{"slug": "prompt-injection-detection-and-defense-tools-for-enterprise-ai-agents", "title": "Prompt Injection Detection and Defense Tools for Enterprise AI Agents", "summary": "A new guide compares 10 prompt-injection detection and defense tools for enterprise AI agents across five control layers: red-team testing, production detection, guardrails, gateway controls, and action authorization. The guide recommends Microsoft Azure Prompt Shields, Amazon Bedrock Guardrails, and Google Cloud Model Armor for cloud-native production screening, Check Point AI Guardrails for model-agnostic managed or self-hosted screening, Meta Llama Prompt Guard 2 for self-hosted classification, and NVIDIA garak, Microsoft PyRIT, and promptfoo for pre-deployment testing. For agents that take business actions, Arcade.dev checks every tool call routed through it against the user's permissions, the agent's scope, and existing and custom policies, so an injected instruction that evades a detector still cannot execute a denied action.", "body_md": "Prompt injection becomes a different security problem when an AI agent can send an email, update a CRM, modify a record, or trigger a workflow. Detecting the malicious instruction matters, but so does controlling what happens if detection misses it.\n\nThis guide compares 10 tools across the layers that matter for production agents: red-team testing, production detection, guardrails, gateway controls, and action authorization. It focuses on where each control runs, what it protects, and what additional layers are still required.\n\n## TL;DR\n\n- **For cloud-native production screening:** Microsoft Azure Prompt Shields, Amazon Bedrock Guardrails, and Google Cloud Model Armor provide managed controls within their respective cloud ecosystems.\n- **For model-agnostic managed or self-hosted screening:** Check Point AI Guardrails supports SaaS, private-cloud, on-premises, and air-gapped deployments.\n- **For self-hosted classification:** Meta Llama Prompt Guard 2 keeps prompt-injection detection inside infrastructure the team controls.\n- **For pre-deployment testing:** NVIDIA garak, Microsoft PyRIT, and promptfoo cover vulnerability scanning, red-team scenarios, and regression testing. promptfoo also extends into Enterprise production guardrails.\n- **For programmable application guardrails:** NVIDIA NeMo Guardrails provides configurable controls around input, output, retrieval, dialog, and tool calls.\n- **For agents that take business actions:** Arcade.dev checks every tool call routed through it against the user’s permissions, the agent’s scope, and your existing and custom policies, so an injected instruction that gets past a detector still cannot execute an action those rules deny.\n\n## Quick comparison: At-a-glance summary\n\nPrompt-injection controls fall into four groups based on where they run. Pre-deployment scanners and red-team frameworks test models and applications before release. Production screening tools inspect live prompts, retrieved content, and outputs where configured.\n\nGateways and frameworks apply policies to the traffic and workflows they control.\n\nAction-boundary controls, such as an action runtime, decide what an agent can do when it attempts an action.\n\n| Tool | Where the control runs | Indirect injection: detection or defense | Action-control mechanism | Deployment | Best fit / choose when | \n|---|---|---|---|---|---|\n| **Microsoft Azure Prompt Shields** | Production screening | Detection (user prompts and documents) | Content screening/blocking; no action authorization | Azure AI Content Safety API; [preview container deployment](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/containers/prompt-shields-container) | Choose when you run Azure OpenAI/RAG apps and want Microsoft-native screening. | \n| **Check Point AI Guardrails (Lakera Guard)** | Production guardrail | Detection with inline blocking (integrated application and agent traffic) | Tool-call policy enforcement through Agent Behavior Defense; not an action runtime | SaaS; self-hosted Kubernetes/Docker; air-gapped | Choose when you need model-agnostic production screening across managed or self-hosted infrastructure. | \n| **Amazon Bedrock Guardrails** | Production guardrail | Detection when content is explicitly submitted to InvokeGuardrailChecks; native prompt-attack filtering does not automatically inspect tool results | Guardrail blocking or application-defined responses; not delegated action authorization | AWS managed service / API | Choose when you need prompt-attack filtering plus PII and policy controls in AWS. | \n| **Meta Llama Prompt Guard 2** | Production classifier | Detection within its model limits | Classification only | Open weights / self-hosted | Choose