Quick verdict
Choose Prompt Security if: Your primary concern is securing generative AI applications at runtime. You have LLM-powered chatbots, copilots, or agentic AI tools deployed in production and you need prompt injection detection, data redaction, and MCP gateway security that sits inline between your users and your models. You want a platform that enforces policy at the moment an AI interaction happens, not after.
Choose HiddenLayer if: You need AI security that covers the full lifecycle, including predictive and classical ML models, not just LLMs. You want adversarial AI research baked into your defense, model scanning for supply chain vulnerabilities, and runtime monitoring against model theft and evasion attacks. Your AI estate includes custom-trained models deployed in air-gapped or on-premises environments.
At-a-glance comparison
| Criteria | Prompt Security | HiddenLayer |
|---|---|---|
| Security focus | GenAI/LLM runtime protection | Full AI lifecycle (ML + LLM) |
| Core technology | Inline proxy for LLM interactions | ML detection engine + adversarial research |
| Prompt injection defense | Real-time input/output scanning | AI Detection and Response (AIDR) classifiers |
| MCP gateway security | Yes, dynamic risk scoring across MCP servers | Not primary focus |
| Model scanning | Not primary focus | Yes, ModelScan for supply chain |
| Deployment | SaaS, hybrid | SaaS, on-premises, air-gapped, hybrid |
| Research team | Growing | Established, public vulnerability disclosures |
| OWASP LLM Top 10 coverage | LLM01-LLM10 via runtime inspection | LLM01-LLM10 via detection + scanning |
| Gartner Peer Insights | 4.4 stars (7 reviews, AI Security market) | 5.0 stars (1 review) |
| Best for | Production GenAI apps, MCP security | Enterprise AI programs with diverse model types |
How we compared
This analysis is based on publicly available product documentation, security research publications from both vendors, independent reviews on Gartner Peer Insights, third-party comparisons on AppSec Santa, and each vendor’s published technical research. We evaluated both platforms across six criteria: threat detection approach, GenAI/LLM protection, AI supply chain security, deployment flexibility, research depth, and operational fit.
Prompt Security and HiddenLayer represent two different philosophies in the emerging AI security market. Prompt Security focuses narrowly on the interaction layer between users and generative AI. HiddenLayer takes a broader view, treating AI security as a full-stack problem from model development through production.
Head-to-head by criteria
Threat detection: Inline proxy vs ML detection engine
Prompt Security wins on GenAI interaction visibility. It operates as a proxy layer between your AI applications and the tools, models, and data sources they connect to. Every prompt, every response, every tool invocation passes through Prompt Security’s inspection layer. It scans for prompt injection, jailbreak attempts, sensitive data leakage, and policy violations in real time. When it detects a threat, it blocks before the model processes the input or before the user sees the response. This inline architecture means enforcement happens at execution time.
HiddenLayer wins on adversarial AI research depth. Its AIDR engine is built on years of adversarial machine learning research. HiddenLayer’s team has publicly disclosed novel attack techniques including the Policy Puppetry attack (April 2025), which demonstrated a universal bypass for all major LLM guardrails by reformulating prompts as policy files like XML or JSON. In October 2025, they showed how OpenAI’s guardrails framework could be subverted by prompt injecting both the model and its guardrails simultaneously. This research feeds directly into the product: HiddenLayer’s detectors are trained on attack patterns that most vendors have not even catalogued yet.
HiddenLayer’s own research on prompt injection datasets (May 2025) makes a key distinction: “We focus on prompt injections in particular because this threat is more directly aligned with application-specific security and an attacker’s economic incentives.” They separate prompt injection from jailbreaking, treating injection as a security problem and jailbreaking as a safety problem. This framing matters when you are deciding how to allocate your security budget.
GenAI and LLM protection: Runtime guardrails vs model-level defense
Prompt Security wins on MCP gateway security. Model Context Protocol (MCP) is the emerging standard for connecting AI agents to enterprise tools. A single MCP server can expose an agent to dozens of downstream systems. Prompt Security sits between the AI client and MCP servers, inspecting every interaction, assessing risk, and enforcing policy before data moves. For organizations deploying agentic AI at scale, this MCP gateway capability is a structural advantage: it protects the connections that legacy security tools cannot even see.
Prompt Security also handles the GenAI-specific threat surface that CISOs lose sleep over: employees pasting sensitive data into ChatGPT, shadow AI tool usage across the organization, and data exfiltration through model outputs. Its redaction engine strips PII, secrets, and regulated data from prompts and responses without blocking legitimate AI usage.
