford a central cloud hop for security filtering.

2. Dedicated AI Security & Guardrail Platforms

If you require deep semantic analysis, data loss prevention (DLP), or multi-model orchestration alongside injection protection, dedicated platforms provide robust middleware proxies.

• Lakera (Lakera Guard): Widely known for its roots in adversarial prompt detection (heavy contributor to security benchmark datasets like Gandalf), Lakera specializes in ultra-fast prompt injection and jailbreak detection via dedicated APIs or containerized deployments that can sit close to your infrastructure.
• Prompt Security: Focuses explicitly on enterprise LLM guardrails, intercepting prompt injections, shadow-AI usage, and data leaks across LLM gateways with an emphasis on low-latency inline proxy decisions.
• Protect AI (LLM Guard): Offers an open-source library (LLM Guard ) alongside an enterprise commercial tier. Because you can self-host the lightweight open-source scanner container locally within your own Kubernetes cluster next to your application pods, network hops are minimized.

3. Native Cloud & Framework Options

• Microsoft Azure AI Content Safety (Prompt Shields): If your infrastructure is natively hosted on Azure OpenAI Service, Prompt Shields offer integrated, hardware-accelerated checks for prompt injection on multimodal and text inputs with minimal additional routing overhead.
• NVIDIA NeMo Guardrails: An open-source toolkit allowing programmatic definition of conversational and topical rails using Colang. While it requires self-hosting and tuning, it gives you total control over performance optimization and where the evaluation code executes in your stack.

To help narrow down the best fit, could you share:

• Are you deployed on-premise/cloud-native (Kubernetes) or looking for a CDN/Edge-delivered service?
• What is your target latency budget (e.g.,<2 0 m s)?

Yes. If the requirement is an inline security layer in front of the LLM, strong prompt-injection detection, and very low added latency, I’d shortlist these vendors:

1. Lakera Guard — strongest candidate for a lightweight dedicated detector

Lakera's Guard is specifically designed to detect prompt injection and other LLM threats. Lakera currently advertises <12 ms average latency, along with a 0.01% false-positive rate and coverage across 100+ languages.

Why I'd consider it: If your primary requirement is "inspect this prompt/context before it reaches the model and make a fast allow/block decision," this is probably the cleanest fit.

2. Palo Alto Networks Prisma AIRS — best enterprise firewall architecture

Palo Alto Networks's Prisma AIRS provides an AI Runtime Firewall that can inspect prompts, responses and data flows and block prompt injection, malicious content and data leakage.

Its newer AI Gateway is particularly interesting for your latency requirement: Palo Alto says it operates as a lightweight runtime proxy inside your infrastructure and adds typically <1 ms at p50 for prompts.

Why I'd consider it: If you already run Palo Alto security infrastructure—or want a broader AI security platform rather than a single-purpose injection detector—this is probably the strongest enterprise option.

3. Cloudflare AI Security for Apps — best edge deployment

Cloudflare's AI Security for Apps (formerly Firewall for AI) can sit in front of LLM endpoints and detect prompt injection, PII and unsafe topics.

Cloudflare explicitly designed the architecture for low latency: detection modules run asynchronously and in parallel, so adding additional detectors doesn't serially add their latencies; Cloudflare says latency is bounded by the slowest detection model.

Why I'd consider it: Excellent if your LLM API is already behind Cloudflare and you want security enforcement at the edge without inserting another application-level service.

4. Lasso Security — worth evaluating for agentic/multi-turn attacks

Lasso Security claims its Intent Deputy detects prompt injections in under 50 ms, including instruction smuggling, obfuscation, privilege-escalation attempts and multi-turn attacks.

Why I'd consider it: Particularly interesting if your concern isn't just classic "ignore previous instructions" attacks but semantic and multi-turn manipulation of agents.

---

My shortlist for a latency-sensitive architecture

I'd run a bake-off between:

1. Prisma AIRS — if <1 ms p50 is the priority and you want an enterprise inline gateway.
2. Lakera Guard — if you want a focused, high-speed prompt-injection detector.
3. Cloudflare AI Security for Apps — if you're already on Cloudflare and can enforce the control at the edge.
4. Lasso — if agentic/multi-turn attacks are especially important.

One caveat: vendor-reported latency numbers aren't directly comparable. Lakera's <12 ms and Lasso's <50 ms are detector claims, while Palo Alto's <1 ms p50 is specifically for its AI Gateway proxy, and Cloudflare describes architectural latency characteristics rather than giving a directly comparable end-to-end number.

