If you search for llm security testing tools as a buyer, you land on a category that is quietly two categories wearing one name, and the tools in each do almost opposite jobs. One group uses large language models to do security testing for you: scanners that reason about a target, copilots that sit next to a human tester, and autonomous agents that try to find and prove real bugs. The other group tests the security of LLM applications themselves: red teaming and guardrail tools that throw prompt injection, jailbreaks, and data leakage attempts at a model to see what it gives up. This guide maps both halves so you can tell which one a vendor is actually selling, name the real tools in each, line them up against the frameworks that govern them, and walk away with a short checklist for evaluating any of them without falling for a demo.
This is a cluster guide under our broader pillar on AI security testing. If you want the wide angle on how machine learning is reshaping offensive and defensive testing, start there. This page stays narrow on purpose: the tools, what they are, and how to judge them.
Why llm security testing tools means two different things
The phrase is genuinely ambiguous, and the ambiguity is not pedantic. A team shopping for a way to find vulnerabilities faster and a team shopping for a way to keep their chatbot from leaking customer records will both type the same words into a search bar. They need different products. Before you compare anything, you have to decide which problem you are solving.
Meaning (a) is LLM driven security testing: the tool is the tester, and a language model is the engine inside it. The thing under test is ordinary software, a web app, an API, a network. The model reads responses, forms hypotheses, and decides what to try next. Here the LLM is offense.
Meaning (b) is security testing of LLM applications: the tool is the attacker and the thing under test is itself a model or an application built around one. The goal is to break the model’s guardrails, extract its system prompt, make it follow an injected instruction, or coax out training data. Here the LLM is the target.
Some platforms blur the line, using a model to attack another model, but the distinction still tells you what a tool is for. The rest of this guide takes each meaning in turn, names verifiable tools, and stays at the category level wherever a specific claim cannot be confirmed.
Meaning (a): tools that use LLMs to perform security testing
This side of the market is moving fastest and is also the easiest to oversell. It splits cleanly into three categories that differ by how much autonomy the model holds and how much a human stays in the loop.
AI augmented classic scanners and SAST and DAST
The most incremental category is the established scanner with a language model bolted on. Static application security testing (SAST) reads source code for dangerous patterns. Dynamic application security testing (DAST) probes a running application from the outside. Both have lived for years with a well known weakness: noise. A traditional SAST tool flags a pattern that looks like a SQL injection but cannot tell whether the tainted input ever reaches the sink under real conditions, so it reports a finding a human then has to triage.
The language model addition tries to cut that triage cost. It reads the flagged code path, the surrounding context, and sometimes the data flow, then it explains whether the finding looks real and proposes a fix. The honest framing is that this is assistance on top of the same underlying detection engine, not a new way of finding bugs. It can reduce false positive review time and it can also introduce a new failure mode, a confident model explanation that is simply wrong. If you want the ground truth on how these detection approaches differ before judging an AI layer on top of them, our explainer on SAST vs DAST vs IAST lays out what each one can and cannot see.
LLM assisted manual testing copilots
The second category keeps a human firmly in the driver’s seat and uses the model as an advisor. A copilot suggests the next step, interprets tool output, drafts a payload, or explains an unfamiliar response while the tester decides what to actually run. The clearest public example of this pattern from research is PentestGPT, an open source project and academic study presented at USENIX Security 2024. PentestGPT structures a model’s reasoning into a tester like workflow and was evaluated on a benchmark of penetration testing sub tasks. The research itself is candid about the limits: the authors found that language models handle discrete operations such as interpreting a single tool’s output reasonably well but struggle to hold a coherent multi step strategy across a long engagement, losing the thread as context grows. That is the honest state of the copilot category. It is a force multiplier for a skilled human, not a replacement for one.
The value of a copilot is bounded by the person using it. In expert hands it speeds up the boring parts and surfaces ideas. In inexperienced hands it can produce confident nonsense that the user is not equipped to catch. Treat copilots as the human in the loop category, because the human is the safeguard.
