The 10 Best AI Security Tools

Somebody in your company shipped a feature that sends user text to a language model, and that model can call tools or answer from an internal system. You own the risk, and your existing controls do not see it. SAST reads the glue code and finds nothing, the WAF sees a well formed POST body, and the attack is a paragraph of English in a support ticket the model summarizes.

The labels in this category hide five different jobs. Testing tools generate adversarial input and report what broke. Guardrails classify or block inside the request path. Artifact scanners inspect model files and the supply chain behind them. Agent tooling governs what a model may do once it can call things. Governance platforms answer which AI is running where. A tool strong at one is usually thin at the rest.

This article covers ten tools spanning those five jobs. The category is young: detection quality varies, benchmarks are vendor produced, and several products that present as established are new.

What actually matters when choosing #

The obvious criterion is detection quality, and it is the wrong place to start: you cannot verify it from a data sheet, and every number you see came from the vendor's test set.

Start with placement. A library you import sees what your application sees. A hosted API adds a network hop and a data sharing decision. A gateway sees only what crosses it, so server side code calling a model SDK directly is invisible. That choice is the hardest to reverse.

Then ask what happens to a finding. Red teaming tools produce long lists of jailbreaks, most with no owner and no fix beyond editing a system prompt, which then regresses silently. Name who triages the output, or the tool manufactures work nobody does.

Then latency and failure mode: guardrails that call another model add time to every request, and you decide now whether the system fails open or closed. Last, a tool that probes a bare endpoint says little about a retrieval application where injection arrives through indexed documents.

How these were selected #

These are scenario picks, not a ranking. Each entry names a distinct situation where it wins, and no two share that claim. The order groups related tools, testing first, then runtime, supply chain, agents and governance, not quality. No vendor funded this and nobody paid for placement.

Promptfoo #

Best for: treating adversarial testing as a test suite that runs in CI

Promptfoo is configuration driven. You declare providers, prompts and assertions in a config file and it runs them as a matrix, so one harness covers ordinary evaluation and red teaming. The red team side generates adversarial cases by plugin, covering injection, data disclosure and excessive agency, then grades responses. Being a config file and a CLI, it drops into a pipeline like any other test runner.

That suits teams who want this to be an engineering discipline, not an annual exercise: a baseline, a diff when a prompt changes, a failing build on regression. It targets applications and agents, not only raw endpoints.

The caveat is grading. Many assertions are model graded, so a second model judges the first, inconsistently at the margins and slowly. Expect real tuning before the suite is stable enough to gate a build.

Garak #

Best for: a fast first read on what a model endpoint will do, before you invest in anything

Garak is a scanner in the traditional sense: a library of probes fired at a generator, each paired with detectors that decide whether a response is a failure. Probes cover jailbreaks, injection, encoding tricks, toxicity, training data regurgitation and package hallucination. It speaks to hosted APIs, Hugging Face models, local runtimes and generic REST endpoints.

Its value is that it takes an afternoon. Run it against the model your team is about to build on: seeing the output yourself is more persuasive internally than a vendor deck.

Detectors are heuristic, so false positives and quiet misses are common and a red report needs a human read. And Garak tests the model, not your application: it will not exercise your retrieval path, tool calls or session state, which is where deployed exposure lives.

PyRIT #

Best for: a dedicated red team building multi turn attack campaigns with custom logic

PyRIT is a framework rather than a scanner. You assemble a campaign from parts: a target, converters that mutate prompts into obfuscated or encoded variants, orchestrators that drive single or multi turn conversations, and scorers that judge whether an objective was met. Runs land in a memory store, so they are reproducible.

Use it when the attacks must be yours. If success in your application means extracting a specific record type or driving a specific tool call, no generic probe library expresses that. Multi turn orchestration also reaches failures single shot testing misses.

PyRIT assumes skill and time. It is a framework with concepts to learn, it emits artifacts rather than a report, and it does not tell you what to test. Without someone whose job is AI red teaming, teams run the samples once and never return.

