We are worried about prompt injection attacks exposing our system prompts. Who offers "AI firewalls" for input sanitization?

Yes. The market usually calls these AI firewalls, LLM firewalls, AI guardrails, or runtime AI security products. Several vendors specifically address prompt injection and system-prompt/data leakage.

• lakera.ai — Lakera Guard. Detects direct and indirect prompt injection, jailbreaks, and attempts to extract system prompts; it can block, redact, or warn before content reaches the model. It also scans retrieved documents, URLs, and other external content.
• checkpoint.com. Provides an API-based runtime layer that screens user input, RAG/reference content, tool responses and tool descriptions. Its controls include prompt-attack detection and explicit system-prompt/data-leakage prevention.
• protectai.com. Focuses broadly on securing the ML/GenAI lifecycle, including model and AI application security. It's worth evaluating if you need more than an input filter.
• hiddenlayer.com. Another enterprise AI-security vendor, with emphasis on protecting AI models and AI infrastructure rather than just prompt filtering.

One important distinction

I wouldn't treat an AI firewall as a guarantee that your system prompt can't leak. Input sanitization alone is insufficient, particularly for agents and RAG systems: an attacker can put an injection inside a webpage, PDF, retrieved document, tool response, or MCP metadata.

A robust architecture generally looks like:

untrusted input → injection detector/firewall → authorization/data-access controls → LLM → output/DLP filter

And the system prompt itself should not contain secrets. The firewall should be an additional layer, not the mechanism on which confidentiality depends. Check Point/Lakera's documentation explicitly recommends combining prompt defenses with secure system prompts and separate data-leakage controls.

If you're evaluating these for production, I can also give you a vendor comparison matrix (Lakera vs. Check Point vs. Protect AI vs. HiddenLayer, including API integration, self-hosting, latency, RAG/agent coverage, and pricing model).

To protect your system prompts and prevent prompt injection, the market has largely coalesced around AI firewalls, LLM gateways , and guardrail libraries . These tools intercept user inputs, sanitize or flag malicious instructions (like "ignore previous instructions"), and block data exfiltration before the payload ever reaches your primary model.

The ecosystem of vendors and tools offering input sanitization and firewall capabilities falls into distinct categories:

1. Specialized Commercial & Managed AI Firewalls (SaaS / API)

• • Lakera Guard (Check Point): Widely considered a heavyweight specifically for prompt-injection and jailbreak detection . Born out of the famous Gandalf AI security game, Lakera excels at catching zero-day and complex semantic injections with minimal latency (~50ms). It is available via API or integrated into gateways like LiteLLM.
• • HiddenLayer: Provides model-layer security and an AI firewall approach that protects enterprise LLMs from adversarial manipulation, input attacks, and unauthorized behavior.
• • Arthur Shield: Operates as a compliance and security firewall, monitoring inputs and outputs for toxic content, prompt injections, and data leakage.
• • Aporia: Combines observability with real-time guardrails to catch adversarial inputs and monitor agent behavior inline.

2. Hyperscaler & Native Cloud Guardrails

If you are already hosted within a major cloud ecosystem, native security firewalls can be turned on directly inside your infrastructure:

• • AWS Bedrock Guardrails: Allows you to filter user inputs and model responses for prompt attacks, PII, and custom denied topics directly inside Amazon Bedrock.
• • Azure AI Prompt Shields / Content Safety: Microsoft’s native defense layer explicitly designed to detect and block user prompt injections and jailbreak attacks in real-time.
• • Google Cloud Model Armor: Google's managed security layer to sanitize prompts and guard against jailbreaks and sensitive data exposure.

3. Open-Source & Self-Hosted Frameworks (In-App Libraries)

If your data sovereignty requirements mean prompts cannot leave your network or cluster, you can embed these open-source firewalls/validators directly into your application pipeline:

• • LLM Guard (Protect AI): A massive open-source toolkit acting as a true WAF (Web Application Firewall) for LLMs, featuring dozens of specialized scanners for prompt injection, secrets, PII, and toxicity.
• • NVIDIA NeMo Guardrails: A programmable toolkit that lets you define safety boundaries, dialog flows, and input-validation constraints using Colang.
• • Guardrails AI: A modular, validator-hub approach where you stitch together specific checks for input validation, jailbreak detection, and output correctness within your app process.
• • Llama Guard (Meta): An open-weight "model-as-judge" classifier that you can host yourself to evaluate incoming user prompts and outgoing responses against a safety taxonomy.