HiddenLayer wins on model-level security and supply chain. Its ModelScan tool scans AI models for vulnerabilities, backdoors, and malicious code before deployment. For organizations pulling models from Hugging Face or other open-source repositories, this is essential. HiddenLayer’s AI Bill of Materials (AIBOM) capability tracks model lineage, provenance, and dependencies, which matters for compliance with emerging regulations like the EU AI Act.
HiddenLayer’s taxonomy of adversarial prompt engineering (published June 2025) demonstrates the research depth behind its detection engine. While “prompt injection” is often used as a catch-all term, HiddenLayer’s research catalogs distinct techniques that existing frameworks like OWASP Top 10 for LLMs and MITRE ATLAS do not adequately capture. This research translates into detection coverage that point-solution vendors cannot match without comparable investment in adversarial AI science.
Deployment: SaaS-centric vs anywhere
HiddenLayer wins on deployment flexibility. It supports SaaS, on-premises, air-gapped, and hybrid deployments. For government agencies, defense contractors, and financial institutions that cannot send model data to a third-party cloud, HiddenLayer’s on-premises and air-gapped options are non-negotiable. The platform was designed for environments where models contain proprietary or classified information.
Prompt Security is primarily SaaS with some hybrid deployment support. For organizations comfortable with cloud-delivered security, this is fine. For organizations with strict data residency or air-gap requirements, HiddenLayer is the more flexible option.
AI lifecycle coverage
HiddenLayer wins on breadth. Its platform covers the full AI security lifecycle: AI discovery (finding all models and AI assets in your environment), supply chain security (scanning models and dependencies), attack simulation (automated red teaming and system prompt evaluation), and runtime security (AIDR monitoring against model theft, evasion, and adversarial attacks). It works across generative AI, predictive AI, and classical ML models.
Prompt Security wins on depth in the GenAI runtime layer. It does one thing and does it well: securing the interaction between users and generative AI applications. For organizations whose AI security concerns are primarily about LLM-powered applications, Prompt Security’s focused approach means fewer false positives, simpler configuration, and faster time to protection for the most urgent GenAI risks.
Use cases
Securing customer-facing chatbots and LLM-powered products: Prompt Security. Its inline proxy architecture sits directly in the request/response path and blocks prompt injection, jailbreaks, and data leaks at runtime. If your product depends on LLM reliability and you cannot afford a single toxic output or data leak, Prompt Security’s enforcement model is purpose-built for this.
Enterprise AI program with custom-trained models across multiple types: HiddenLayer. If your AI estate includes classical ML models for fraud detection, computer vision models for quality inspection, and LLMs for internal productivity, you need a platform that covers all of them. HiddenLayer’s full-lifecycle approach and model-agnostic detection engine handle this diversity.
Air-gapped or classified environments: HiddenLayer. On-premises and air-gapped deployment options make it the only choice for defense, intelligence, and critical infrastructure organizations that cannot send model data to external clouds.
Shadow AI and MCP sprawl: Prompt Security. Its discovery capabilities find unsanctioned AI tool usage and its MCP gateway secures the connections between agents and enterprise systems. For organizations where AI adoption is outpacing security governance, Prompt Security provides visibility and control without rearchitecting the AI stack.
Red teaming and adversarial testing before deployment: HiddenLayer. Its AI Attack Simulation module provides automated system prompt evaluation and red team exercises that identify vulnerabilities before models reach production. Combined with ModelScan for supply chain threats, HiddenLayer covers the pre-deployment side of the AI security lifecycle that runtime-only tools miss. For the broader context on securing large language models, see our guide on LLM security.
Pros and cons
Prompt Security
Pros: Strongest inline GenAI protection available. MCP gateway security is a genuine differentiator with no clear equivalent from HiddenLayer. Real-time prompt injection detection and data redaction operate at production latency. Shadow AI discovery catches unsanctioned tool usage. Policy enforcement is simple to configure. Integrates with major LLM providers and SIEM/SOAR platforms.
Cons: Limited to GenAI/LLM use cases. No model-level supply chain scanning. No support for non-LLM AI models. Deployment options are narrower (limited air-gap support). Smaller research team compared to HiddenLayer. Younger company with less public adversarial research track record. Not suited for on-premises-only requirements.
