TL;DR
LLM security tools fall into six groups, and each one guards a single point on the path from a user’s prompt to a model’s answer. Input filters catch prompt injection and jailbreaks before they reach the model. Output filters and data-leak tools also stop personal data and secrets from leaving in responses. Gateways and runtime guardrails apply policy to every call, while scanners and red-team kits find weaknesses before release. Supply-chain scanners check model files and packages, and monitoring tools then record what happened. Choose by deployment pattern, test in a short proof of concept, and treat agent permissions as a separate job.
Key Takeaways No single LLM security tool covers the whole request path, so enterprises combine controls at the input, the runtime, the output and the build pipeline. Prompt-injection detectors lower risk, but OWASP says it is unclear whether fool-proof prevention exists at all. Data-leak tools catch things with a shape, such as card numbers and API keys, while confidential business facts need classification and access rules. Gateways enforce policy on traffic, whereas guardrail libraries enforce rules inside the application, so larger estates usually need both. Several well-known tools changed owners or repositories in 2025 and 2026, so check the vendor and the license before you shortlist. A two-week proof of concept with attack and benign test sets tells you more than any vendor ranking. Watch on YouTube
What Does It Take to Deploy an Enterprise LLM?
A Kanerika conversation on what it takes to run a large language model in production, from data and architecture to the controls that keep it safe.
Your LLM App Is a Route, and Each Tool Guards One Junction
Picture your LLM application as a city map. A request starts with the user and passes a gateway. Then it picks up documents from a search index, reaches the model, calls a tool or two and returns as an answer.
Each of those stops is also a junction where something can go wrong.
Vendor lists of LLM security tools read like a map legend with no streets drawn. They name the symbols, such as firewall, guardrail and scanner, yet never show which junction each one watches. So buyers end up with three guards on the same corner and nobody at the exit where data leaves.
Draw the route first. Then the shortlist almost writes itself, because every tool either guards a junction you care about or it does not.
What LLM Security Tools Do, One Control Point at a Time
LLM security tools are software and services that detect, block, test or record risky behavior in applications built on large language models. Compare them by control point, because two products with the same label often protect different stops on the route.
The Six Control Points
Six groups cover nearly every product on the market today, so the table sorts them by junction. The last column matters most, because it tells you which other control has to exist before the tool earns its keep.
Control point What it protects Where it sits Example tools What it does not cover Prompt-injection and jailbreak detection The model’s instructions User input and retrieved content Check Point AI Guardrails (formerly Lakera Guard), Azure Prompt Shields, Llama Prompt Guard 2 User permissions and tool access Output filtering and data-leak prevention Personal data, secrets, unsafe replies Before the response leaves Presidio, LLM Guard, Google Model Armor Confidential facts that match no pattern Gateways and runtime guardrails Policy on every call Model boundary and app runtime Kong AI Gateway, LiteLLM, NeMo Guardrails, Guardrails AI Authorization inside downstream systems Vulnerability scanners and red-team kits Weak spots found before release Development and CI/CD garak, promptfoo, PyRIT, Giskard Live traffic Model and dependency scanning Model files and packages Build and model intake ModelScan, Prisma AIRS Runtime behavior Monitoring and incident detection A record of what happened Production telemetry Langfuse, Arize Phoenix Stopping an attack in progress
Read the table from right to left when you build a budget. A prompt filter without access control, or a monitor without anyone watching it, leaves the gap in the last column wide open.
Where the OWASP Top 10 for LLM Applications Fits
The OWASP Top 10 for LLM Applications , now in its 2025 edition, is the closest thing the field has to a shared threat list. It helps here because each risk points to a different control point, which keeps a shortlist honest.