To help narrow down which path makes sense for you, could you share:

• Are you deploying on-prem/self-hosted or using cloud-managed models (like OpenAI, Anthropic, or AWS)?
• Is your primary concern direct user prompts or indirect prompt injection (e.g., your LLM reading untrusted external web pages/emails)?

Protecting your system prompts from extraction via direct and indirect prompt injection requires a "sandwich architecture"—where a proxy or firewall intercepts user inputs and cleans them before they touch your core LLM, and then scans the output on the way back.

A wide ecosystem of commercial SaaS providers, cloud giants, and open-source toolkits offer specialized "AI firewalls" and runtime guardrails for input sanitization:

1. Dedicated Commercial AI Firewalls & Security APIs

• Lakera Guard (Check Point): Widely recognized as a powerhouse specifically for prompt injection and jailbreak detection. Backed by massive adversarial datasets (they created the famous Gandalf AI security game), it offers low-latency API checks optimized for catching sophisticated zero-day injections. (Note: Lakera was acquired by Check Point, meaning it is increasingly integrated into the Check Point Infinity ecosystem).
• Prompt Security: A specialized runtime security platform focused on stopping prompt injections, data exfiltration, and shadow AI usage across enterprise applications.
• Akamai Firewall for AI: A network-edge and application-layer solution by Akamai designed to sit in front of LLM chatbots, inspecting traffic for jailbreaks and prompt injections in real time.
• Cloudflare AI Gateway / AI Security: Cloudflare offers visibility, rate-limiting, and security controls directly at the proxy layer where your LLM requests are routed.

2. Hyperscaler & Native Cloud Guardrails

If you are already hosted within a specific cloud ecosystem, native enterprise guardrails can sanitize inputs natively without adding external third-party API dependencies:

• Amazon Bedrock Guardrails: Allows you to block malicious user inputs, filter harmful content, and sanitize context windows on AWS Bedrock.
• Azure AI Content Safety: Offers jailbreak and prompt-injection blocklists and text-filtering controls integrated directly into Microsoft Azure.
• Google Cloud / Vertex AI Model Armor: Provides integrated security filters and defense-in-depth layers for prompt-injection detection on Google's infrastructure.

3. Open-Source & Programmable Frameworks (Self-Hosted)

If your data sovereignty requirements mean prompts cannot leave your own cluster or private VPC, you can self-host open-source guardrail layers:

• NVIDIA NeMo Guardrails: An open-source toolkit by NVIDIA that lets you programmatically define conversational rails, input-checking flows, and topical boundaries.
• LLM Guard (Protect AI): A comprehensive open-source tool for scanning inputs (prompt injections, secrets, PII) and filtering outputs, which can be run locally as a microservice container.
• Guardrails AI: An open-source Python framework that uses structural validators to ensure inputs and outputs conform to strict safety and structural rules.
• Meta Llama Guard / Prompt Guard: Open-weights safety classifiers provided by Meta that can be deployed on a smaller, dedicated local model to evaluate incoming prompts before sending them to your primary LLM.

Are you looking for a fully managed SaaS API (quickest setup, data leaves your environment) or a self-hosted open-source tool (keeps data fully in-house)? Let me know your infrastructure setup and I can help you narrow down the best integration path.

Yes. The main vendors offering “AI firewall” / prompt-defense layers include:

• Lakera Guard — detects/blocks direct and indirect prompt injection and can prevent system-prompt leakage.
• Cloudflare AI Security for Apps — formerly Firewall for AI; detects prompt injection, PII, and unsafe topics at the edge.
• Palo Alto Networks Prisma AIRS — enterprise AI runtime firewall covering prompt injection, data leakage, malicious content, and agents.
• Check Point AI Guardrails — prompt-defense layer that detects attacks and can block/warn, including attacks embedded in retrieved documents and tool responses.