when prompts and retrieved data must stay inside controlled infrastructure. | \n| **NVIDIA garak** | Pre-deployment scanner | Testing only (app-specific probes) | Pre-deployment testing only | Open-source / self-hosted / CLI | Choose when security teams need automated LLM vulnerability scanning before release. | \n| **Microsoft PyRIT** | Pre-deployment framework | Testing only (adversarial scenarios) | Pre-deployment testing only | Open-source / self-hosted / CLI | Choose when enterprise red teams need automated and human-led adversarial scenario orchestration. | \n| **promptfoo** | Pre-deployment framework and production guardrail | Testing (configurable red-team tests); detection with blocking in Enterprise guardrails | Input/output and tool-call guardrail blocking in Enterprise | Open-source CLI / hosted and Enterprise options | Choose when developers want prompt-injection regression tests in PR checks, with optional production guardrails. | \n| **Google Cloud Model Armor** | Production screening | Detection on submitted prompts/responses; inline blocking on supported gateway or MCP integrations | Inline screening/blocking on supported gateway and MCP integrations; REST API is detector-only | Google Cloud managed service / REST API / supported gateway integrations | Choose when you operate in Google Cloud and want managed prompt, response, and supported agent-traffic screening. | \n| **NVIDIA NeMo Guardrails** | Framework/orchestration | Detection and blocking, depending on configured rails | Tool-call validation/blocking; application owns authorization and execution | Open-source / self-hosted framework | Choose when you need programmable guardrails for complex RAG, dialog, and tool-calling flows. | \n| **Arcade.dev** | Action boundary (before and after each tool call executes) | Defense: limits what an injected instruction can make the agent execute, whether or not it was detected | Checks every tool call against user permissions, agent scope, and existing and custom policies before execution; blocks injected calls that fail and can require approval for high-risk actions | Cloud, VPC, on-premises, air-gapped | Choose when agents take business actions and you need to stop injected tool calls that get past detectors. | \n\n## How we evaluated these tools\n\nThe evaluation compares product capabilities, pricing, model cards, repositories, benchmark results, and published latency claims. Generic content moderation APIs are excluded unless they provide prompt-injection, tool-poisoning, agent-workflow, or guardrail-specific capabilities.\n\nWhen reviewing benchmark claims, look for results that directly report precision, recall, latency, throughput, or false-positive rates. Published numbers do not serve as universal rankings unless tools were tested on the exact same dataset, threat model, language mix, input length, and deployment environment.\n\nScanners like garak, PyRIT, and promptfoo produce workload-specific red-team findings, not universal detector accuracy scores. Teams should validate shortlisted tools on their own prompts, RAG data, tool arguments, languages, approval flows, and production latency budget.\n\n### Criterion 1: Where the control runs\n\nPre-deployment, production screening, gateway/framework, or downstream action boundary.\n\n### Criterion 2: Indirect injection detection or defense\n\nWhether the tool detects injected instructions in direct prompts, retrieved documents, tool outputs, webpages, emails, PDFs, and other untrusted context, or defends against them by limiting what the agent can execute when an injection gets through. Pre-deployment scanners do neither in production; they test exposure before release.\n\n### Criterion 3: Agent workflow support\n\nRAG scanning, tool-call inspection, MCP/API pathways, orchestration compatibility, and production routing.\n\n### Criterion 4: Action-control mechanism\n\nWhether the tool only detects or flags risky content, can block or validate tool calls, or performs identity-aware authorization and policy enforcement before execution. Tool-call blocking and delegated action authorization are different controls and should not be treated as equivalent.\n\n### Criterion 5: Deployment and operational fit\n\nSaaS, cloud-native, VPC, self-hosted, open weights, gateway, on-premises, or air-gapped.\n\n### Criterion 6: Pricing transparency and billing drivers\n\nPublic pricing, usage units, infrastructure costs, model-call costs, enterprise plans, and scaling drivers.\n\n## Option 1: Microsoft Azure Prompt Shields\n\n### Best for\n\n- Azure-native engineering teams running Azure OpenAI, RAG, or tool-calling applications inside the Microsoft ecosystem.\n- Azure Prompt Shields provides managed Microsoft-native screening for both user prompts and retrieved content.