OWASP 2025 risk First control to look at LLM01 Prompt Injection Input detection plus runtime guardrails LLM02 Sensitive Information Disclosure Output filtering and data-leak prevention LLM03 Supply Chain Model and dependency scanning LLM04 Data and Model Poisoning Supply-chain scanning and data provenance checks LLM05 Improper Output Handling Output validation before any downstream use LLM06 Excessive Agency Agent permissions and approvals, mostly outside security tools LLM07 System Prompt Leakage Output filtering and red-team tests LLM08 Vector and Embedding Weaknesses Retrieval permissions and retrieval rails LLM09 Misinformation Grounding checks, evaluation and human review LLM10 Unbounded Consumption Gateway rate limits and budgets
Two rows stand out, though. Excessive agency and misinformation depend more on design and review than on any product. So the gaps section near the end returns to them.
For the full threat model behind each row, our LLM security guide walks through the controls one by one.
Prompt-Injection and Jailbreak Detection Tools
Input filters inspect text before the model acts on it. The better ones look at two sources. The OWASP prompt-injection entry draws the same line, between direct injection typed by a user and indirect injection hidden in websites or files.
Detection methods vary from tool to tool. Some rely on pattern rules, many use a trained classifier, and a few ask a second model to judge intent. Each returns a verdict, so check whether a product blocks the request itself or only hands your code a score.
Options to Compare
Lakera lists prompt injection, indirect prompt injection and data leaks among the risks it manages. Check Point announced its deal to acquire Lakera on September 16, 2025.Azure AI Content Safety Prompt Shields analyzes text for user prompt attacks and for document attacks hidden in supplied content.Amazon Bedrock Guardrails includes a Prompt Attack category in its content filters, so you can configure how strictly it acts.Google Cloud Model Armor screens prompts and responses for injection and jailbreak attempts, and it also works with models hosted on other clouds.Llama Prompt Guard 2, from Meta’s Purple Llama project, is a small classifier released in 22M and 86M sizes for screening inputs. LLM Guard is an MIT-licensed library whose prompt scanners, such as PromptInjection, Secrets and Anonymize, run before the model sees the text.Cloud-native options suit teams already committed to that cloud, since they plug into the same identity and logging. Open-source classifiers, on the other hand, suit teams that must run detection inside their own network.
What Input Filters Cannot Stop
OWASP is blunt on this point. Its guidance says that, given how models work, it is unclear whether fool-proof prevention for prompt injection exists. So a detector lowers the odds that a bad instruction gets through, and the design still has to assume that some will.
An agent with broad permissions can still do damage once one injected instruction slips past the filter. That is why input detection pairs with least-privilege access, which our guide to AI access control for agents covers in detail.
Output Filtering and Data-Leak Prevention Tools
Output controls check what leaves the model before a user, a log or another system sees it. Typical catches are personal data, credentials and policy-breaking text. Some also mask sensitive values on the way in, so the model never sees them at all.
Presidio is an MIT-licensed framework for detecting, redacting, masking and anonymizing personal data in text, images and structured data. It began at Microsoft and now lives in the data-privacy-stack organization on GitHub.Google Cloud Sensitive Data Protection inspects and de-identifies sensitive text, and Model Armor calls it for its own data checks.Amazon Bedrock Guardrails can redact personal data in inputs and responses, although AWS notes that reasoning content blocks are excluded. LLM Guard runs output scanners on responses in the same way its prompt scanners run on inputs. Kong AI Gateway has an AI Sanitizer plugin that redacts personal data before a prompt reaches the upstream model provider.Pattern-based detection works well on anything with a shape, such as card numbers, email addresses and API keys. It struggles with a sentence like “we plan to close the Denver plant in March,” which matches no pattern and is still confidential.
Closing that gap takes classification and access rules at the source, so the model never retrieves what the user may not see. Tools such as Microsoft Purview handle the labeling side, and the basics of enterprise data security still apply to every index you feed a model. Our data governance services also set up those rules as part of the same project.
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Strategies to Reduce LLM Security Risks
An on-demand Kanerika webinar on the practical controls that cut the security and financial risk of enterprise LLM deployments.
Watch the Webinar → Runtime Guardrails and LLM Gateways
A gateway sits between your applications and the model providers, so every call passes one checkpoint. A guardrail library, by contrast, runs inside the application. It can therefore see conversation state, retrieved chunks and tool calls that a gateway may never see.
