Lakera Guard holds a clear lead as a dedicated, real-time security layer for detecting prompt injection, jailbreaks, and sensitive data leakage. However, when teams seek specialized firewalls specifically to shield system prompts from exposure, Palo Alto Networks is the usual answer.
Platforms for real-time prompt injection detection and blocking
For production LLM applications, the strongest options are security platforms that inspect prompts, retrieved documents, tool inputs, and model outputs at runtime—and can block, reject, sanitize, or quarantine suspicious requests before they reach sensitive models or tools.
The key distinction is between a platform that merely detects prompt injection and one that can enforce a blocking decision inline with your application's inference or agent workflow. For agentic systems, you also want protection against indirect prompt injection hidden in web pages, RAG documents, emails, and tool responses.
Leading platforms to evaluate
1. Check Point AI Guardrails (Lakera Guard)
(Specialist option)
Designed specifically for GenAI runtime protection. Detects direct and indirect prompt injection, including malicious instructions in documents and tool responses. Supports real-time enforcement to block, warn, or flag attacks.
Provides inline runtime inspection of prompts, responses, data flows, and agent interactions. Can detect and block prompt injection, malicious URLs, sensitive-data exposure, and agent-specific threats. Supports network-level and API-level enforcement.
Best for: enterprises that need centralized AI security, policy enforcement, and security-team visibility.
3. Microsoft Azure AI Content Safety — Prompt Shields
Detects attacks in user prompts and indirect instructions embedded in documents, emails, and web pages. In Microsoft Foundry, it can be enabled as part of guardrails and controls, with detection and filtering information exposed to the application.
Best for: Azure-native applications that want managed detection without operating a separate security service.
Provides configurable prompt-attack filters for detecting attempts to override model instructions, alongside content filtering and sensitive-information controls. Can be applied to model invocations and agent workflows.
Best for: AWS-centric production systems, especially applications already using Bedrock.
An LLM-agnostic security gateway for inspecting inbound and outbound traffic. Supports real-time prompt-injection prevention, sensitive-data filtering, and logging, with cloud, VPC, and on-premises deployment options.
Best for: organizations protecting multiple LLM providers or homegrown AI applications across different environments.
A developer-oriented alternative for teams that want to implement their own controls. Its validator ecosystem includes prompt-injection detectors, and applications can configure failure handling to reject or filter invalid inputs.
Best for: engineering teams that prefer customizable, code-level enforcement over a managed security platform.
Best specialist shortlist: Check Point AI Guardrails and Prompt Security. Both focus on protecting LLM applications against prompt attacks, including threats in untrusted content.
Best enterprise security integration: Prisma AIRS, particularly if you want centralized monitoring and inline enforcement across AI applications and agents.
Best cloud-native starting point: Microsoft Prompt Shields for Azure or Amazon Bedrock Guardrails for AWS.
Best developer-controlled approach: Guardrails AI, if you're comfortable owning the implementation and operational tuning.
These are capability-based recommendations, not the result of a controlled benchmark. Actual effectiveness, latency, and false-positive rates depend on configuration and attack type.
What to verify before choosing
For a production deployment, I would test each candidate against these requirements:
Indirect injection: Can it detect malicious instructions in retrieved documents, webpages, and tool responses—not just the user's original prompt?
Enforcement before action: Can it stop the request before the model sees it or before an agent executes a tool call?
Fail-closed behavior: What happens if the security service times out or is unavailable?
Latency and scale: What is the p95/p99 latency overhead at your expected throughput?
Auditability: Are blocked attempts logged with detection reasons, policy IDs, and enough context to investigate?
Evasion resistance: How does it handle obfuscated instructions, multilingual attacks, and benign content that resembles an attack?
One important caveat: Prompt-injection detection is not a complete defense. Combine it with least-privilege tool permissions, authorization checks outside the model, and human approval for high-impact actions. No detector can guarantee that every injection attempt will be caught.