\n\n### Overview\n\nAzure Prompt Shields is part of Azure AI Content Safety and provides classifiers for [direct prompt attacks and indirect prompt injection](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection) in third-party content. It fits when the surrounding application, model hosting, logging, and compliance posture are already Azure-centered.\n\n### Key features\n\n- **User Prompt Shield:** Screens user-supplied prompts for jailbreaks and adversarial instructions.\n- **Document Shield:** Inspects retrieved or third-party content for embedded malicious instructions.\n- **Azure AI integration:** Fits directly with Azure OpenAI and Azure AI application architectures.\n- **Managed API deployment:** Provides screening through Azure’s managed service.\n- **Enterprise platform controls:** Sits within Azure’s broader identity, logging, networking, and compliance ecosystem.\n\n### Pricing\n\n- Azure bills Standard-tier Prompt Shields per 1,000 text records; the displayed rate varies by region and customer agreement.\n- A text record contains up to 1,000 Unicode characters.\n- A free tier provides 5,000 text records per month.\n\n### Key strengths\n\n- Native Azure AI Content Safety control for Azure-based applications.\n- Dedicated coverage for both direct and indirect prompt-injection patterns.\n- Available through Azure’s managed API and preview container deployment.\n\n### Where the fit breaks down\n\n- Best suited to Azure architectures. Multi-cloud teams must validate routing, latency, regional availability, and data-residency requirements.\n- Azure Prompt Shields screens content and returns an attackDetected signal, but does not provide downstream action authorization.\n\n## Option 2: Check Point AI Guardrails (Lakera Guard)\n\n### Best for\n\n- Multi-cloud enterprise teams that want a model-agnostic security layer for production traffic.\n- Check Point AI Guardrails provides production screening across SaaS, private-cloud, on-premises, and air-gapped deployment models.\n\n### Overview\n\nCheck Point AI Guardrails incorporates Lakera Guard as a security layer for prompt, RAG, agent, and MCP risks. It can run as a hosted service or in customer-controlled infrastructure, including private-cloud, on-premises, and air-gapped deployments.\n\n### Key features\n\n- **Prompt-injection detection:** Screens prompts and application traffic for adversarial instructions.\n- **Jailbreak protection:** Check Point AI Guardrails detects jailbreak and bypass attempts through its detection stack.\n- **API-based integration:** Sits in front of model providers or application endpoints.\n- **Data leakage controls:** Supports detection of sensitive-data exposure.\n- **Agent and tool controls:** Screens agent interactions and tool responses, with Agent Behavior Defense controls for off-task actions and tool allow/deny policies.\n\n### Pricing\n\n- Check Point provides AI Guardrails pricing through its enterprise sales process.\n\n### Key strengths\n\n- Commercial guardrail for production LLM and agent traffic.\n- Supports model-agnostic and multi-cloud architectures.\n- Check Point maintains the detection stack for SaaS deployments; self-hosted customers can run the screening stack in their own infrastructure.\n\n### Where the fit breaks down\n\n- Published performance numbers, including detection-rate, latency, false-positive, and language-coverage claims, require workload-specific validation.\n- Check Point AI Guardrails can enforce security policies on agent interactions and tool calls. Its control point is the security layer around agent traffic rather than an action runtime responsible for delegated end-user authorization, credential handling, and tool execution.\n\n## Option 3: Amazon Bedrock Guardrails\n\n### Best for\n\n- AWS-centric organizations building generative AI applications on or around Amazon Bedrock.\n- Amazon Bedrock Guardrails combines prompt-attack filtering, denied topics, sensitive-data controls, and grounding checks in AWS.\n\n### Overview\n\nAmazon Bedrock Guardrails is a managed AWS guardrail service for applying safety, privacy, and policy controls to generative AI applications. Its ApplyGuardrail API evaluates content through configured guardrails, supporting use cases outside a [single model invocation path](https://docs.aws.amazon.com/en_en/bedrock/latest/userguide/guardrails-use-independent-api.html).\n\nAWS also provides InvokeGuardrailChecks for agentic workflows, allowing applications to run individual guardrail checks at specific points in an agent loop without automatically blocking the request.