LLM security stopped being a research topic the moment models started reading untrusted input and calling tools on your behalf. Prompt injection has held the top spot in the OWASP Top 10 for LLM Applications for two editions running, and the 2025 list added System Prompt Leakage and Vector and Embedding Weaknesses as RAG went mainstream. If your application takes a user prompt, retrieves a document, or hands an agent a tool, you have an attack surface that traditional AppSec tooling does not cover.
The trouble is that "LLM security tool" describes at least three different jobs, and most teams buy or adopt for one and discover too late they needed another. This guide separates the categories, names strong tools in each, and is honest about the trade-offs.
A note on bias: this is published by Safeguard, an enterprise software supply chain and AI security platform. The open-source tools below are genuinely good and we will say so plainly. We will also be clear about where a platform earns its place. Treat this as a shortlist to start from, not gospel.
The three jobs an LLM security tool might do
- Pre-deployment testing and red teaming — probe a model or application before it ships, simulating jailbreaks, prompt injection, and data-leak attacks to find weaknesses.
- Runtime guardrails — sit between users and the model in production, screening inputs and outputs in real time for injection, PII leakage, toxic content, and policy violations.
- Posture, governance, and supply chain — inventory which models and datasets you run, track their provenance, enforce policy across the lifecycle, and govern shadow AI and agentic risk.
Pick by the job you actually have. Most mature programs end up using one tool from each row.
Pre-deployment testing and red teaming
garak — best open-source LLM vulnerability scanner
garak is an exhaustive, free LLM vulnerability scanner that probes a model across many failure modes — prompt injection, jailbreaks, toxic generation, data leakage, and more. It works like a fuzzer for language models and is the natural starting point if you want a broad, automated first pass before launch. Best for: security teams wanting a wide, scriptable scan of a model's failure modes for free.
PyRIT and DeepTeam — best for structured adversarial testing
Microsoft's PyRIT (Python Risk Identification Toolkit) and the open-source DeepTeam framework both help teams run repeatable adversarial campaigns against LLM systems, simulating jailbreaking and injection at scale. DeepTeam maps its attacks to the OWASP LLM Top 10, which makes reporting cleaner. Best for: AppSec and ML teams building a recurring red-teaming practice rather than a one-off check.
Promptfoo — best for evals plus security in CI
Promptfoo (open-source core) is widely used for prompt and model evaluation, and its red-teaming features let you fold adversarial checks into the same harness you use for quality evals. Best for: teams that want security testing to live alongside their existing eval pipeline in CI/CD.
Runtime guardrails
NVIDIA NeMo Guardrails — best programmable open-source guardrails
NeMo Guardrails is an Apache-2.0 toolkit for adding programmable guardrails to LLM applications, using a domain-specific language (Colang) to define dialog flows and safety policies. It is the clearest open-source choice when you want fine-grained, codified control over what a conversational app is allowed to do. Best for: engineering teams that want to express guardrails as code and version them like everything else.
LLM Guard — best free input/output scanning library
LLM Guard is a free, MIT-licensed toolkit with a large set of scanners that block prompt injection, redact PII, and filter toxic or unsafe output on both the input and output side. It installs via pip and slots into a Python service quickly. Best for: teams that want dual-stage scanning without standing up a separate service.
Meta Llama Prompt Guard and LlamaFirewall — best lightweight classifiers
Meta's Prompt Guard classifiers and the open-source LlamaFirewall project give you compact, purpose-built detectors for injection and jailbreak attempts that you can run inline. Best for: adding a fast, focused injection classifier to an existing stack.
Lakera Guard — best commercial runtime API
Lakera Guard is a real-time API that screens traffic between users and an LLM for prompt injection, jailbreaks, and data leakage, with low latency and multilingual coverage. Lakera was acquired by Check Point in late 2025 and folded into its broader portfolio, while Guard remains available as a standalone API. Best for: teams that want a managed, low-latency guardrail without operating their own models.
A noticeable theme of the last two years is consolidation: Cisco acquired Robust Intelligence (2024) into its AI Defense line, Palo Alto Networks acquired Protect AI (2025) into Prisma AIRS, and Check Point acquired Lakera. If you adopt a runtime guardrail from a startup, factor in the real possibility it becomes part of a larger suite.