Autonomous pentest agents
The third category is the one drawing the most attention and the most hype: agents that run an end to end test with little or no human steering. They map an application, pick targets, attempt exploits, observe results, and decide their next move in a loop. The most prominent commercial example is XBOW, which describes itself as an autonomous offensive security platform that performs web application penetration tests and surfaces a finding only after it has confirmed exploitability through a controlled challenge. That last property, confirming a bug by actually exploiting it in a non destructive way rather than just flagging a pattern, is the meaningful design choice in this category and the one worth probing in any agent that claims it.
The promise of autonomous agents is real and the caveats are equally real. An agent that can prove a finding saves enormous triage effort. An agent that operates without supervision needs hard scope and safety controls, because the same autonomy that lets it chain an exploit lets it wander outside the targets you authorized. The agent attack surface is itself a security topic worth understanding before you point one at production, which we cover separately in our piece on the AI agent attack surface.
An autonomous tool that flags a vulnerability is making a claim. An autonomous tool that exploits it is offering proof. The gap between those two is the entire question of whether a finding is worth your time.
Meaning (b): tools that test the security of LLM applications
Now flip the polarity. Here the application under test is the model, or a product built on top of one, and the tools are designed to break it. This category exists because LLM applications fail in ways traditional scanners were never built to see: a prompt injection buried in a retrieved document, a jailbreak that talks the model out of its own rules, a system prompt that leaks under pressure, or sensitive data surfacing in a completion. These are the failure modes a red teaming tool is built to provoke on purpose.
NVIDIA garak
garak is an open source LLM vulnerability scanner from NVIDIA. The name stands for Generative AI Red teaming and Assessment Kit, and the tool works much like a classic vulnerability scanner pointed at a model instead of a network. It ships with a library of probes that try to make a model fail in known ways, then detectors that judge whether the attempt succeeded. You point it at a model, choose probes, and it runs them and reports what got through. It is freely available and a sensible starting point for anyone who wants a repeatable, automated first pass over a model’s weaknesses. The repository lives at github.com/NVIDIA/garak.
Microsoft PyRIT
PyRIT, the Python Risk Identification Tool for generative AI, is an open source framework from Microsoft built to help security professionals probe generative AI systems. Where a scanner runs a fixed battery, PyRIT is a framework you compose: it is designed to automate parts of the red teaming workflow and can adapt its approach across a multi turn exchange rather than firing a single static prompt. Microsoft has described it as something its own AI red team uses in practice. Treat it as a toolkit for building red teaming campaigns rather than a one click scanner. The repository is at github.com/microsoft/PyRIT.
Promptfoo
Promptfoo is an open source tool that started life as an LLM evaluation harness and grew red teaming and vulnerability scanning features. The evaluation heritage matters: it is built around declarative test configurations you can run locally and wire into a continuous integration pipeline, which makes it a natural fit for teams that want LLM security checks to run on every change rather than as a one off audit. Its red team mode generates adversarial test cases aimed at the kinds of weaknesses the OWASP LLM list catalogs. The project is at github.com/promptfoo/promptfoo.
Giskard
Giskard is an open source Python library for testing and evaluating machine learning models that has extended into LLM and agent testing. Its scanning approach generates test suites aimed at issues such as prompt injection, harmful content, and information disclosure, and it positions itself across both quality and security testing rather than security alone. Like the others here, treat the open source library as the verifiable core and read the current documentation for the exact probe coverage, since these projects iterate quickly. The repository is at github.com/Giskard-AI/giskard.
Two notes on this whole category. First, several of these tools overlap in what they cover, so the question is rarely which one but which combination, and how it fits your workflow. Second, an evaluation harness and a security red teaming tool share a lot of plumbing, which is why so many of these projects do both. The line between testing whether a model is good and testing whether a model is safe is thinner than the marketing suggests.
How llm security testing tools map to the real frameworks
A tool is only as useful as the threat model it covers, and the frameworks are how you check coverage without taking a vendor’s word for it. Each side of this landscape has its own reference points.
Frameworks for the LLM application side
The anchor for testing LLM applications is the OWASP Top 10 for Large Language Model Applications. It enumerates the dominant risk classes for systems built on language models, including prompt injection, sensitive information disclosure, insecure output handling, and supply chain risks, and it is the closest thing the field has to a shared vocabulary. When a red teaming tool says it tests for OWASP LLM risks, this is the list it means, and you should ask which entries it actually exercises rather than accepting the logo. If you want a baseline before you shop, our free OWASP LLM Top 10 self assessment scorecard walks your own application through each entry so you know which risks you most need a tool to cover.
