Best LLM Tool Integration Security Tools in 2026: 7 Tools Compared

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By the Ainotely Editorial Team.
Updated November 2026. 10 min read. Prices and features researched from each vendor's official pages and app store listings at time of writing.
Best LLM Tool Integration Security Tools
Quick picks
  1. 1Lakera Guard logo
    Lakera GuardFiltering prompt injections
    Visit
  2. 2Protect AI logo
    Protect AISecuring AI and agent systems
    Visit
  3. 3HiddenLayer logo
    HiddenLayerThreat detection for AI models
    Visit
  4. 4Pangea AI Guard logo
    Pangea AI GuardAPI-based AI safety checks
    Visit
  5. 5Portkey AI Gateway logo
    Portkey AI GatewayManaging model and tool calls
    Visit
Short version:
In this guide Quick comparisonThe best tools for secure LLM tool integrationHow we picked thesePractices that keep LLM tool use safeFrequently asked questionsSources

Quick comparison

ProductIdeal forPrice fromFree version
Lakera GuardFiltering prompt injectionsSee websiteNoVisit
Protect AISecuring AI and agent systemsSee websiteNoVisit
HiddenLayerThreat detection for AI modelsSee websiteNoVisit
Pangea AI GuardAPI-based AI safety checksSee websiteNoVisit
Portkey AI GatewayManaging model and tool callsFreeYesVisit
Kong AI GatewayGateway controls for AI trafficSee websiteNoVisit
Azure AI Content SafetyMicrosoft-based AI safety checksSee websiteNoVisit

The best tools for secure LLM tool integration

When a language model can call tools, read files or send messages, a prompt injection can turn into a real action. The tools below help teams guard that boundary: filtering hostile prompts, watching tool calls, enforcing permissions and logging what agents do. They work best beside good practice, such as least-privilege tool access and human approval for sensitive actions.

Lakera Guard logo
#1

Lakera Guard

Ideal for: Filtering prompt injections

Lakera Guard website
lakera.ai homepage

Lakera Guard checks prompts and model inputs for injection and jailbreak attempts before they reach the model. Its API returns a risk assessment in milliseconds, so teams can block or flag hostile input without slowing normal use. It covers direct injections, indirect injections from tool outputs and common jailbreak patterns.

Pricing is aimed at production teams rather than hobby projects, and any guard needs tuning against your own prompts to avoid false blocks. It also cannot fix a deeper design flaw, such as giving an agent tools it should never have. As one layer in a wider safety setup, it is a strong choice.

For readers focused on filtering prompt injections, Lakera Guard is worth a trial before deciding, since the free tier or trial shows its real feel fast. Check the current plan details on the vendor site, since prices and limits change, and confirm that the version you need works on your devices.

The good and the bad
  • Detects prompt injection and jailbreak attempts
  • Low-latency API
  • Covers major attack types
  • Enterprise pricing
  • Needs tuning for your prompts
Protect AI logo
#2

Protect AI

Ideal for: Securing AI and agent systems

Protect AI website
protectai.com homepage

Protect AI covers several layers of AI security, including guards for LLM traffic and tools for model supply-chain risks. Its guard product inspects prompts and responses for injection and unsafe content, while its wider platform tracks models and dependencies that teams deploy. That breadth suits organizations running many AI systems at once.

The platform can feel heavy for a team that only needs one guard, and pricing is enterprise level. Smaller teams may prefer a single-purpose guard. For organizations standardizing AI security across many projects, the combined coverage is useful.

We would shortlist Protect AI for securing AI and agent systems, and compare it directly with the two options nearest it in price before paying. Check the current plan details on the vendor site, since prices and limits change, and confirm that the version you need works on your devices.

The good and the bad
  • Guards for LLM and agent traffic
  • Model and supply-chain security
  • Policy controls
  • Broad platform can be heavy for small teams
  • Enterprise pricing
HiddenLayer logo
#3

HiddenLayer

Ideal for: Threat detection for AI models

HiddenLayer website
hiddenlayer.com homepage

HiddenLayer focuses on detecting attacks against AI models and agents, including prompt injection, data theft attempts and model manipulation. Its monitoring watches model traffic for patterns that suggest an active attack, and it is built for security teams that need alerts they can act on.

It is an enterprise product that assumes a security team will run it, so smaller teams may find it more than they need. It also works best alongside gateway controls and logging rather than alone. For companies treating AI as critical infrastructure, it adds a dedicated detection layer.

If threat detection for AI models is your main need, HiddenLayer deserves a place on a shortlist, provided you test the catch described above first. Check the current plan details on the vendor site, since prices and limits change, and confirm that the version you need works on your devices.

The good and the bad
  • Detects attacks on models and agents
  • Covers prompt and data risks
  • Enterprise monitoring
  • Enterprise focus
  • Setup needs security team involvement
Pangea AI Guard logo
#4

Pangea AI Guard

Ideal for: API-based AI safety checks

Pangea AI Guard offers API services that check prompts and content for injection, sensitive data and unsafe material. Its developer-friendly docs make it easy to add a check before a prompt reaches a model or before a tool result is acted on, and its redaction service can strip secrets from text.

Usage-based pricing can grow with traffic, so high-volume teams should model costs. It is also narrower than full AI security platforms, covering checks rather than the whole agent lifecycle. For teams that want a quick, well-documented guard API, it is a practical pick.

Our view is that Pangea AI Guard earns its place here for aPI-based AI safety checks, even with the limits noted above. Check the current plan details on the vendor site, since prices and limits change, and confirm that the version you need works on your devices.