LLM Guard #

Best for: input and output scanning where nothing may leave your environment

LLM Guard is a library of composable scanners wrapped around your model calls. Input scanners handle injection detection, secret and PII anonymization, token limits and banned topics. Output scanners check for sensitive data leakage, refusals, relevance and de-anonymization on the way back. Everything runs locally, mostly on small classifier models you host.

Local execution is the whole argument. For regulated workloads, air gapped deployments, or anywhere sending prompts to a third party classifier is itself the problem, it removes the data sharing question.

The trade-off is that you operate it. Scanners load models into memory, so plan for GPU or CPU capacity, cold starts and latency measured per scanner. Injection detection quality sits behind the better commercial classifiers, particularly against obfuscated and multilingual attacks. Treat it as defense in depth, not a boundary.

Lakera Guard #

Best for: low latency injection and jailbreak detection in front of a user facing product

Lakera Guard is a detection API. You submit a prompt, a retrieved chunk or a model response and it returns classifications for injection and jailbreak attempts, sensitive data and content policy categories. Being fast enough to sit synchronously in a production path is the design goal, and what separates it from wrapping a general purpose model as a judge.

It suits product teams who need something defensible in front of a customer facing assistant without staffing detection engineering. Integration is a call, policy is configuration.

Two caveats. Classifiers are probabilistic, so you will tune thresholds against real traffic, and at scale even a low false positive rate blocks legitimate users visibly. And routing prompts through a third party is a conversation with legal. A self-hosted option changes that calculation and the operational one.

NVIDIA NeMo Guardrails #

Best for: constraining what a conversation is allowed to do, not just filtering the text

NeMo Guardrails is the one tool here that models the conversation itself. In a rail definition language you describe canonical user intents, permitted flows and bot responses, and the toolkit enforces them as input, dialog, retrieval, output and execution rails. That last category matters for agents: you can say which tool may be invoked in which conversational state rather than only inspecting strings.

It is the right answer when your application has a defined scope and staying inside it is the security requirement: an assistant that must never touch an account action outside a verified flow.

The caveat is complexity. You are maintaining a behavioral specification in a language your team does not know, the rails add model calls and latency, and coverage is only as good as the intents you enumerated.

Protect AI Guardian #

Best for: stopping malicious model artifacts at the point they enter your organization

Everything above assumes the model itself is benign. Serialized model files are not inert data: pickle based formats execute code on load, and other formats carry their own execution paths. Guardian inspects artifacts for those payloads and unsafe constructs and enforces policy on what may be pulled, acting as a control point between public hubs and your environment, not a report you read afterward.

Your data science teams pull models from public hubs, and this is the closest analogue to the artifact scanning you run for containers and packages. It also has an obvious owner and a clear block action.

The caveat is scope. It addresses malicious code in artifacts, not a model backdoored or poisoned in its weights, which static inspection does not reliably detect. And someone pulling weights in a notebook bypasses it without noticing.

Agentic Radar #

Best for: finding out what your agents can actually reach before you argue about controls

Agentic Radar statically analyzes an agentic codebase and produces a map: which agents exist, what tools each can call, which of those reach external systems, and where MCP servers are wired in. It understands several common orchestration frameworks and emits a report you can take to an architecture review.

It belongs in a security roundup because the first problem with agents is not detection, it is inventory. Most teams cannot answer what their agent is permitted to do, and excessive tool permission is what turns a prompt injection into an incident.

The caveats are the usual static analysis ones. Dynamically registered tools and runtime configuration are invisible, framework coverage is limited to what is supported, and it describes structure rather than proving exploitability. It is an emerging project, so check support for your stack.

WitnessAI #

Best for: seeing and governing employee and application AI use across the organization

WitnessAI observes AI traffic across an environment, identifies which services are in use and by whom, and applies policy to prompts and responses in flight, with identity attached so rules differ by user or group. It answers the question a CISO asks first: what AI is running here, used by which teams, carrying what data.