\n\n### Key features\n\n- **Prompt-attack filtering:** Native InvokeModel and Converse filtering evaluates tagged user input but does not automatically inspect tool results or tool definitions. InvokeGuardrailChecks can run prompt-attack checks on content explicitly submitted at different steps of an agent loop.\n- **Denied topics:** Blocks restricted subject areas the organization defines.\n- **Sensitive information filters:** Detects or redacts PII and other sensitive data.\n- **Contextual grounding checks:** Validates whether responses are grounded in provided context.\n- **Guardrail APIs:** ApplyGuardrail can enforce configured guardrails, while InvokeGuardrailChecks provides detect-only safeguards that applications can invoke at individual steps of an agent workflow and use to block, retry, pass, or log.\n\n### Pricing\n\n- Prompt-attack checks through InvokeGuardrailChecks are $0.08 per 1,000 text units.\n- Content filters are $0.07 per 1,000 text units, sensitive-information filters are $0.10, and contextual-grounding checks are $0.10.\n- One text unit contains up to 1,000 characters.\n\n### Key strengths\n\n- Integrates directly with AWS-native AI applications and IAM-controlled architectures.\n- Combines prompt-injection-related controls with broader privacy and response-quality guardrails.\n- AWS operates the guardrail service; teams configure the safeguard policies and application integration.\n\n### Where the fit breaks down\n\n- Multi-cloud teams must test cross-cloud latency, routing complexity, and data-transfer implications.\n- Bedrock Guardrails evaluates content and policy. Delegated authorization for downstream business actions remains the responsibility of the application or an action runtime.\n\n## Option 4: Meta Llama Prompt Guard 2\n\n### Best for\n\n- Regulated, privacy-sensitive, or air-gapped environments where prompts and retrieved content cannot be sent to external APIs.\n- Llama Prompt Guard 2 provides self-hosted prompt-injection classification for teams that can operate an open-weights classifier.\n\n### Overview\n\nLlama Prompt Guard 2 is an [open-weights classifier for identifying prompt injection](https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-22M/blob/main/README.md) and jailbreak attempts. It suits teams that want to keep detection entirely inside their own infrastructure rather than calling a managed security API.\n\n### Key features\n\n- **Open-weights model:** Deploys in controlled infrastructure under the applicable Meta Llama Community License terms.\n- **Attack classification:** Evaluates text for injection-style adversarial instructions.\n- **Model variants:** Available as 86M- and 22M-parameter classifiers. Both support a 512-token context window.\n- **Probability-based output:** Produces classifier scores that teams can threshold and tune.\n- **Self-hosted deployment:** Enables private inference, domain-specific evaluation, and internal monitoring.\n\n### Pricing\n\n- No per-use vendor API fee for the model weights under the applicable Meta license.\n- Operational costs depend on hosting, inference infrastructure, monitoring, and engineering operations.\n\n### Key strengths\n\n- Self-hosted inference keeps screening inside infrastructure the team controls.\n- Teams control hosting, model version, thresholds, and evaluation in their own environment.\n- Supports organization-specific testing and threshold tuning.\n\n### Where the fit breaks down\n\n- Requires MLOps resources to deploy, scale, monitor, update, and evaluate.\n- Both variants have a 512-token context window, so longer documents and tool payloads need to be segmented before screening.\n- The model provides classification only; downstream action enforcement requires a separate application or action-runtime layer.\n\n## Option 5: NVIDIA garak\n\n### Best for\n\n- Security teams, red teams, and platform teams adding automated LLM vulnerability scanning to development and release workflows.\n- garak provides pre-deployment adversarial probing for LLM applications.\n\n### Overview\n\ngarak is an open-source [LLM vulnerability scanner](https://github.com/NVIDIA/garak) that runs adversarial probes against models and applications. It serves as a pre-deployment or regression-testing tool, not as an inline production defense.\n\n### Key features\n\n- **Adversarial probe library:** Includes prompt-injection, jailbreak, hallucination, and leakage tests.\n- **Automated test execution:** Runs repeatable scans against configured targets.\n- **Plugin architecture:** Supports different generators, detectors, probes, and harnesses.\n- **CLI and structured reporting:** Runs repeatable scans from the command line and produces detailed report, hit, and debug logs.\n- **Structured findings:** Produces outputs that security teams can review and triage.\n\n### Pricing\n\n- Free and open-source under the Apache-2.0 license.