NVIDIA NeMo Guardrails is open source under Apache 2.0 and supports five rail types. Input, dialog, retrieval, execution and output rails each act at a different step, including the input and output of tools.Guardrails AI is an Apache 2.0 framework built around validators. In July 2026 the project said validators are moving to standard PyPI packages. It also said hosted remote inferencing would end, with a planned cutoff of August 25, 2026.LiteLLM’s AI Gateway can run guardrails before a call and after the response , including third-party checks such as Lakera. Kong AI Gateway has a separate plugin for each job. AI Prompt Guard blocks listed topics, while AI Semantic Prompt Guard catches injection phrased in natural language. Gateway or Guardrail First?
Start with the gateway when many teams call many models and you need one place for keys, rate limits, budgets and logs. Start with a guardrail library when one high-risk application needs rules that depend on the conversation. A refund flow that must stop after a failed identity check is a typical case.
Larger estates usually end up with both, and neither replaces authorization in the systems an agent touches. Our explainer on LLM gateways covers the traffic layer, while the guide to AI guardrails shows how to design the rules themselves.
LLM Vulnerability Scanners and Red-Team Tools
Scanners attack your application on purpose, so you find weak spots before users or attackers do. They belong in development and in the release pipeline, so on their own they protect nothing in production.
garak is NVIDIA’s Apache 2.0 LLM vulnerability scanner, and it probes for hallucination, data leakage, prompt injection and misinformation.promptfoo is an MIT-licensed CLI and library for testing prompts, agents and RAG apps, with red teaming and CI/CD integration. OpenAI announced on March 9, 2026 that it is acquiring Promptfoo and will keep building the open-source project.PyRIT is Microsoft’s MIT-licensed red-teaming toolkit. Its home is now the microsoft/PyRIT repository, and the old Azure/PyRIT repository was archived on March 27, 2026, so update any pinned links.Giskard publishes an Apache 2.0 library for evaluating and testing LLM agents.This guide keeps the testing tools short on purpose, because other posts cover them in depth.
For how to run an attack campaign, see our walkthrough of LLM red teaming . For the buyer’s view of outside testers, read the AI red teaming guide. Test layers built for agents are covered in our guide to agentic AI testing .
Checklist
Generative AI Checklist for Secure Adoption
A practical checklist for adopting generative AI securely, from data handling and approved tools to testing and governance, ready to use beside your tool shortlist.
Get the Checklist → Model and Dependency Supply-Chain Scanners
Models and packages arrive from outside your company, and either one can carry code. Some model formats, such as Python pickle files, can run code when they load. That is why model scanners and safer file formats exist.
ModelScan from Protect AI is an Apache 2.0 scanner that checks model files for unsafe code. It also supports the H5, Pickle and SavedModel formats.Safetensors is an Apache 2.0 format for storing model weights safely instead of with pickle. Choosing it removes a class of risk rather than detecting it.Prisma AIRS comes from Palo Alto Networks, which completed its acquisition of Protect AI on July 22, 2025. The company says the combined platform covers model scanning, posture management, AI red teaming, runtime protection and agent security. When the Security Tool Became the Way In
The LiteLLM incident in March 2026 shows why this layer matters. Two LiteLLM releases on PyPI, versions 1.82.7 and 1.82.8, were compromised. According to the LiteLLM security update , they were live for about 40 minutes on March 24 before PyPI quarantined them.
The maintainers believe the breach began with Trivy, a security scanner in their own CI/CD workflow. In other words, a security tool became the entry point. LiteLLM also reports that customers running its official Docker image were not affected.
The lesson for buyers is therefore simple. Pin versions with hashes, scan the tools that scan you, and list your security stack in the same software inventory as everything else.
Monitoring and Incident Detection for LLM Apps
Monitoring tools record prompts, responses, tool calls and costs, so you can see what happened and when. Most began as observability platforms, so their security value depends on the rules and evaluations you configure.