HiddenLayer
Pros: Broadest AI security platform across model types. Deep adversarial ML research team with public vulnerability disclosures. ModelScan for supply chain security. AIBOM for compliance and provenance tracking. Deployable anywhere (SaaS, on-premises, air-gapped). Automated red teaming and system prompt evaluation. Supports both generative and predictive AI.
Cons: Less focused on the GenAI interaction layer. No dedicated MCP gateway capability. Complexity reflects its breadth; configuration requires AI security expertise. Higher operational overhead. Gartner Peer Insights has limited review volume (1 review). Some capabilities overlap with broader cloud security platforms.
Pricing comparison
| Prompt Security | HiddenLayer | |
|---|---|---|
| Pricing model | Usage-based (per prompt/token) | Platform subscription (per model/endpoint) |
| Free tier | Not publicly documented | Not publicly documented |
| Enterprise | Custom pricing | Custom pricing |
| Deployment included | SaaS (primary) | SaaS, on-premises, air-gapped |
| Best value for | High-volume GenAI apps needing runtime guardrails | Diverse AI portfolios needing full-lifecycle security |
Both vendors offer custom enterprise pricing based on deployment scale, model count, and feature selection. Neither publishes transparent pricing, which is common in the early-stage AI security market.
FAQ
Does HiddenLayer handle prompt injection?
Yes. HiddenLayer’s AIDR engine detects prompt injection and jailbreak attempts at runtime, supported by its extensive adversarial prompt engineering research. Its coverage is research-driven and covers both direct and indirect injection techniques. The difference from Prompt Security is architectural: HiddenLayer monitors and detects, while Prompt Security sits inline and blocks before execution.
Does Prompt Security scan AI models for vulnerabilities?
No. Prompt Security focuses on the GenAI runtime layer: prompts, responses, and tool invocations. For model scanning and supply chain security, HiddenLayer or Protect AI are better fits. Some organizations use both: Prompt Security for production guardrails and HiddenLayer or Protect AI for pre-deployment model validation.
Which is better for securing AI coding assistants like GitHub Copilot or Cursor?
Prompt Security has a clearer play here because it can govern what data flows into and out of coding assistant tools via its inline inspection layer. HiddenLayer’s strength is more in securing the models themselves and detecting adversarial behavior, rather than governing employee usage of third-party AI tools.
What is MCP gateway security and why does it matter?
Model Context Protocol (MCP) is an open standard for connecting AI agents to enterprise tools, APIs, and data sources. A compromised MCP server can expose an agent to dozens of downstream systems. Prompt Security sits between AI applications and MCP servers, inspecting interactions, assessing risk, and enforcing policy. HiddenLayer does not currently offer a comparable MCP gateway capability.
How do these platforms compare to open-source alternatives?
For pre-deployment testing, open-source tools like Garak (maintained by NVIDIA), PyRIT (Microsoft), and Promptfoo are strong options that cost nothing. For runtime protection, LLM Guard and NeMo Guardrails provide open-source guardrail frameworks. Commercial platforms like Prompt Security and HiddenLayer add enterprise features (scalable deployment, compliance reporting, SLA-backed detection, dedicated research teams) on top of what open-source tools provide. For a small engineering team, the open-source stack is viable. For enterprise production deployments with regulatory requirements, the commercial platforms justify their cost through reduced operational burden and faster incident response.
Final recommendation
Prompt Security and HiddenLayer do not actually compete for the same buyer. They solve adjacent problems that most organizations will eventually need to address.
If your AI security problem today is GenAI applications in production, choose Prompt Security. It protects what is most likely to go wrong right now: a prompt injection attack that exfiltrates customer data, an employee pasting source code into a public LLM, an AI agent that invokes a tool it should not access. Its inline architecture means you block threats at execution time, which is the only model that works at production scale.
If your AI security problem is managing risk across a growing AI portfolio that includes multiple model types, choose HiddenLayer. Its research-driven detection, supply chain scanning, and broad model coverage make it the right foundation for an enterprise AI security program that will expand over time. And if you operate in environments where data cannot leave your premises, HiddenLayer’s deployment flexibility makes it the only realistic choice between these two.
For organizations that deploy both customer-facing LLM applications and internal ML models, the two platforms are complementary. Prompt Security handles the GenAI runtime layer where user interaction is the primary threat vector. HiddenLayer handles the model development and supply chain layer where adversarial manipulation and model theft are the primary concerns. The teams running production GenAI at scale in 2026 often use both.