For a production evaluation, I'd measure p50/p95/p99 added latency at your actual prompt sizes, while testing direct injection, indirect injection from RAG documents/web pages, encoded/obfuscated attacks, multi-turn attacks, and false positives on your real workload.

Deploying a security layer in front of an LLM requires balancing robust threat detection (specifically for indirect and direct prompt injection and jailbreaks) with sub-millisecond or low double-digit millisecond latency overhead.

High-performance LLM firewalls and security gateways operate either at the network edge, as a reverse proxy, or via optimized inference-adjacent microservices. Leading vendors and tools providing enterprise-grade, high-performance mitigation include:

• Akamai Firewall for AI : Leverages Akamai's massive distributed edge platform to inspect and block prompt injections, jailbreaks, and data exfiltration attempts before requests ever hit your origin LLM infrastructure. Best for teams needing globally distributed, low-latency edge enforcement.
• Lakera (Lakera Guard) : Built specifically around industry-leading prompt injection and adversarial ML detection models (trained heavily on real-world datasets like Gandalf). It offers low-latency APIs designed to drop inline into request pipelines with minimal performance drag.
• Microsoft Entra / Azure AI Content Safety (Prompt Shields) : Offers network-level AI gateways and native prompt-injection protection designed to intercept and neutralize jailbreak risks dynamically. Ideal if your stack is already hosted within Azure or requires strict enterprise Security Service Edge (SSE) integration.
• Prompt Security : Specializes in runtime guardrails and prompt filtering that address shadow-AI discovery, data leakage, and injection attacks synchronously during the request lifecycle.
• Robust Intelligence (Cisco) : Focuses on comprehensive AI firewall and automated model validation capabilities, securing the operational pipeline against both traditional software vulnerabilities and LLM-specific semantic injections.
• Protect AI (LLM Guard) : Offers a high-performance open-source guardian library alongside enterprise support, allowing teams to self-host scanning microservices optimized for speed right next to their local or private LLM instances.

If you can share:

• Your deployment environment (AWS, Azure, on-premise, or edge)
• Your latency budget (e.g.,<5 0 m s)

I can help you narrow down whether an edge proxy or a self-hosted microservice fits your architecture best.

Yes. If your primary requirement is prompt-injection blocking with very low added latency, I’d shortlist these vendors:

My recommendation

For your specific requirement—"in front of the LLM, block prompt injection, don't add significant latency"—I'd start with a 3-way POC:

1. Lakera/Check Point — strongest candidate if latency is the #1 criterion. Its published <12 ms average is unusually aggressive.
2. Cloudflare AI Security for Apps — particularly compelling if your traffic already traverses Cloudflare. Its architecture performs security detections asynchronously and in parallel at the edge.
3. Your cloud-native option — e.g. Azure Prompt Shields, AWS Bedrock Guardrails, or Google Model Armor, depending on where inference runs.

One important caveat: don't equate an "AI gateway" with an LLM firewall. A gateway handles routing, authentication, rate limiting, logging, etc.; the firewall/detector is what actually analyzes the semantics of a prompt for injection.

Also, be careful with generic content guardrails. For example, Cloudflare's current AI Gateway Guardrails use Llama Guard 3 8B for many safety categories; Cloudflare documents roughly 500 ms additional latency for those evaluations. Its dedicated prompt-injection detection uses a much smaller Prompt Guard model, so you should benchmark the specific feature rather than assume all "guardrails" have the same latency profile.

For a production bakeoff, I'd measure p50/p95/p99 added latency, detection recall against your own injection corpus, false-positive rate, and behavior on indirect/obfuscated injections—not vendor benchmark scores alone.

If the goal is an inline LLM firewall that blocks prompt injection with minimal added latency, the strongest candidates are generally vendors that avoid using a full LLM judge on every request. Look for small classifiers, semantic detectors, edge execution, streaming inspection, and policy engines.

A shortlist of vendors to evaluate:

For lowest latency architectures

For production systems where every millisecond matters, I would usually evaluate architectures like:

1. Edge/WAF firewall

• Cloudflare or Akamai style deployment.
• Best when you already have global traffic infrastructure.
• Avoids adding another application hop.