\n- Model API calls, hosting, infrastructure, and test execution remain separate costs.\n\n### Key strengths\n\n- Supports baseline vulnerability scanning and repeatable regression checks.\n- Helps teams identify weaknesses before production deployment.\n- garak’s probe coverage expands through community contributions.\n\n### Where the fit breaks down\n\n- garak does not block live prompt-injection attempts in production.\n- Findings require security expertise to interpret, prioritize, and translate into remediation work.\n\n## Option 6: Microsoft PyRIT\n\n### Best for\n\n- Enterprise red teams and AI security teams running structured adversarial scenarios before deployment.\n- PyRIT provides orchestration for automated and human-led red-team workflows rather than inline detection in production.\n\n### Overview\n\nMicrosoft PyRIT is an open-source [red-team orchestration framework](https://github.com/microsoft/PyRIT) for generative AI systems. It coordinates adversarial scenarios, targets, scoring, and multi-turn strategies for pre-deployment or regression testing.\n\n### Key features\n\n- **Scenario orchestration:** Supports automated and human-led adversarial testing workflows.\n- **Multi-turn strategies:** Enables attacks that unfold across multiple model interactions.\n- **Custom targets:** Configures against different models, applications, or endpoints.\n- **Memory and scoring:** Tracks interaction history and evaluates results with configurable scorers.\n- **CLI and GUI support:** Provides multiple ways for red teams to run and review tests.\n\n### Pricing\n\n- Free and open-source under the MIT license.\n- Model calls, infrastructure, storage, and test execution remain separate costs.\n\n### Key strengths\n\n- Supports repeatable adversarial scenario management for red-team workflows.\n- Supports both automated and human-in-the-loop testing.\n- Supports both single-turn and multi-turn attack strategies, including Crescendo, TAP, and Skeleton Key.\n\n### Where the fit breaks down\n\n- PyRIT is not an inline production detector or blocking control.\n- Results depend heavily on scenario design, target configuration, scorers, and red-team expertise.\n\n## Option 7: promptfoo\n\n### Best for\n\n- AI developers and QA teams wanting security and quality evaluations inside normal development workflows.\n- promptfoo supports prompt-injection regression tests in PR checks and CI pipelines, with Enterprise Adaptive Guardrails available for production enforcement.\n\n### Overview\n\npromptfoo is an evaluation and red-teaming framework for LLM applications. It lets teams define tests, providers, assertions, and adversarial cases in configuration.\n\npromptfoo Enterprise also offers Adaptive Guardrails for production enforcement, including policies on model inputs, outputs, tool-call inputs, and tool-call outputs.\n\n### Key features\n\n- **Declarative YAML configuration:** Defines prompts, providers, tests, and assertions.\n- **Red-team test generation:** Supports adversarial testing workflows for LLM apps.\n- **CI/CD integration:** Runs in GitHub Actions, GitLab CI, and similar pipelines.\n- **Broad provider support:** Tests across commercial APIs, local models, and custom endpoints.\n- **Regression tracking:** Helps teams measure whether prompt-injection exposure changes over time.\n- **Adaptive Guardrails:** Enterprise guardrails can block or flag model traffic and validate structured tool-call inputs and outputs.\n\n### Pricing\n\n- The Community CLI is free and includes up to 10,000 red-team probes per month.\n- Enterprise and on-premises plans use custom pricing.\n\n### Key strengths\n\n- Integrates LLM security evaluations and red-team tests into developer and CI workflows.\n- Combines output-quality checks and security assertions in the same workflow.\n- Uses configuration-driven tests and CI integrations for developer workflows.\n\n### Where the fit breaks down\n\n- Adaptive Guardrails is an Enterprise capability that must be integrated into the model or tool-call path. Its tool-call policies provide guardrail enforcement rather than delegated end-user action authorization.\n- Multi-turn agent workflows and complex tool-calling paths require substantial upfront configuration.\n\n## Option 8: Google Cloud Model Armor\n\n### Best for\n\n- Google Cloud and Vertex AI teams that want native prompt, response, and supported agent-traffic screening.\n- Model Armor provides managed prompt, response, and supported agent-traffic screening across Google Cloud integrations.\n\n### Overview\n\nGoogle Cloud Model Armor is a managed security service for inspecting AI prompts and responses. It supports prompt-injection, jailbreak, sensitive-data, and related model-security controls through its REST API and supported Google Cloud integrations.