The prominent platforms providing real-time monitoring, scoring, and blocking of malicious prompt injections include:
Lakera Guard — A dedicated real-time security layer built specifically to intercept direct/indirect prompt injections and jailbreaks with ultra-low latency (<12 ms). It scans multimodal inputs and supports 100+ languages, returning active block/redact decisions before payloads reach core LLMs.[](https://www.lakera.ai/risk/prompt-injection-attacks)
Protect AI LLM Guard — An open-source and enterprise-ready security toolkit designed for real-time sanitization, prompt injection detection, PII masking, and toxic content filtering in production pipelines.
AWS Bedrock Guardrails — Fully managed AWS service functionality that evaluates user inputs and foundational model responses in real-time across Amazon Bedrock, actively blocking prompt attacks and configured harmful topics.
EdgeLabs — Tailored for self-hosted or air-gapped agent fleets. It deploys an agent-only runtime detection mechanism (the Parallax engine) capable of kernel/eBPF-level and node-level monitoring to intercept prompt overrides locally without cloud dependencies.[](https://edgelabs.ai/blog/prompt-injection-detection-tools)
Would you like to explore how to implement latency budgets for these tools, or do you need help comparing cloud-edge firewalls vs. self-hosted SDK guardrails for your architecture?
Yes. For real-time blocking, the current options fall into three buckets: managed AI-security APIs, cloud-native guardrails, and self-hosted scanners.
Option
Real-time blocking
Direct + indirect injection
Sanitization / PII
Deployment
Notes
Lakera Guard / Check Point AI Guardrails
Yes
Yes
Yes
SaaS/API
Purpose-built runtime security; its /guard endpoint screens user/LLM messages, tool responses/descriptions, tool calls, and agent interactions.
Google Model Armor
Yes
Yes
Yes
Google Cloud
Runtime protection for prompts, responses and agents; supports streaming sanitization, including chunk-by-chunk real-time processing.
Azure AI Content Safety – Prompt Shields
Yes
Yes
Some adjacent Content Safety features
Azure API
Specifically detects user-prompt and document attacks and can block before generation. Synchronous API makes it straightforward to put in front of your LLM.
Amazon Bedrock Guardrails
Yes
Yes
Yes
AWS
Prompt-attack filtering supports jailbreaks and prompt injection; BLOCK can be configured as the action. Also has PII, word, topic and grounding filters.
Protect AI LLM Guard
Yes, self-enforced
Yes
Yes
Self-hosted Python
Has PromptInjection, Secrets, PII/anonymization, regex, toxicity, etc. However, the repository was archived July 9, 2026 and is no longer maintained, so I wouldn't start a new production deployment on it.
What I'd architect
For a production app, I would not rely on a simple regex "sanitizer." Prompt injection is semantic, and indirect attacks can arrive through RAG documents, webpages, emails, tool results, or tool descriptions. Azure explicitly distinguishes user-prompt attacks from document attacks, while Lakera's current API also screens tool-related inputs.
Important: scan untrusted retrieved content and tool responses separately, not just the original user prompt. That's where indirect prompt injection becomes particularly relevant.
If latency is your primary concern
Google Model Armor is particularly interesting if you're streaming large inputs because its real-time streaming mode processes chunks as they arrive rather than waiting for the entire payload.
For a conventional request/response application, the managed APIs from Lakera, Azure, AWS, or Google are easier to operate than maintaining your own detection model.
One useful independent benchmark is Lakera's PINT benchmark, which is specifically designed to test prompt-injection detectors rather than generic content moderation. Its published results include Lakera Guard, Bedrock Guardrails, Azure Prompt Shields, Model Armor, and open-source detectors. Treat those numbers as benchmark results rather than universal accuracy guarantees—the test date and benchmark configuration matter.
My practical shortlist:
Multi-cloud / agent-heavy: Lakera Guard
AWS stack: Bedrock Guardrails
Azure stack: Prompt Shields
Google Cloud + streaming: Model Armor
Self-hosted: use a currently maintained alternative rather than the archived LLM Guard
If you tell me your LLM provider (OpenAI/Anthropic/AWS/etc.), language/runtime (Python/Node/Go), and latency target, I can narrow this to a concrete production architecture and give you the middleware implementation.