The good and the bad
  • Simple API for prompt and content checks
  • Redaction and guard services
  • Clear docs
  • Usage-based pricing can grow
  • Narrower than full platforms
Portkey AI Gateway logo
#5

Portkey AI Gateway

Ideal for: Managing model and tool calls

Portkey AI Gateway sits between your app and model providers, centralizing API keys, rate limits, retries and logging. That central view matters for tool-using agents, because it records which models were called and when, and its guardrails support lets teams add safety checks in one place. Fallback across providers also improves reliability.

Advanced features sit behind paid plans, and self-hosting adds operational work. It does not by itself block prompt injection, so pair it with a guard and strict tool permissions. As the control point for model traffic, it is a sensible foundation for agent safety.

For readers focused on managing model and tool calls, Portkey AI Gateway is worth a trial before deciding, since the free tier or trial shows its real feel fast. Check the current plan details on the vendor site, since prices and limits change, and confirm that the version you need works on your devices.

The good and the bad
  • Central gateway with keys, limits and logging
  • Guardrails support
  • Provider fallback
  • Advanced features are paid
  • Self-hosting adds ops work
Kong AI Gateway logo
#6

Kong AI Gateway

Ideal for: Gateway controls for AI traffic

Kong AI Gateway extends the widely used Kong gateway with AI-specific plugins for authentication, rate limiting, prompt safety and logging. Teams already running Kong can add AI traffic controls without a new platform, and the plugin model lets them pick exactly which checks apply to each route.

Setup is enterprise work, and the plugin model needs configuration to get right. Teams without Kong experience may prefer a dedicated AI gateway. For organizations already standardized on Kong, it is the most natural way to govern agent and model traffic.

We would shortlist Kong AI Gateway for gateway controls for AI traffic, and compare it directly with the two options nearest it in price before paying. Check the current plan details on the vendor site, since prices and limits change, and confirm that the version you need works on your devices.

The good and the bad
  • Plugin-based AI controls
  • Rate limiting and auth
  • Enterprise scale
  • Enterprise setup
  • Plugin model needs configuration
Azure AI Content Safety logo
#7

Azure AI Content Safety

Ideal for: Microsoft-based AI safety checks

Azure AI Content Safety filters prompts and outputs for harmful content, including jailbreak and injection patterns, and it integrates closely with Azure OpenAI services. Teams building agents on Azure can add filtering with minimal new infrastructure, and policy controls let them tune what gets blocked.

Its value is highest inside the Azure ecosystem, and teams on other clouds should compare standalone guards. Pricing is usage-based, so high-volume agents need cost estimates. For Azure-based agent projects, it is the simplest safety check to add first.

If microsoft-based AI safety checks is your main need, Azure AI Content Safety deserves a place on a shortlist, provided you test the catch described above first. Check the current plan details on the vendor site, since prices and limits change, and confirm that the version you need works on your devices.

The good and the bad
  • Content filtering for prompts and outputs
  • Integrates with Azure OpenAI
  • Policy controls
  • Tied to Azure ecosystem
  • Usage-based pricing

How we picked these

We compared official feature pages, pricing pages, app store listings, privacy policies and public reviews for each option below. We checked that each product is still available and that its stated plans match what the vendor publishes. We did not run hands-on tests, so our picks rest on documented features, pricing and the fit for the reader described in each section, not on lab results. Where a price could not be confirmed on the vendor site at the time of writing, we wrote See website rather than guessing. We also looked at how each option handles the everyday details that decide whether a tool gets used: setup time, device support, what happens when you cancel or downgrade, and whether help is available when something breaks. A product that looks strong on a feature list but is hard to set up, hard to leave, or poorly supported will cost more in time than it saves, so we weighed those practical points alongside price and features. Where reviewers or users reported a recurring problem, we noted it in the honest catch for that product, rather than leaving it out because the vendor page did not mention it.

Practices that keep LLM tool use safe

Give each agent the smallest set of tools that completes its task, and require human approval for irreversible actions such as sending money, deleting data or changing access. Validate tool outputs before acting on them, since a tool can return hostile text that tries to steer the next step. Keep secrets out of prompts, rotate API keys, and log every tool call with its inputs so incidents can be traced. Finally, test with hostile prompts during development, because the first real injection attempt should not be the first time your guard is tried.

A final check before you commit

Run a short trial on a real task before you pay for a year or move a whole team. Use the free plan or trial to complete one full job, from setup to export or delivery, and note where you got stuck. Check the cancellation path too: can you leave and keep your data, or does leaving mean starting over? Read the pricing page for renewal rates, not just the first-year offer, and confirm what support you get on the plan you will actually buy. Finally, ask one current user about the worst part of the product, since that answer tells you more than any feature list. If the trial feels good on a real task and the exit path is clear, the choice is much safer.

Frequently asked questions

What is an LLM tool integration?

An LLM tool integration lets a language model call outside functions, such as searching files, querying a database or sending a message. The model decides when to call a tool and uses the result to continue its task.

What is prompt injection?

Prompt injection is hostile text hidden in a web page, file or message that tries to make a model follow instructions it was not given. When a model can call tools, injected text can lead to unwanted actions, which is why guards and permissions matter.

Do I need a dedicated guard tool?

Teams running agents with sensitive tools benefit from one, since a guard filters hostile prompts and watches outputs. Smaller projects can start with least-privilege permissions, input validation and logging, then add a guard as usage grows.

What is an AI gateway?

An AI gateway sits between your app and model providers, handling API keys, rate limits, retries and logging in one place. It also lets teams add guardrails and switch providers without changing every integration.

Should agents ever act without approval?

Low-risk actions such as reading public data can run automatically. Irreversible or sensitive actions, including payments, deletions and external messages, should require human approval or a strict allow-list.

Sources

  1. https://www.lakera.ai
  2. https://protectai.com
  3. https://hiddenlayer.com
  4. https://pangea.cloud
  5. https://portkey.ai
  6. https://konghq.com
  7. https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety