Arcade.dev has released a comprehensive guide comparing ten tools designed to mitigate prompt injection risks in production AI agents. The analysis moves beyond simple detection, arguing that for agents capable of executing business actions—like sending emails or modifying CRM records—controlling the outcome of a missed detection is just as critical as the detection itself. The report categorizes solutions into red-team testing, production screening, guardrails, gateway controls, and action authorization layers.

The Four Layers of Defense

The guide segments the market into four distinct control points: pre-deployment scanners, production screening tools, gateways/frameworks, and action-boundary controls. For cloud-native production screening, Microsoft Azure Prompt Shields, Amazon Bedrock Guardrails, and Google Cloud Model Armor are highlighted as managed options within their respective ecosystems. Meanwhile, Check Point AI Guardrails (incorporating Lakera Guard) is positioned as the primary choice for model-agnostic, multi-cloud environments, supporting SaaS, on-premises, and air-gapped deployments. For teams prioritizing infrastructure control, Meta Llama Prompt Guard 2 offers a self-hosted classification model with 86M and 22M parameter variants. However, the report notes its 512-token context window limitation, requiring longer documents to be segmented before screening. Pre-deployment testing is covered by NVIDIA garak for vulnerability scanning, Microsoft PyRIT for adversarial scenario orchestration, and promptfoo for CI/CD regression checks. These tools identify weaknesses but do not block live attacks in production.

Action Authorization as the Final Gate

The critical distinction Arcade.dev draws is between content blocking and action authorization. Most tools, including Azure Prompt Shields and Amazon Bedrock Guardrails, screen content and return signals or block requests based on policy. They do not, however, handle delegated end-user authorization for downstream business actions. This is where Arcade.dev’s own solution differentiates itself by acting as an action runtime. It checks every tool call against user permissions, agent scope, and custom policies before execution, ensuring that even if an injected instruction bypasses a detector, the agent cannot execute an unauthorized action. NVIDIA NeMo Guardrails is also noted for programmable application guardrails, offering configurable controls for input, output, retrieval, and tool calls. However, the report emphasizes that NeMo requires the application to own the authorization and execution logic, whereas an action runtime like Arcade.dev handles the identity-aware policy enforcement directly at the tool-call boundary.

Key Takeaways

  • Detection alone is insufficient for agents with write-access; action authorization is required to prevent unauthorized business logic execution.
  • Cloud-native teams should look to Azure Prompt Shields, Bedrock Guardrails, or Model Armor for integrated managed screening.
  • Self-hosted and air-gapped environments can utilize Meta Llama Prompt Guard 2, though it requires significant MLOps overhead and token segmentation.
  • Pre-deployment tools like garak, PyRIT, and promptfoo are essential for finding vulnerabilities but do not provide inline production defense.
  • Arcade.dev positions its action runtime as a necessary layer to enforce permissions and policies on tool calls that get past content detectors.

The Bottom Line

If your AI agents can write to databases or send messages, you have outgrown simple prompt filters. The real security gap isn't in detecting the injection, but in authorizing the resulting action; without an action runtime, your agent is just a very polite hacker waiting for a slip-up.