Top Real-Time Prompt Injection & Sanitization Tools
Lakera Guard : An enterprise-grade, real-time security API designed specifically to intercept direct and indirect prompt injections, jailbreaks, and PII leaks. It delivers sub-50ms latency (averaging under 12ms), supports over 100 languages, and integrates easily into application pipelines via a single API call. Learn more at .[](]()]()
The top real-time LLM firewalls and security gateways are categorized below by their deployment architecture:
1. Dedicated Real-Time API Firewalls (Lowest Operational Overhead)
Lakera Guard (Check Point): Operates as a high-speed, sub-50ms runtime API firewall specifically built to catch direct/indirect prompt injection and jailbreaks. It is heavily battle-tested using adversarial data from their famous Gandalf game. Best for customer-facing production apps requiring elite precision and external SaaS execution.[](https://www.getmaxim.ai/articles/top-5-llm-security-tools-for-enterprise-ai-applications-in-2026/)
NVIDIA NeMo Guardrails: Excellent open-source programmable framework that allows you to define behavioral, topical, and safety guardrails using Colang or Python. Best if you need deep control over the dialog flow and conversational boundaries alongside basic injection protection.[](]()]()
3. Comprehensive Enterprise Security Suites (Best for Broad Governance)
Cisco AI Defense () & Palo Alto Prisma AIRS (): Enterprise-grade security fabrics that tie LLM runtime guardrails, vulnerability scanning, and data loss prevention (DLP) directly into broader corporate network and security operation workflows.[]()
Choose Protect AI LLM Guard or Llama Guard if you require 100% data privacy, zero recurring SaaS API costs, and local air-gapped hosting.[](]()](]()]()
To help narrow down the ideal option, could you share:
Are you hosting your LLM locally/VPC or calling a third-party API (OpenAI, Anthropic, etc.)?
What is your strict latency budget for security checks (e.g., <50ms, <200ms)?
Yes. The market is usually described as AI firewalls, LLM firewalls, AI gateways, or AI guardrails rather than simply “input sanitizers.” The main vendors/products I’d look at for prompt-injection detection and system-prompt/data-leakage protection include:
lakera.ai — Screens user prompts, retrieved documents, URLs, tool responses, and tool descriptions for prompt attacks. It also has controls specifically aimed at detecting system-prompt leakage and other sensitive-data exposure.
cloudflare.com — Formerly “Firewall for AI.” Its WAF-layer tooling assigns a prompt-injection score that you can use in rules/rate limiting, making it attractive if you're already behind Cloudflare.
calypsoai.com — Its Inference Defend product operates at the inference layer and includes prompt-injection, obfuscation, system-prompt, PII, and custom scanners.
Check Point AI Security — The current Check Point offering around Lakera includes AI Guardrails for prompt attacks, data leakage, malicious links, and agent/tool behavior. It explicitly describes the runtime layer as a protective firewall around GenAI applications.
One important distinction
If your specific concern is “an attacker asks the model to reveal our system prompt,” don't rely solely on input sanitization. A robust architecture should have at least:
Input/indirect-injection detection before untrusted content reaches the model.
Output/DLP inspection to catch system-prompt or secret leakage even if the input detector misses the attack.
Strict separation of secrets from model context wherever possible—the model shouldn't have access to credentials or highly sensitive material merely because it has been given a system prompt.
Tool/agent controls if your model can browse, retrieve documents, call APIs, or use MCP tools. Indirect prompt injection can arrive through those channels rather than the user's message. Check Point and Lakera explicitly address these surfaces.
Logging and attack testing so you can measure false positives/negatives against your actual application.
In other words, I'd evaluate these as runtime security layers, not as a magical “sanitize the prompt and we're safe” filter. Prompt injection is fundamentally difficult to eliminate with string filtering alone.
If you tell me your stack (e.g. OpenAI API/Azure OpenAI + RAG + LangChain, AWS Bedrock, self-hosted models, agents/MCP, etc.), I can narrow this to the vendors that actually fit your architecture and compare deployment model, API integration, latency, self-hosting, and system-prompt-leak protection.
Guardrails AI : A framework focused on input/output validation, letting you enforce deterministic schemas and safety guardrails. It acts as a wrapper to catch adversarial attempts and ensure user inputs or tool outputs conform to strict structural and safety requirements before hitting downstream models or databases.[](https://edgelabs.ai/blog/prompt-injection-detection-tools)
Azure AI Content Safety (Prompt Shields) : A managed cloud service by Microsoft that includes dedicated Prompt Shields for real-time detection of jailbreak attacks and indirect prompt injections embedded in documents or external web retrievals. Perfect for applications already built within the Azure ecosystem.