\n\nSupported integrations extend Model Armor into agent gateway and MCP paths, where it can inspect and block supported traffic inline.\n\n### Key features\n\n- **Prompt inspection:** Screens user inputs for security and safety risks.\n- **Response inspection:** Evaluates model outputs before they return to the user or system.\n- **Injection and jailbreak detection:** Applies Google-managed filters where configured.\n- **Sensitive-data controls:** Detects or handles protected data.\n- **Agent and MCP integrations:** Supported integrations can inspect and block prompts, responses, and supported agent or MCP traffic, including MCP tool calls and responses.\n- **Google Cloud integration:** Works with IAM, logging, gateways, and security operations tooling.\n\n### Pricing\n\n- Model Armor includes up to 2 million tokens per month at no cost when purchased standalone.\n- Additional usage is $0.10 per 1 million tokens.\n\n### Key strengths\n\n- Provides native integrations for Google Cloud AI environments.\n- Provides managed screening through its REST API and supported Google Cloud integrations.\n- Integrates with Google Cloud logging, gateways, and security infrastructure.\n\n### Where the fit breaks down\n\n- Model Armor supports models and workloads across clouds through its REST API and supported gateway integrations, but capabilities differ by integration. The REST API is detector-only, while supported Google Cloud integrations can provide inline blocking.\n- Model Armor evaluates prompts and responses independently and does not maintain conversation history across multi-turn interactions.\n- Model Armor provides screening and inline policy enforcement, not delegated downstream action authorization.\n\n## Option 9: NVIDIA NeMo Guardrails\n\n### Best for\n\n- Advanced AI engineering teams building complex RAG, dialog, and agent orchestration flows.\n- NeMo Guardrails provides programmable control over conversation flow, validation, and policy behavior.\n\n### Overview\n\n[NVIDIA NeMo Guardrails](https://docs.nvidia.com/nemo/guardrails/about-nemo-guardrails-library/rail-types) is an open-source framework for defining application-level guardrails around LLM behavior. It integrates classifiers and rules into input, output, retrieval, dialog, and execution flows, but teams must design and operate those controls themselves.\n\n### Key features\n\n- **Colang policy language:** Defines conversational and behavioral rules.\n- **Input rails:** Validates or screens user inputs before model execution.\n- **Output rails:** Checks model responses before returning them.\n- **Retrieval rails:** Adds controls around RAG workflows.\n- **Execution and tool rails:** Can validate configured custom actions, tool calls, arguments, and results; authentication and authorization remain the application’s responsibility.\n- **Pluggable integrations:** Connects to external classifiers or custom logic where configured.\n\n### Pricing\n\n- Free and open-source under the Apache-2.0 license.\n- Costs come from hosting, engineering time, model inference, and any connected commercial services.\n\n### Key strengths\n\n- Customizable for complex multi-turn and RAG applications.\n- Supports configurable input, output, retrieval, dialog, execution, and tool rails.\n- Can combine Colang flows, custom Python actions, classifiers, and third-party guardrail services.\n\n### Where the fit breaks down\n\n- Tool-call validation runs through the opt-in IORails engine, which is currently experimental and supports the OpenAI Chat Completions wire format.\n- IORails validates tool names, arguments, and results but does not execute the tools. The application or service that owns the protected resource remains responsible for authentication and authorization.\n\n## Option 10: Arcade.dev\n\n### Best for\n\n- Enterprise teams deploying AI agents that can take actions in SaaS, internal tools, CRMs, email, ticketing systems, or operational workflows.\n- If malicious instructions bypass a detector, Arcade still blocks the resulting tool call when it exceeds the user’s permissions or the agent’s scope, or violates your existing or custom policies.\n\n### Overview\n\nArcade.dev is the action runtime between enterprise AI agents and the systems they act on. It combines delegated authorization, agent-optimized tools, and tool- and agent-level governance. It does not classify prompts as benign or malicious. Instead, it enforces authorization and policy on every tool call it mediates, before the action executes.\n\nIn a prompt-injection defense stack, Arcade is the deterministic action-boundary layer. A detector judges whether content looks malicious. Arcade decides whether the resulting action runs.