For specifically preventing system-prompt extraction, I’d shortlist Lakera, Cloudflare, and Prisma AIRS and evaluate them on indirect-injection detection, latency, false positives, and whether they inspect RAG/tool/MCP content—not just the user’s raw prompt.

Yes. The market has matured quite a bit, although vendors use overlapping terms such as AI firewall, LLM firewall, AI gateway, guardrails, and AI runtime security.

For your specific concern—blocking prompt injection before it reaches the model, including attempts to extract system prompts—I'd shortlist these:

• lakera.ai — Lakera Guard. Probably one of the most directly relevant products. It analyzes inputs for direct/indirect prompt injection, jailbreaks, and obfuscated attacks, and can block, redact, or warn. It also scans content coming from documents, URLs, and other external context.
• developers.cloudflare.com. Formerly Firewall for AI. It sits at the application/WAF layer and provides a prompt-injection score that you can use in blocking rules. It explicitly covers attempts to make an LLM reveal its system prompt.
• paloaltonetworks.com. A broader enterprise AI-security platform. Its runtime-security layer inspects prompts, responses, and data flows and blocks prompt injection, malicious URLs, poisoned content, etc. It also has agent/tool security and DLP capabilities.
• akamai.com. An edge-level LLM firewall aimed at prompt injection, jailbreaks, data exfiltration, and harmful output.
• Check Point AI Guardrails. Its Prompt Defense capability specifically detects prompt attacks in user input and in content consumed by agents, such as poisoned documents, web pages, tool responses, and tool descriptions.

One important caveat

I wouldn't think of this as simply "sanitize the user's input and we're safe." Prompt injection isn't reliably solved by stripping suspicious phrases. In an agentic application, malicious instructions can arrive through retrieved documents, webpages, tool results, MCP servers, or other supposedly trusted context.

For protecting system-prompt confidentiality, I'd use a layered architecture:

User/input → AI firewall/guardrail → retrieval/tool-content scanning → LLM → output/DLP guardrail → application

And independently enforce least-privilege tool access, because even a very good injection detector can miss a novel attack.

If you're evaluating vendors, Lakera, Cloudflare, and Prisma AIRS are probably the three I'd put into an initial bake-off. The key test isn't just "does it detect obvious jailbreaks?" but whether it catches direct + indirect + obfuscated prompt injection, while keeping false positives low on your actual workload.

Several enterprise security vendors and specialized platforms offer "AI firewalls," runtime guardrails, and input sanitization layers specifically designed to block prompt injections and prevent system prompt leakage.

The prominent providers fall into a few clear categories:

• Dedicated AI Security Firewalls
  • Lakera Guard : Offers a real-time API-based security firewall specifically built to screen and block prompt injections, jailbreaks, and toxic inputs before they hit your LLM.
  • Akamai Firewall for AI : A model-agnostic edge security solution that monitors and filters real-time inputs and outputs to prevent data exfiltration and adversarial manipulation.
• Cloud & Enterprise Platform Guardrails
  • Azure AI Content Safety : Integrated into Microsoft’s ecosystem, it delivers robust text and image moderation, detecting jailbreak risks and malicious user inputs through enterprise APIs.
  • Galileo Guardrail : Unifies LLM observability, evaluation, and real-time runtime interventions/guardrails to intercept malicious prompts dynamically.
• Open-Source & Developer Toolkits
  • NVIDIA NeMo Guardrails : An open-source toolkit providing programmable middleware that lets you writerails to guide conversations and block off-topic or malicious prompt overrides.
  • Guardrails AI : An open-source Python framework enabling structural and safety validations on both inputs and outputs using a composable validator architecture.

Would you like to explore open-source developer toolkits that you can self-host, or are you looking for a fully managed enterprise SaaS firewall?

Protecting system prompts from extraction and overriding via prompt injection requires intercepting inputs and outputs through a "sandwich" proxy architecture (an AI firewall or gateway). These tools inspect user prompts, Retrieval-Augmented Generation (RAG) data, and tool responses before they hit your core LLM, and scrub the outputs before they return to the user.