Langfuse is an open-source tracing and evaluation platform you can self-host. Code outside its enterprise directories is MIT-licensed.Arize Phoenix traces LLM applications with OpenTelemetry-based instrumentation and runs evaluations. It uses the Elastic License 2.0, which has different terms from MIT or Apache, so ask your legal team to review it.Whatever you choose, send security events to the place your security team already watches. A jailbreak attempt, a blocked data leak and a denied tool call belong in the same queue as any other alert, with the trace attached.
Log enough to rebuild a session, and then protect the logs too, because prompts often hold the very data you are guarding. For a wider product list, see our roundup of AI observability tools and the deeper guide to LLM observability .
Which LLM Security Tools You Need by Deployment Pattern
The right shortlist depends on what the application can reach, because risk grows with access. A chat app on a hosted API has a smaller blast radius than an agent that can send email or move money.
Deployment pattern Main risks Controls to put in first Add next Chat app on a hosted model API Sensitive input, leaked output, misuse of keys Gateway, input and output screening Monitoring, periodic scans RAG application Indirect injection, documents the user should not see Retrieval permissions, retrieval rails, document scanning Output filtering, tracing Agent that uses tools Unauthorized actions, tool misuse, data exposure Agent identity, scoped permissions, tool-call checks, approvals Runtime monitoring, red teaming Self-hosted open-weight model Unsafe model files, vulnerable packages Model and dependency scanning, safe formats Runtime filters, gateway
Treat the table as a starting point rather than a rule. A RAG app over HR files may need agent-grade controls, while an internal agent with read-only access may need fewer.
For RAG, the biggest single fix is to apply the user’s own permissions at retrieval time, which no prompt filter can do. Our primer on retrieval-augmented generation also shows where that check sits. For agents, servers built on the Model Context Protocol widen the tool surface quickly, so keep every tool behind an allow-list.
A Layered Reference Stack
Put together, the controls form a stack that follows the route from the first section. Each layer gets an independent check where data or actions cross a boundary.
Identity for the user or workload making the request. A gateway with input screening, rate limits and budgets. Retrieval permissions and checks on documents entering the context. Runtime guardrails on the model and on every tool call. Authorization and approvals at each system the application touches. Output filtering before anything reaches the user. Testing, supply-chain scanning and monitoring run across all six layers. They do not sit at one junction, so plan them as shared services instead of per-app add-ons.
Build or Buy: Open-Source Libraries or Commercial Platforms
Open-source tools give you control and no license fee, but you own the tuning, the updates and the on-call duty. Commercial platforms bundle detection updates, dashboards and support, so model your request volume before you compare quotes.
Open-source tool License (checked November 5, 2026) Control point LLM Guard MIT Input and output scanning NeMo Guardrails Apache 2.0 Runtime guardrails Guardrails AI Apache 2.0 Output validation Presidio MIT Personal-data detection garak Apache 2.0 Vulnerability scanning promptfoo MIT Testing and red teaming PyRIT MIT Red teaming Giskard (giskard-oss) Apache 2.0 Agent testing ModelScan Apache 2.0 Model file scanning Langfuse MIT outside enterprise directories Tracing and evaluation Arize Phoenix Elastic License 2.0 Tracing and evaluation
Licenses change, and ownership changes even faster. Ownership shifted between July 2025 and March 2026.
Palo Alto Networks completed its purchase of Protect AI, Check Point agreed to buy Lakera, and OpenAI announced it is acquiring Promptfoo. So ask each vendor what the roadmap and data terms look like after the deal.
Questions to Ask Before You Sign
Where are prompts and responses processed and stored, and for how long? Does the product block a request, or does it only return a score? Which content does it inspect, from prompts and retrieved documents to tool calls and responses? When the service is down, does it fail open or fail closed? Can logs flow to our SIEM with full context? Which features are open source, and which are paid add-ons? How to Run a Two-Week Proof of Concept
A short, structured test beats a feature checklist, because it runs on your own traffic. Two weeks is enough, provided you prepare the test sets before the trial starts.