\n\nDeployment options include cloud, VPC, on-premises, and air-gapped environments.\n\n### Key features\n\n- **Existing policy enforcement** : Applies the rules already defined in your existing policy engines to every tool call, so an injected instruction cannot push an agent past policy you already maintain.\n- **Custom policies** :[Contextual Access](https://docs.arcade.dev/en/guides/contextual-access) hooks run your own policy logic when tools are listed, before a tool executes, and before its result returns to the agent. Each hook can allow, deny, or modify, so teams can hide tools a user should not reach, block risky arguments, and redact or filter tool output before it enters model context.\n- **Per-action authorization** : Evaluates every tool call against the intersection of the user’s permissions and the agent’s scope, so an injected instruction cannot make the agent do more than both allow.\n- **Approval workflows** : Supports configurable[human confirmation](https://www.arcade.dev/blog/approve-once-scale-every-agent/) for sensitive or high-risk actions.\n- **OAuth and secret handling** : Manages OAuth flows and keeps[sensitive credentials outside the model context](https://www.arcade.dev/blog/arcade-keeps-credentials-away-from-llm/) entirely, so a prompt injection cannot extract them.\n- **Prebuilt, custom, and remote MCP tools** : Arcade can host custom MCP servers and register existing remote MCP servers. Their tools enter the same governed catalog under the same policies, whether executed through Arcade SDKs or exposed through Arcade’s MCP Gateway.\n- **Tool- and agent-level governance** : Provides a shared registry, version control, execution logs, and[OpenTelemetry-compatible audit telemetry](https://www.arcade.dev/blog/audit-logs-for-ai-agent-platforms/) that names the agent, user, and system behind every action.\n\n### Pricing\n\n- Customers can start free, followed by a platform fee plus usage-based metering per user challenge and per tool call.\n\n### Key strengths\n\n- Adds deterministic enforcement at the action boundary rather than relying only on probabilistic text classification.\n- Combines delegated authorization, tool execution, and governance for agents that modify records, send messages, trigger workflows, or access sensitive business systems.\n- Provides centralized tool- and agent-level governance and OpenTelemetry-compatible audit logs for tool calls routed through the runtime.\n\n### Where the fit breaks down\n\n- Arcade is not a prompt-injection scanner or text classifier. Teams needing text screening should pair Arcade with a detector or guardrail tool.\n- Arcade’s enforcement applies to actions mediated by the runtime, including Arcade-hosted tools, custom MCP servers, and registered remote MCP servers. Direct API paths that bypass Arcade remain outside that enforcement point.\n\n## Which tool should you choose? Use case recommendations\n\n### Cloud-native production screening\n\nMicrosoft Azure Prompt Shields, Amazon Bedrock Guardrails, and Google Cloud Model Armor provide managed production screening within Azure, AWS, and Google Cloud respectively.\n\n### Model-agnostic managed or self-hosted guardrails\n\nCheck Point AI Guardrails provides a model-agnostic security layer for production traffic across SaaS and self-hosted infrastructure.\n\n### Self-hosted prompt injection classification\n\nMeta Llama Prompt Guard 2 provides self-hosted prompt-injection classification for environments where prompts and retrieved data must remain inside controlled infrastructure.\n\n### Programmable application-level guardrails\n\nNVIDIA NeMo Guardrails provides programmable application-level controls around prompts, retrieval, dialog, and tool calls. It operates at the application layer rather than serving as the downstream action runtime.\n\n### Pre-deployment red-teaming and CI testing\n\ngarak, Microsoft PyRIT, and promptfoo support pre-deployment red-teaming, CI regression testing, security baselining, and adversarial scenario orchestration.\n\nPromptfoo also provides Enterprise Adaptive Guardrails when you want to carry findings from testing into production policy enforcement.\n\n### Action-boundary authorization and policy enforcement\n\nArcade.dev checks every tool call against the user’s permissions, the agent’s scope, and your existing and custom policies before it executes. Pair it with a detector or guardrail from this list: the detector screens content, and Arcade blocks any injected action that fails those checks, including ones the detector missed.\n\n## How to choose and layer prompt injection controls for enterprise AI agents\n\n### Step 1: Decide where the control must run\n\nDecide whether the immediate need is pre-deployment testing, production screening, gateway governance, or downstream action enforcement. Identify deal-breakers such as indirect-injection coverage, RAG scanning, MCP support, air-gapped deployment, or agent tool access.