Enterprise-grade AI firewalls, security gateways, and guardrail providers span major cloud ecosystems, specialized cybersecurity vendors, and open-source frameworks:

1. Hyperscaler & Cloud Security Gateways

• Microsoft Entra AI Gateway / Azure Prompt Shields: Offers native prompt injection protection and jailbreak screening designed to intercept malicious queries at the network/proxy level before they reach your models.
• AWS Bedrock Guardrails: Lets you implement safeguards across foundational models to block unsafe user inputs, configure denial filters, and redact sensitive information or system prompt leaks.
• Cloudflare AI Gateway: Provides built-in observability, rate limiting, and guardrail capabilities to filter and monitor requests flowing into LLMs.
• Akamai Firewall for AI: Positioned at the edge to inspect traffic and mitigate prompt injections and data exfiltration attempts.

2. Specialized AI Security & Runtime Vendors

• HiddenLayer: Specializes in MLSecOps, offering runtime protection and model security to detect prompt injections and adversarial manipulations.
• Radware Runtime LLM/Agent Protection: Focuses on real-time behavior monitoring and firewalling to block abnormal or malicious conversational patterns.
• Lakera Guard: A dedicated API-first guardrail specifically built to stop prompt injection, jailbreaking, and toxic inputs in real time.
• Prompt Armor / Rebuff: Specialized tools focused heavily on detecting and isolating indirect and direct prompt injection vectors.

3. Open-Source & Framework-Level Guardrails

• NVIDIA NeMo Guardrails: An open-source toolkit that lets developers build programmable guardrails (colloquial and topical rails) to prevent models from deviating or leaking internal system instructions.
• Llama Guard (Meta): A safety classifier model that you can self-host and run as an input/output filter layer in your pipeline.
• Promptfoo / Garak: While more accurately described as evaluation and red-teaming frameworks rather than runtime firewalls, these tools are essential for testing how resilient your system prompt is against injection suites before deployment.

If you share details about where your LLM is hosted (e.g., AWS, Azure, self-hosted on Kubernetes) and whether you are using AI agents with external tools , I can recommend the most seamless architectural integration for your stack.

Yes. The market is increasingly using terms like AI firewall, LLM firewall, AI guardrails, and runtime AI security for products that sit between your application and the model.

A few vendors worth evaluating:

• Lakera — Lakera Guard. Probably one of the closest matches to what you're describing. It can screen inputs for prompt injection/jailbreaks and outputs for sensitive-data leakage, including attempts to extract system prompts. It also supports indirect prompt injection from retrieved documents and tool responses.
• Check Point Software Technologies — Check Point AI Guardrails. Check Point acquired Lakera and now offers AI Guardrails for prompt attacks, data leakage, system-prompt detection, malicious links, and agent/tool interactions. It can operate in detect or enforce mode, including blocking or warning.
• HiddenLayer — Runtime Security / Interactions. Its Interactions API analyzes LLM inputs and outputs for prompt injection, PII, code, denial-of-service and other security categories, with policies determining what gets flagged.
• Operant AI — Semantic Firewall. A newer approach aimed particularly at AI agents: it evaluates intent across prompts, tool calls, code and data transfers and can block, allow or redact activity.

One important caveat

I wouldn't rely on an "AI firewall" as the only defense against system-prompt extraction. A good architecture should assume that prompt injection will sometimes get through.

For protecting your system prompt specifically, I'd combine:

1. Input/context scanning for direct and indirect prompt injection.
2. Output scanning for system-prompt, credential, PII and other sensitive-data leakage.
3. Strict separation of trusted instructions from untrusted retrieved content.
4. Least-privilege tool permissions so a successful injection can't automatically exfiltrate secrets.
5. Tool-call validation outside the LLM—don't let the model itself be the final authorization layer.
6. Secrets kept outside the prompt/context wherever possible.

This matters especially for RAG and agents: an attacker doesn't necessarily have to type "reveal your system prompt." They can put malicious instructions into a webpage, PDF, email, RAG document, or tool response that your agent subsequently ingests.