That discovery job is real work and none of the developer tools above do it. Unsanctioned AI use is a common path for sensitive data to leave a company, and acting on it means seeing traffic, not sitting inside one application.

The caveats matter. Visibility depends entirely on routing traffic through the enforcement point, and gaps appear with unmanaged devices, native applications and server side agents calling providers directly. Enforcement on natural language is approximate, so expect tuning and expect people to route around a control that blocks too much.

Holistic AI #

Best for: producing defensible AI risk documentation for regulators and auditors

Holistic AI is a governance platform rather than a scanner. It maintains an inventory of AI systems, assesses each against regulatory frameworks and risk taxonomies, and supports bias and fairness auditing of automated decision systems. The output is evidence: a register, an assessment trail, and artifacts legal and compliance can hand to somebody external.

For many organizations the binding constraint on AI deployment is not jailbreaks, it is showing a regulator or enterprise customer that the system was assessed before it shipped. Nothing else in this article produces that.

The caveat is that it is not a technical control. It does not test your model, inspect prompts or block anything. It is only as accurate as the assessments people complete inside it, so it needs a program owner and cooperation from teams who would rather be building.

How to choose #

If you shipped one LLM feature and have no AI security tooling, start with Garak for evidence, then Promptfoo to make testing repeatable.

If prompts cannot leave your environment, start with LLM Guard.

If a customer facing assistant is already live, start with Lakera Guard.

If your application must stay inside a defined scope and can call tools, start with NVIDIA NeMo Guardrails.

If you have a dedicated red team and generic probes miss what matters, start with PyRIT.

If data scientists pull models from public hubs, start with Protect AI Guardian.

If you must approve an agent and cannot list its tools, start with Agentic Radar.

If the question is unsanctioned AI use, start with WitnessAI. If a regulator is the forcing function, start with Holistic AI.

What these tools will not do for you #

None of them fixes prompt injection, because prompt injection is not fixed. Instruction and data share a channel in a language model, and every control here is a probabilistic filter over that fact. Classifiers are evaded with encoding, translation, indirection through retrieved content, and attacks published after training.

So architecture carries more weight than tooling. If an injected instruction can reach a tool that moves money, deletes records or reads another tenant's data, no guardrail should be your last line. Scope the model's credentials to what a compromised session may safely do, require human confirmation for consequential actions, keep authorization outside the model, and treat retrieved content as untrusted input.

Two further gaps. Almost nothing here covers the data layer: what went into fine tuning, who can read the vector store, whether embeddings leak the documents behind them. And detection claims are largely unverified, so insist on a trial against your own traffic.

This tooling needs an owner. Red team output with no triage path becomes a report nobody reads, and a guardrail nobody tunes becomes the switch someone flips during an incident.

Frequently asked questions #

Do I need AI security tools if I already run SAST, SCA and DAST?

Yes. Those inspect code and HTTP behavior, and the surface here is model behavior: your SAST engine reads the call that sends a prompt and has no opinion about what the prompt does. Keep them: the application around the model is still ordinary software with ordinary flaws.

Open source or commercial for red teaming?

It depends on whether you have an owner. Open source frameworks give you control and no data sharing question, but somebody must maintain attack coverage as techniques change. Commercial platforms bring a maintained attack library and reporting, in exchange for a vendor in your testing path.

Should guardrails live in the application or at a gateway?

A gateway gives one enforcement point and catches traffic you did not know about, but it sees only what crosses it and lacks application context. In-application guardrails understand the request but must be implemented by every team. The common mistake is assuming a gateway covers server side agents.

How often should we re-run adversarial testing?

Any change to a system prompt, a model, a tool definition or a retrieval source changes the attack surface, so an automated suite belongs on every deploy. Deeper manual red teaming is worth doing after any change that gives the model a new capability rather than new content.