If you can share where your app is hosted (AWS, Azure, on-prem) and whether you prefer a managed API or an open-source self-hosted gateway , I can help you narrow down the best integration fit.
Extremely convenient if you're already on Cloudflare
AWS Bedrock Guardrails
AWS/Bedrock workloads
Prompt injection, jailbreak and prompt-leakage detection
Native AWS/Bedrock APIs
Azure AI Content Safety / Prompt Shields
Azure/OpenAI workloads
User-prompt and document attack detection
Native Azure API
Prompt Security
Enterprise AI gateway / multiple LLMs
Runtime prompt-injection and data-leak protection
Cloud, VPC/self-hosted, or on-prem
My practical pick
If you're LLM-provider agnostic, I'd evaluate Lakera first. Its current product is specifically aimed at runtime AI security rather than being primarily a conventional content-moderation layer. Lakera advertises sub-50 ms runtime latency, 100+ language coverage, and protection against prompt injection, data leakage and unsafe agent/tool behavior.
Its architecture is particularly relevant if your production system has RAG, tools, agents, or MCP: the company says it can inspect not only user input but also tool responses and tool descriptions, which matters for indirect prompt injection.
If you're already on Cloudflare, I'd seriously consider Cloudflare first. Its AI Security for Apps operates at the WAF layer, gives you an injection-risk score, and lets you create rules to log, challenge, or block requests. That's attractive when you want the security control outside your application code.
If you're deeply committed to AWS or Azure, use the native controls as a baseline. AWS Bedrock Guardrails now explicitly supports prompt injection, jailbreaks, and—on its Standard tier—prompt leakage detection. docs.aws.amazon.com Azure Prompt Shields detects both user prompt attacks and attacks embedded in documents, which is particularly useful for RAG systems.
One important caveat
Don't treat an LLM firewall as a complete solution. Research has demonstrated that prompt-injection detectors themselves can be evaded, including through adversarial character-level techniques.
For a production system, I'd use defense in depth:
For agents, the last layer is especially important: even if an injection gets past the classifier, the model should not have unrestricted authority to send email, access databases, transfer money, modify infrastructure, etc.
What I'd benchmark before buying
Don't rely on vendor-reported detection rates. Put each candidate through your actual production prompts and attack corpus and measure:
True-positive rate on your injection set
False-positive rate on legitimate prompts
Indirect injection detection in RAG documents/web pages/tool output
Multilingual/obfuscated attacks
Latency p50/p95/p99
Availability/failure behavior
Data retention and whether prompts leave your environment
Ability to run in your VPC/on-prem
Logging/SIEM integration
Blocking vs. detection-only modes
Custom policies and allowlists
Agent/tool/MCP coverage
I'd also run an automated red-team suite continuously rather than doing a one-time bakeoff; current AI-security guidance increasingly treats prompt injection as an ongoing adversarial problem rather than something a single classifier can permanently solve.
If you tell me your stack (AWS/Azure/GCP, OpenAI/Anthropic/self-hosted, RAG vs. agents, and approximate requests/sec), I can narrow this to 2–3 architectures and compare likely latency, deployment complexity, and security coverage.
HiddenLayer: Provides model-layer security and an AI firewall approach that protects enterprise LLMs from adversarial manipulation, input attacks, and unauthorized behavior.[](https://cloudthrill.ca/llm-guardrail-solutions)
Arthur Shield: Operates as a compliance and security firewall, monitoring inputs and outputs for toxic content, prompt injections, and data leakage.[](https://cloudthrill.ca/llm-guardrail-solutions)
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.[](https://www.morphllm.com/llm-guardrails)
Google Cloud Model Armor: Google's managed security layer to sanitize prompts and guard against jailbreaks and sensitive data exposure.
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:[](https://cloudthrill.ca/llm-guardrail-solutions)
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.[](https://cloudthrill.ca/llm-guardrail-solutions)