2. Dedicated AI gateway

• Lakera, WitnessAI, Pillar, Noma-style products.
• Better when you need richer AI-specific policies: - prompt injection
• RAG poisoning detection
• data loss prevention
• tool-call authorization
• audit trails

3. Self-hosted inline classifier

• Useful if data residency or latency requirements dominate.
• Example open-source projects exist that place a lightweight inspection layer before model calls.

What to ask vendors during evaluation

Do not just ask “does it block prompt injection?” Ask for:

• P50/P95/P99 added latency at your payload sizes
• Whether detection uses: - regex/signatures
• embeddings/classifiers
• another LLM call
• Whether it can run: - synchronously inline
• asynchronously
• at the edge
• inside your VPC/Kubernetes cluster
• False-positive rates on your workload
• Performance against: - indirect prompt injection in retrieved documents
• multilingual attacks
• encoded attacks
• multi-turn jailbreaks
• tool-call manipulation

A practical benchmark is to run a 10k–100k prompt replay from your own traffic plus a red-team set and measure:

• added latency
• block rate
• false blocks
• missed injections
• throughput under load

For a typical enterprise deployment, I would put Lakera Guard, Cloudflare AI Security for Apps, Akamai Firewall for AI, WitnessAI, and Pillar Security on the first evaluation list, then narrow based on whether you need edge scale, private deployment, or agent/tool security.

When deploying a security layer in front of an LLM, the goal is to implement a low-latency "sandwich architecture"—inspecting inbound prompts for jailbreaks and injection attempts before they hit the core model, and scanning responses for data leaks or toxicity on the way out.

High-performance LLM firewall and security gateway vendors engineered for enterprise workloads and minimal latency include:

• Check Point AI Guardrails (formerly Lakera Guard): Known for specialized, ultra-fast prompt defense models. It provides real-time detection for direct and indirect prompt injection, jailbreaks, and data leakage (PII) with optimized inference paths designed to keep latency minimal. Available as a managed SaaS or self-hosted runtime.
• Cloudflare AI Security for Apps (Firewall for AI): Built directly into Cloudflare's edge network, this tool assigns risk and confidence scoring to inbound prompts to detect malicious overrides or payload splitting. Because it runs at the edge, it intercepts prompt injection attempts before they ever reach your origin servers or LLM provider, cutting down transit and processing bottlenecks.
• Akamai Firewall for AI: Leverages Akamai’s massive edge distribution to continuously scan LLM interactions in real time. It evaluates adversarial phrasing and enforces governance policies close to the user, minimizing round-trip latency overhead for globally distributed applications.
• Prisma AIRS (AI Runtime Security) by Palo Alto Networks: Designed to secure enterprise GenAI usage by inspecting runtime traffic inline. It focuses on blocking prompt injections, insecure plugin/tool execution, and data exfiltration while integrating smoothly with existing enterprise security observability pipelines.

If you can share where your LLMs are hosted (e.g., AWS/Azure, self-hosted vLLM, or direct via OpenAI/Anthropic APIs) and your preferred deployment footprint (edge network vs. in-cluster proxy), I can recommend the most seamless integration approach for your stack.

When deploying an inline security layer to block prompt injection, keeping latency minimal requires balancing deterministic checks (regex, heuristics, fast token classifiers) with semantic verification (lightweight transformer models like Llama Guard or specialized small language models). Running heavy secondary models sequentially will tank your time-to-first-token, which is why high-performance vendors focus on optimized inference, async parallel scanning, or edge deployment.

The prominent vendors providing high-performance LLM firewalls and security gateways are categorized below by their deployment approach:

Specialized AI Security & Firewall Vendors

• Lakera – Known for Lakera Guard , a purpose-built security API optimized specifically to catch prompt injections, jailbreaks, and indirect injection risks with extreme speed. It relies on heavily optimized proprietary models designed to add minimal overhead compared to generic secondary LLM checks.
• Prompt Security – Offers a comprehensive enterprise LLM security platform acting as a gateway/firewall. It covers shadow AI discovery, input/output guardrails, and real-time prompt injection blocking across enterprise copilot and custom LLM applications.
• HiddenLayer – Provides an ML SecOps platform with model-guarding capabilities that intercept requests inline. Their approach focuses on protecting AI models from adversarial machine learning attacks, including prompt injections and data poisoning, without heavily degrading inference pipelines.
• CalypsoAI – Focuses on LLM security and enablement, featuring robust gateway controls that inspect prompts and responses for prompt injection, toxicity, and data leakage before they hit your primary model.