Posture, governance, and AI supply chain
This is the layer the testing and guardrail tools do not cover, and where most programs are weakest. A scanner tells you a model fails an injection probe; a runtime filter blocks a bad prompt. Neither answers: which models and datasets are actually running across our org, where did they come from, who approved them, and what is our exposure when a new model-level vulnerability lands? That is governance and supply chain territory.
It matters more every quarter because of two trends. Shadow AI — teams wiring up models, agents, and MCP tools without review — means your real attack surface is larger than your inventory. And agentic AI security raises the stakes: an agent that can read untrusted content and call tools turns a successful prompt injection into action, not just a bad answer.
AI security platforms and posture management
Vendors including Cisco AI Defense, Palo Alto Prisma AIRS, and cloud-native posture tools (for example, Wiz's AI security coverage) increasingly fold model discovery, posture, and runtime protection into one place. Best for: organizations standardizing on a single large security suite, especially if they already run that vendor's platform. See Safeguard vs Wiz.
Safeguard — best for AI supply chain, AIBOM, and agentic governance
Safeguard approaches LLM security from the supply chain and governance angle: it generates an AIBOM / ML-BOM so you actually know which models, datasets, and weights are in your stack, attaches provenance and attestation, detects shadow AI, and enforces policy gates across the lifecycle. Its Multi-Agent TAOR Deep Think engine is a model-agnostic verification and orchestration layer that sits above the model rather than betting on any single one. It runs in cloud, on-prem, and air-gapped environments. Best for: enterprises that need to govern the AI entering their software supply chain — not just filter prompts at runtime.
A quick decision shortcut
- "I need to test a model before launch." → garak, PyRIT, or DeepTeam (free).
- "I want red teaming in my eval pipeline." → Promptfoo.
- "I need guardrails as code." → NeMo Guardrails.
- "I want a free input/output scanner." → LLM Guard or a Prompt Guard classifier.
- "I want a managed runtime API." → Lakera Guard.
- "I need an AIBOM, shadow-AI discovery, provenance, and policy gates — possibly air-gapped." → Safeguard.
Frequently asked questions
What is the best LLM security tool in 2026? There is no single best tool, because the category covers three jobs. For pre-deployment red teaming, garak is the strongest free starting point. For runtime guardrails, NeMo Guardrails (open source) and Lakera Guard (managed) lead. For AI supply chain governance — AIBOM, shadow-AI discovery, provenance, and policy enforcement, including air-gapped — Safeguard is built for that job.
How do I defend against prompt injection? There is no single fix. Combine input and output guardrails (a classifier plus a scanner like LLM Guard or NeMo Guardrails), least-privilege tool access for agents so a successful injection cannot do much, red-team testing before launch with garak or DeepTeam, and treat all retrieved or tool-returned content as untrusted. OWASP lists prompt injection as the number-one LLM risk for a reason: defense in depth, not one tool.
What is an AIBOM, and why does LLM security need one? An AIBOM (or ML-BOM) inventories the AI components in your systems — models, datasets, and weights — with their provenance and risk, the way an SBOM does for software. You cannot secure models you do not know you are running, and shadow AI makes that gap real. An AIBOM turns "we think we use these models" into a governed, auditable inventory.
Are free LLM security tools enough? For testing and basic guardrails, often yes — garak, NeMo Guardrails, and LLM Guard are genuinely strong. The gap usually appears in governance: org-wide model and dataset inventory, provenance, shadow-AI discovery, policy enforcement, and agentic-risk controls. That operational layer is where most teams end up needing a platform.
How Safeguard Helps
Once LLM security grows past "scan the model and add a filter," the hard part is knowing what you actually run and governing it. Safeguard generates an AIBOM / ML-BOM across your stack, detects shadow AI, attaches provenance and attestation, and enforces policy gates on the models and agents entering your software supply chain — with a model-agnostic verification layer (Multi-Agent TAOR Deep Think) that sits above any single model. It complements your guardrails and red-teaming tools rather than replacing them, and runs in cloud, on-prem, and air-gapped environments. Reach out and we will map it to your current AI stack.