\n\n### Step 2: Match deployment constraints to your existing stack\n\nConfirm whether prompts, retrieved data, and tool payloads can leave your VPC or cloud boundary. Prioritize native controls when you are fully committed to Azure, AWS, or Google Cloud.\n\nCheck whether integrations are native, API-based, gateway-based, or require custom middleware.\n\n### Step 3: Test with your actual agent workload\n\nRun shortlisted tools against your real prompts, retrieved documents, tool arguments, languages, and failure modes. Measure false positives, false negatives, latency impact, and operational alert volume.\n\n### Step 4: Add action-boundary controls when agents can take actions\n\nDo not rely on screening alone if agents can modify business systems. Add a layer that governs tool execution after the model decides what to do. That layer should authorize each action using the user’s existing permissions and the agent’s scoped permissions, enforce your existing and custom policies, and require approval when configured policy calls for it.\n\n### Step 5: Calculate total cost of ownership for prompt injection controls\n\nInclude usage fees, model calls, infrastructure, self-hosting, logging, implementation work, migration effort, and ongoing tuning.\n\n## Conclusion\n\nPrompt-injection detection is one layer of production agent security, not the final enforcement point. Detectors and guardrails can identify or block malicious content, but action-taking agents also need a deterministic decision about whether a tool call is allowed to execute.\n\nThat makes the buying decision a layering problem. Select the detector, scanner, or guardrail that matches the deployment model and threat surface, then verify how downstream actions are authorized, executed, and audited.\n\nFor agents that can change business systems, Arcade.dev makes that decision at the action boundary, checking every tool call against the user’s permissions, the agent’s scope, and your existing and custom policies before it executes. Evaluate the complete path from injected input to attempted action, not the detector in isolation.\n\n## FAQ\n\n### What is the best prompt-injection detection tool for enterprise AI agents?\n\nNo universal best tool exists. Choose based on where you need control: pre-deployment testing, production screening, gateway enforcement, or downstream action authorization.\n\n### What is the difference between a scanner, detector, guardrail, gateway, and action runtime?\n\nScanners test before release. Detectors classify live content. Guardrails apply model or app policies. Gateways govern routed traffic. Action runtimes authorize and execute what agents can actually do.\n\n### Can prompt-injection detection prevent unsafe agent actions?\n\nNot by itself. Detection reduces malicious-instruction exposure, but action-taking agents also need authorization, scoped permissions, approval flows, and audit controls at the [tool-execution layer](https://www.arcade.dev/blog/ai-agent-tool-calling-hierarchy-of-needs/).\n\n### When should teams use a self-hosted prompt-injection classifier?\n\nUse a self-hosted classifier when prompts, retrieved documents, or tool payloads cannot leave controlled infrastructure and the team can operate the model reliably.\n\n### Are action-runtime tools like Arcade.dev prompt-injection detectors?\n\nNo. Action-runtime tools such as Arcade.dev do not classify text as malicious or benign. They complement detectors by enforcing authorization and policy when an action executes, with tool execution and audit handled in the same runtime.\n\n### Do you need both pre-deployment scanners and production detectors?\n\nUsually, yes, for production agent systems. Pre-deployment scanners and red-team frameworks like garak, PyRIT, and promptfoo help find vulnerabilities before release. Production detectors or guardrails inspect live prompts, retrieved context, and responses.\n\n### How should buyers compare benchmark and latency claims?\n\nCompare numbers only when the benchmark setup is exactly comparable: same dataset, attack type, language mix, input length, metric, and deployment environment. Published latency claims can inform shortlisting, but final validation should use the production workload and latency budget being evaluated.\n\n### What should you ask when evaluating indirect prompt injection coverage?\n\nAsk whether the tool can inspect the untrusted content your agent actually consumes: RAG chunks, webpages, emails, PDFs, tool outputs, or MCP payloads. 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