Define the route first, so you can draw the application’s path and mark the junctions each candidate tool will guard.Build two test sets , so that one holds attacks, including indirect injection planted in documents, and the other holds normal traffic with personal data removed.Run the whole workflow and send tests through the real path, including retrieval and tool calls, instead of a single prompt box.Measure the results by scoring every candidate on the same cases and versions with the scorecard below.Agree on thresholds before the trial, deciding which false-positive rate and added latency you will accept.Metric How to measure it Attack detection rate Malicious cases caught divided by total malicious cases False-positive rate Benign cases flagged divided by total benign cases Added latency Response time with and without the control on the same workload Failure behavior What happens when the tool times out or errors Audit evidence Share of required events logged with enough context Operating cost Usage fees plus infrastructure and staff time
Record the results internally with versions and dates, so later re-tests compare like with like. A vendor’s benchmark is a claim, whereas your scorecard on your own traffic is evidence.
Gaps No LLM Security Tool Closes
Some risks live in design and judgment rather than in a product. Naming them up front stops a team from paying for a tool to solve a design problem.
Excessive agency comes first, because the OWASP entry on excessive agency traces it to excessive functionality, permissions and autonomy. Those are design choices, so the fix is a permission model, which our agentic AI permission control framework lays out action by action.
Confidential facts with no pattern come next, since data-leak tools miss business secrets that read like ordinary sentences. Classification and retrieval permissions therefore do most of the work here.
Unapproved AI use is harder, because staff who paste data into personal chatbots never touch your gateway, so no filter ever sees them. A policy, an approved-tool list and training matter more, and our guide to generative AI governance covers that program. Tools for shadow AI detection then find what slipped through.
Human judgment remains, since some outputs, such as advice sent to customers, still need a person to approve them. Our post on human-in-the-loop AI agents explains when to require that step.
Tools also cannot grade themselves. An independent AI security assessment checks whether the stack you bought covers the risks you actually have. Without one, rogue AI incidents tend to start with an agent doing exactly what its permissions allowed.
Kanerika Service
AI Governance Consulting Services
Kanerika designs the policies, runtime controls and evidence reporting that keep enterprise LLM and agent deployments secure and auditable.
Explore AI Governance Services How Kanerika Helps Enterprises Put LLM Security Tools to Work
Kanerika does not sell an LLM security product. We are a services partner that designs, builds and governs LLM applications. Then we fit the tools above into each one, based on the route it takes.
Our AI governance consulting covers LLMOps governance. That includes prompt-injection protection with output filtering, hallucination monitoring with audit logging, and model version control with secure access.
Our LLM development team builds runtime guardrails that block off-policy responses. The agentic AI practice gives agents scoped access to ERP, CRM and document systems, with monitoring and approval flows.
Engagements run on Kanerika’s IMPACT framework (Identify, Map, Prove, Analyze, Create, Transform), which ties governance controls to business outcomes. For LLM security, Identify surfaces the gaps at each junction of the route before they become audit findings.
Prove tests the chosen controls on the highest-risk applications first. Transform makes the stack part of everyday AI operations rather than a separate program.
In one engagement, Kanerika built a knowledge base and an LLM-based ticket resolution system for a B2B SaaS client. The team prepared the client’s historical tickets for the model and reached 80% ticket auto-resolution.
Systems like that read customer messages all day, so input screening, output checks and logging belong in the design from the first sprint. Our generative AI services build LLM and RAG applications with security and compliance built in. Kanerika maintains ISO 27001 and ISO 27701:2019 certifications and SOC 2 Type II compliance.
Traps Our Teams Watch For
Two tools guarding the same input while the output path has none. Detectors left in log-only mode long after the pilot ends. Security logs that store full prompts with personal data and no retention limit. Tool versions pinned nowhere, so an update can change behavior overnight. Each trap looks harmless in a design review but expensive in an incident review. Catching them early is cheaper than any license.
