Prompt injection detection and input sanitization

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.docs.lakera.ai+2

Best for: teams building custom LLM apps, RAG pipelines, and AI agents.

Website ·Documentation

2. Palo Alto Networks Prisma AIRS

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.www.paloaltonetworks.com+2

Best for: enterprises that need centralized AI security, policy enforcement, and security-team visibility.

Website ·Documentation

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.learn.microsoft.com+5

Best for: Azure-native applications that want managed detection without operating a separate security service.

Documentation

4. Amazon Bedrock Guardrails

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.docs.aws.amazon.com+2

Best for: AWS-centric production systems, especially applications already using Bedrock.

Website ·Documentation

5. Prompt Security

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.prompt.security+1

Best for: organizations protecting multiple LLM providers or homegrown AI applications across different environments.

Website ·Product details

6. Guardrails AI

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.guardrailsai.com+2

Best for: engineering teams that prefer customizable, code-level enforcement over a managed security platform.

Website ·Validator catalog

Comparison at a glance

Sources: official product documentation from docs.lakera.aidocs.paloaltonetworks.comlearn.microsoft.comdocs.aws.amazon.comprompt.securityguardrailsai.com.

Which should you shortlist?

• 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:

1. Indirect injection: Can it detect malicious instructions in retrieved documents, webpages, and tool responses—not just the user's original prompt?
2. Enforcement before action: Can it stop the request before the model sees it or before an agent executes a tool call?
3. Fail-closed behavior: What happens if the security service times out or is unavailable?
4. Latency and scale: What is the p95/p99 latency overhead at your expected throughput?
5. Auditability: Are blocked attempts logged with detection reasons, policy IDs, and enough context to investigate?
6. 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.

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.docs.lakera.ai+1
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.cloud.google.com+1
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.learn.microsoft.com
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.docs.aws.amazon.com
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.github.com

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.docs.lakera.ai+1

A useful enforcement pipeline is:

                    ┌──────────────────────┐
User ──────────────►│ Input security layer │
                    │                      │
                    │ injection detection  │
                    │ PII/secrets          │
                    │ length/schema limits  │
                    │ custom deny rules    │
                    └──────────┬───────────┘
                               │
                         ALLOW │ BLOCK
                               ▼
                         LLM / Agent
                               │
              ┌────────────────┴────────────────┐
              ▼                                 ▼
        Tool/RAG inputs                   Model output
              │                                 │
        scan again                         output scan
              │                                 │
          ALLOW/BLOCK                       ALLOW/BLOCK

Important: scan untrusted retrieved content and tool responses separately, not just the original user prompt. That's where indirect prompt injection becomes particularly relevant.learn.microsoft.com+1

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.cloud.google.com+1

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.github.com

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.

For a production LLM, I’d prioritize an LLM firewall that evaluates prompts and model inputs in real time, detects indirect prompt injection, and can block or quarantine suspicious requests before they reach the model or its tools.

The key is not just detecting malicious-looking text. A robust defense should also address retrieved documents, RAG content, tool outputs, and attempts to manipulate an agent into leaking data or taking unauthorized actions.

There isn't one universally best product. The right choice depends on whether you're protecting a conventional LLM API, a RAG application, or an agent with access to production systems.

My shortlist for real-time LLM protection

1. Check Point AI Guardrails (Lakera)

(Best starting point for prompt injection)

Managed detection API · Model-agnostic

Strong fit when your main goal is to detect direct and indirect prompt injection in user prompts, retrieved documents, and agent tool interactions. Also supports data-leakage defenses, malicious-link detection, and configurable blocking or monitoring. Supports hosted and self-hosted deployments.docs.lakera.ai+2

Best for: Teams that want specialized prompt-attack detection without building their own detection models.

Product documentation

2. Palo Alto Networks Prisma AIRS

Enterprise AI security · Network or API enforcement

A strong choice if you need LLM protection as part of a broader enterprise security program. It can inspect live AI traffic for prompt injection, data leakage, malicious content, and agent-specific risks. Its network-intercept and API-intercept deployment options provide flexibility for existing infrastructure.docs.paloaltonetworks.com+1

Best for: Enterprises that want AI runtime security integrated with network security and centralized policy enforcement.

Product overview

3. NVIDIA NeMo Guardrails

Open-source framework · Self-managed

Lets developers define programmable rules around input, dialogue, retrieval, tool execution, and output. It is useful when you need control over the enforcement logic and want to run guardrails within your own environment. It requires more engineering and operational responsibility than a managed security service.infosec.qa+1

Best for: Teams with strong ML/platform engineering capabilities, strict data-residency requirements, or a preference for self-hosting.

Source code and documentation

Which would I choose?

My default recommendation is Check Point AI Guardrails (Lakera) if prompt injection is your primary concern. I'd shortlist Prisma AIRS instead if you already operate a Palo Alto Networks security environment and want a broader runtime security layer. NeMo Guardrails is worth evaluating if you prefer to own the enforcement stack.

These are architecture-based recommendations, not the results of an independent benchmark. Vendor detection claims alone don't establish which product has the lowest latency or the fewest missed attacks.

What to test before putting one in production

A security product can detect suspicious text without reliably stopping the harmful action. Run a proof of concept against your own application and measure:

One critical architectural point: don't rely on an LLM firewall as your only security boundary. Enforce least-privilege tool permissions, validate authorization outside the model, restrict data access, and require approval for consequential actions. Prompt-injection detection is defense in depth, not a guarantee.docs.lakera.ai+1

Let me narrow this to your production stack

One question: where does your LLM application run?