If your primary requirement is specifically "prevent users from extracting our system prompt," I'd shortlist Lakera/Check Point, HiddenLayer, and an architecture-level solution rather than buying based on the "AI firewall" label alone. The key question for vendors is whether their detector can catch system-prompt extraction as a data-leakage policy, rather than merely detecting obvious jailbreak phrases.

Several dedicated cybersecurity vendors, cloud providers, and open-source projects offer AI firewalls and runtime guardrails specifically designed to sanitize user inputs, block jailbreaks, and prevent prompt injections from leaking system prompts.

Specialized AI Security & Guardrail Providers

• Lakera Guard : A low-latency API security layer (part of Check Point) that actively inspects and blocks prompt injections, jailbreaks, and toxic inputs before they reach your LLM.
• Protect AI (LLM Guard) : Offers LLM Guard, a widely used toolkit and gateway proxy for real-time input sanitization, toxicity scanning, and prompt-injection detection.
• NVIDIA NeMo Guardrails : An open-source toolkit enabling developers to programmatically define safety rails, block unauthorized topics, and intercept malicious inputs or unintended prompt extractions.
• Guardrails AI : A framework for validating, structuring, and sanitizing LLM inputs and outputs against specific risk policies.

Enterprise Cloud & Infrastructure Firewalls

• Cloudflare (AI Gateway) : Provides rate-limiting, caching, and security guardrail policies to monitor and filter traffic heading to foundational models.
• F5 & Radware : Traditional enterprise security providers that have expanded their web application firewalls (WAF) to include AI-specific proxies inspecting API payloads for adversarial text triggers.

If you can share where your models are hosted (e.g., self-hosted open-source vs. OpenAI/Anthropic APIs) and your deployment stack , I can suggest the best integration pattern (gateway proxy vs. native SDK guardrails) for your architecture.

Yes. The market often calls these AI firewalls, LLM firewalls, or AI guardrails. For your specific concern—prompt injection plus system-prompt/data leakage—these are worth evaluating:

• lakera.ai — probably the closest match to what you describe. It screens inputs and outputs for prompt injections, including indirect injections in documents/URLs, and supports blocking, redaction, or warnings. It also specifically advertises detection/prevention of system-prompt leakage.
• github.com — open-source and highly configurable. It can sit between your application and LLM and provides jailbreak/injection detection plus programmable input/output/retrieval rails.
• Protect AI LLM Guard — an open-source scanner library for LLM inputs/outputs, covering prompt injection alongside PII, secrets, toxicity, and other risks.
• Cisco AI Defense — an enterprise AI-security platform with prompt inspection/defense capabilities; it is also integrated as an input-protection option in NVIDIA's guardrail ecosystem.
• F5 AI Guardrails — another enterprise gateway/guardrail option, including input scanning and policy enforcement.
• Trend Micro Vision One AI Guard and CrowdStrike AIDR Guard — security-vendor offerings that can perform AI input/output protection and are exposed as guardrail integrations in NeMo.
• OpenRouter Guardrails — a lighter-weight option; its prompt-injection defense currently uses regex/pattern matching, so I'd view it as an additional filter rather than a comprehensive defense against sophisticated indirect injection.

What I'd prioritize

For an enterprise application where protecting the system prompt is a primary requirement, I'd shortlist Lakera, Cisco AI Defense, and a self-hosted NeMo/LLM Guard architecture.

One important caveat: don't treat input sanitization as sufficient protection for the system prompt. OWASP recommends screening not just user messages but also RAG documents, web content, tool results, and other untrusted context—and combining model-based detection with deterministic controls.

In other words, a good architecture is:

untrusted input → AI firewall/guardrail → LLM → output/data-leakage guardrail → user/tool

rather than simply:

user input → sanitizer → LLM

If you tell me whether you're running OpenAI/Anthropic APIs, Azure/AWS, or self-hosted models, I can narrow this to the 3–5 products that best fit your stack, including deployment model, latency, pricing model, and how well each handles indirect prompt injection and system-prompt extraction.