Case Study
80% Ticket Auto-Resolution with LLM-Driven AI
Kanerika built an LLM-based ticket resolution system on a client’s knowledge base and historical tickets, reaching 80% ticket auto-resolution and faster query handling.
Read the Case Study → Start With the Map, Then Pick the Tools
LLM security tools work best when each one has a clear job on a clear route. So draw the path from prompt to action and mark where data enters and leaves. Then place input detection, output filtering, a gateway, guardrails, scanners and monitoring where the risk actually sits.
Test the shortlist on your own traffic before you buy. The products will change owners and names again, but the junctions on your route will not, so the map outlasts any single vendor. For the controls behind each junction, our generative AI security guide is also a good next read.
Frequently Asked Questions
What are LLM security tools?
LLM security tools are software and services that detect, block, test or record risky behavior in applications built on large language models. They include prompt-injection detectors, output and data-leak filters, gateways and runtime guardrails, vulnerability scanners, model supply-chain scanners and monitoring platforms. Each guards a different point on the path from a prompt to an answer or action.
What are the main types of LLM security tools?
There are six main types. Input filters catch prompt injection and jailbreaks. Output filters stop personal data and secrets from leaking. Gateways and guardrails apply policy at runtime. Scanners and red-team kits find weaknesses before release. Supply-chain scanners check model files and packages. Monitoring tools record prompts, responses and tool calls for investigation.
Which LLM security tools help prevent prompt injection?
Options include Check Point AI Guardrails (formerly Lakera Guard), Azure AI Content Safety Prompt Shields, the Prompt Attack filter in Amazon Bedrock Guardrails, Google Cloud Model Armor, Meta’s Llama Prompt Guard 2 and the open-source LLM Guard library. They reduce risk rather than remove it, because OWASP says fool-proof prevention may not exist, so pair them with least-privilege access.
What is the difference between an LLM gateway and a guardrail?
A gateway sits between applications and model providers, so it controls keys, routing, rate limits, budgets and logging for every call. A guardrail runs inside one application and can check conversation state, retrieved documents and tool calls. Many enterprises use a gateway for shared policy and guardrails for rules specific to a single high-risk app.
Can LLM security tools prevent sensitive data leaks?
They prevent many leaks of data that has a recognizable shape, such as card numbers, email addresses, credentials and API keys. They are much weaker on confidential business facts written as ordinary sentences. Stopping those needs data classification and permission checks at retrieval time, so the model never sees content the user is not allowed to read.
Are open-source LLM security tools enough for enterprise use?
They can be, if your team will own tuning, updates, monitoring and support. Tools such as LLM Guard, NeMo Guardrails, Presidio, garak, promptfoo and PyRIT are widely used and permissively licensed. Commercial platforms add managed detection updates, dashboards and support. Check each component’s license and maintenance status, because several projects changed owners recently.
How do I pentest or red team my LLM application?
Start with automated scanners such as garak, promptfoo or PyRIT in your development pipeline, using attack sets that include indirect injection hidden in documents. Test the whole route, including retrieval and tool calls, not only the chat box. Then add human red teamers for creative attacks before major releases and after any model or tool change.
How should enterprises evaluate LLM security tools before buying?
Run a two-week proof of concept on your own application. Prepare one set of attacks and one set of normal traffic, send both through the real workflow, and measure detection rate, false positives, added latency, failure behavior, audit logging and cost. Compare every candidate on the same cases and versions before signing anything.
Do LLM security tools secure AI agents?
They secure part of the problem. Filters and guardrails can inspect prompts, tool inputs and outputs, but they cannot decide what an agent should be allowed to do. That needs agent identity, scoped permissions, approvals for risky actions and the ability to revoke access quickly, which are design decisions rather than features of a detector.
How does the OWASP Top 10 for LLM Applications relate to security tools?
The OWASP Top 10 for LLM Applications, 2025 edition, lists ten risks such as prompt injection, sensitive information disclosure, supply chain and excessive agency. Each risk maps to a different control point, so the list helps buyers check coverage. Some risks, like excessive agency and misinformation, depend more on design and human review than on tools.