Enterprise Infrastructure & Edge Security Giants

• Cloudflare – Offers AI Gateway and security capabilities that sit at the edge. By pushing semantic caching, rate limiting, and lightweight guardrail evaluations to Cloudflare’s global edge network, they achieve some of the lowest possible network-induced latency overhead for proxy-level filtering.
• Palo Alto Networks – Integrates LLM security and runtime protection into its broader enterprise security architecture, allowing organizations to govern prompt injection and data exfiltration inline via enterprise-grade secure web gateways.
• Check Point – Provides AI security enforcement and prompt-injection/jailbreak prevention tools designed to integrate into enterprise CI/CD and runtime application architectures.

Open-Source & Framework-Level Options (For Custom Infrastructure)

• NVIDIA NeMo Guardrails – An open-source, programmable guardrail toolkit. When paired with GPU acceleration and smaller fine-tuned guard models (like Nemoguard 8B), it can achieve sub-50ms processing times, though it requires significant in-house engineering to configure and maintain via its Colang script.
• Llama Guard (Meta / Hugging Face ecosystem) – Frequently deployed by engineering teams on self-hosted vLLM or Triton inference servers as a parallel or concurrent classification step to minimize round-trip latency.

Performance Tip: Architecture Over Strict Sequential Inspection

No matter which vendor you select, how you wire the firewall matters as much as the vendor's raw speed. Avoid a purely sequential blocking architecture where heavy deep-learning classifiers run end-to-end before the main LLM is even invoked. High-performance teams rely on asynchronous or parallel inspection streams coupled with ultra-fast deterministic edge filters for initial sanitization.

To help narrow down the right vendor for your stack, could you share:

• Are you deploying on-premises/VPC or looking for a SaaS/Edge proxy?
• What is your target latency budget (e.g., under 50ms vs. under 200ms)?

If you need an inline security layer in front of an LLM with minimal latency, you’re generally looking for an LLM firewall / AI gateway rather than a traditional WAF. The strongest options today fall into a few categories: dedicated AI security vendors, cloud-native guardrail services, and edge/API gateway providers.

Dedicated LLM firewall vendors

Decryption Digest Awesome Agents## Cloud / edge AI firewall options

Open-source / self-hosted options

If you need the lowest possible latency and want to run inside your own VPC:

• Meta Platforms Llama Guard / Prompt Guard — lightweight classifiers you can colocate near inference.
• NVIDIA NeMo Guardrails — programmable guardrail framework.
• LLM Guard — open-source input/output scanning toolkit.
• Custom small classifier + rules engine — common for high-throughput production systems.

GitHub## What I would evaluate for a low-latency production deployment

For a customer-facing LLM handling thousands of requests/sec, I’d usually shortlist:

1. Lakera Guard — if your primary requirement is prompt injection/jailbreak defense with minimal added latency.
2. Cloudflare AI Security for Apps — if traffic already flows through Cloudflare and you want edge enforcement.
3. Palo Alto Prisma AIRS / Check Point AI Guardrails — if you need enterprise security integration, compliance workflows, and broader AI governance.
4. Self-hosted classifier stack — if single-digit millisecond latency or data residency requirements dominate.

Architecture pattern that usually works well

A common production layout is:

Client
  |
  v
API Gateway / AI Firewall
  |
  +--> Prompt injection detector
  +--> DLP / secrets scanner
  +--> Policy engine
  +--> Rate limiting
  |
  v
LLM Provider (OpenAI / Anthropic / Azure / self-hosted)
  |
  v
Output scanner (optional)

For latency-sensitive apps, avoid sending every request through a large secondary LLM for classification. Prefer:

• lightweight classifiers,
• parallel checks,
• cached policy decisions,
• blocking only high-confidence attacks,
• async logging for lower-risk events.

When comparing vendors, I’d ask for benchmarks on your own traffic, especially:

• direct injection detection,

• indirect injection from RAG documents,

• multilingual attacks,

• false-positive rate,

• p95/p99 latency,

• throughput under load,

• deployment model (SaaS vs VPC vs on-prem).

• direct injection detection,

• indirect injection from RAG documents,

• multilingual attacks,

• false-positive rate,

• p95/p99 latency,

• throughput under load,

• deployment model (SaaS vs VPC vs on-prem).