1. Deployment environment

Cloud API (OpenAI, Anthropic, or similar)

AWS / Amazon Bedrock

Azure / Microsoft Foundry

Self-hosted or on-premises models

Hybrid or multiple providers

2. Application architecture

Basic chatbot or text-generation API

RAG application with document retrieval

Agent with tools, APIs, or database access

Multiple application types

3. Main priority

Highest detection quality

Lowest added latency

Data residency and compliance

Balanced security, latency, and cost

[Find the best fit]

Monitoring a production LLM for real-time prompt injection (both direct user-driven injection and indirect injection from retrieved data or tools) requires an active runtime inspection layer—commonly referred to as an LLM firewall, AI gateway , or guardrails framework.lakera.ai+2

Selecting the "best" service depends on your deployment architecture (SaaS vs. open-source/self-hosted) and whether you need protection against just prompt injection or broader data leakage (PII) and hallucination. The top-performing options break down by category:checkpoint.com+1

1. Commercial SaaS & Enterprise AI Firewalls (Best for Out-of-the-Box Real-Time Protection)

• • Check Point AI Guardrails (formerly Lakera Guard): Widely recognized as a leader specifically for prompt injection and jailbreak detection. It processes real-time inputs using models trained on massive adversarial datasets (such as insights from Gandalf ). It works exceptionally well at catching subtle, multi-step indirect prompt injections in agentic workflows.community.checkpoint.com+3
• • HiddenLayer: Focuses heavily on the model layer, offering robust runtime security monitoring, threat intelligence, and protection against adversarial manipulation and prompt-based attacks without introducing massive latency.cloudthrill.ca
• • Aporia / Arthur Shield: Excellent choices if you need real-time guardrails paired tightly with observability, compliance tracking, and bias/hallucination monitoring.cloudthrill.ca

2. Cloud-Native Ecosystem Managed Services (Best if Locked into a Cloud Provider)

If your production LLMs already live inside a specific cloud ecosystem, using their native security layers reduces latency and simplifies data governance:

• • AWS Bedrock Guardrails: Allows you to filter malicious prompts, block harmful content, and redact PII natively for models hosted on Amazon Bedrock.
• • Azure AI Content Safety / Azure API Management: Provides robust real-time jailbreak and prompt-attack screening built directly into the Microsoft Azure ecosystem.
• • Google Model Armor: Google's native security screening layer designed to sanitize inputs and outputs against prompt injections for applications leveraging Vertex AI.

3. Open-Source & Programmable Frameworks (Best for Zero-License Cost & Custom Control)

If data privacy mandates keeping inspection logic entirely in-house or tailoring rules tightly to custom application logic:

• • LLM Guard (by Protect AI): A comprehensive, highly composable open-source scanner suite featuring dozens of specialized scanners for prompt injection, PII, toxic language, and secrets.agentswarms.fyi+1
• • NVIDIA NeMo Guardrails: Highly programmable using Colang to set precise topical, input, and output boundaries for conversational LLMs and multi-agent workflows.agentswarms.fyi
• • Llama Guard (by Meta): A lightweight, open-weight safety-classifier model designed to act as a "model-as-judge" that you can host yourself and plug into any custom proxy pipeline.agentswarms.fyi

How to Choose the Right Deployment Pattern

1. API Gateway / Reverse Proxy: Route all incoming prompts through a central gateway (like Bifrost or an enterprise API proxy) running an external security service like Check Point/Lakera. This is ideal if you have multiple microservices hitting various LLMs.getmaxim.ai
2. In-App Middleware / SDK: Call a lightweight library (like LLM Guard or Guardrails AI ) synchronously inside your application code right before passing payloads to the LLM. This is ideal for fine-grained control over specific agent tool calls.firstrecon.ai+1

To help narrow down the ideal choice, tell me:

• Are you deployed primarily on AWS, Azure, GCP , or a self-hosted/on-prem infrastructure?
• Does your application involve AI agents with tool use/external web access , or is it a straightforward chatbot/Q&A interface?

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.Lakera+1
• 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.Cloudflare Developer Docs
• calypsoai.com — Its Inference Defend product operates at the inference layer and includes prompt-injection, obfuscation, system-prompt, PII, and custom scanners.Calypso AI Support+1
• 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.Lakera+1

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:

1. Input/indirect-injection detection before untrusted content reaches the model.
2. Output/DLP inspection to catch system-prompt or secret leakage even if the input detector misses the attack.
3. 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.
4. 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.Lakera
5. 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.

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.cloudthrill.ca+2

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.cloudthrill.ca+1
• • HiddenLayer: Provides model-layer security and an AI firewall approach that protects enterprise LLMs from adversarial manipulation, input attacks, and unauthorized behavior.cloudthrill.ca
• • Arthur Shield: Operates as a compliance and security firewall, monitoring inputs and outputs for toxic content, prompt injections, and data leakage.cloudthrill.ca
• • Aporia: Combines observability with real-time guardrails to catch adversarial inputs and monitor agent behavior inline.cloudthrill.ca

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.generalanalysis.com+1
• • 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.morphllm.com
• • 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:cloudthrill.ca

• • 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.cloudthrill.ca
• • NVIDIA NeMo Guardrails: A programmable toolkit that lets you define safety boundaries, dialog flows, and input-validation constraints using Colang.getmaxim.ai+2
• • 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.edgelabs.ai+2
• • 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.morphllm.com+1

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)?