The second reference is MITRE ATLAS, the Adversarial Threat Landscape for Artificial Intelligence Systems. Modeled on the familiar MITRE ATT&CK structure, ATLAS catalogs tactics and techniques that adversaries use against AI and machine learning systems, grounded in real world case studies. Where the OWASP list is a checklist of risk classes, ATLAS is a map of adversary behavior, which makes the two complementary. A serious LLM testing program uses OWASP to scope what to test and ATLAS to think like the attacker.
Frameworks for the web testing side
For meaning (a), where the model is doing the testing of conventional software, the governing reference is the OWASP Web Security Testing Guide, or WSTG. It is the long standing methodology for web application security testing, and it is the right yardstick for any AI driven scanner or autonomous agent that claims to test web applications. If a tool uses a language model to do web testing, the relevant question is how much of the WSTG methodology it actually covers, not how clever the model sounds. The framework existed before the AI layer and it still defines the job.
The mapping is the honest way to compare tools across vendors. A tool that names the specific OWASP LLM entries or ATLAS techniques it covers is giving you something checkable. A tool that gestures at being comprehensive without mapping to anything is asking for trust it has not earned.
How to evaluate an llm security testing tool
Whichever meaning you are buying, the same small set of questions separates a useful tool from an expensive demo. None of them require you to trust the vendor’s framing.
Does it prove findings or just flag them
This is the single most important question, and it applies to both halves of the landscape. A tool that flags a possible vulnerability hands you a hypothesis you still have to verify. A tool that proves the finding, by exploiting it in a controlled way or by showing the exact adversarial input that broke a guardrail, hands you something actionable. The cost of the difference is false positive triage, which is where security teams quietly lose most of their time. Ask for the evidence a finding ships with, and weigh a tool that produces fewer, proven findings over one that produces a flood of maybes.
Coverage of vulnerability classes
Breadth is easy to claim and easy to check against a framework. For the LLM application side, ask which OWASP LLM Top 10 entries and which ATLAS techniques the tool actually exercises. For the web testing side, ask which parts of the WSTG it covers. A precise answer is a good sign. A tool that cannot map its coverage to any framework is telling you something.
Autonomy versus human in the loop
Decide how much independence you want before you shop, because it changes which category you are in. A copilot expects an expert beside it and is only as good as that person. An autonomous agent runs alone and must be judged on whether it can be trusted to stay in scope. Neither is better in the abstract. The wrong fit is buying autonomy you cannot supervise or buying a copilot when you needed scale.
Scope and safety control
Any tool that takes offensive action, especially an autonomous one, must give you hard control over what it touches. Look for explicit scope boundaries, the ability to stop a run, and non destructive testing modes. An agent that can chain an exploit is an agent that can cause damage if it wanders, so the controls around it are not a nice to have, they are the product.
Reproducibility
A finding you cannot reproduce is hard to fix and harder to verify as fixed. Favor tools that record exactly what they did, the inputs they used, and the path they took, so a result can be replayed. This matters doubly for LLM application testing, where model behavior can vary between runs, and a one time jailbreak that cannot be reproduced is difficult to prove or patch.
Can the tool be turned against you
This question is unique to the AI era and easy to forget. A tool that uses a language model to read untrusted content, a scanner ingesting a target’s responses, an agent reading a page, a copilot summarizing output, is itself exposed to prompt injection. Hostile text in the target can try to hijack the tool’s own model and steer its behavior. Ask how a tool isolates the untrusted content it reads from the instructions it follows. A testing tool that can be talked into misbehaving by its target is a liability, not an asset.
A caveat worth keeping
This space moves fast, and capabilities are easy to overstate. The tools named here are real and verifiable as of this writing, but specific features, coverage, and even ownership change quickly, so confirm the current state from each project’s own documentation rather than from any guide, including this one. Be especially wary of capability claims that lean on the mystique of a particular model rather than on reproducible evidence. The right posture is the one this whole field rewards: ask for proof, map claims to frameworks, and trust results you can reproduce over demos you cannot. A claim about an AI security tool deserves exactly the scrutiny you would apply to any other security claim.
For the wider context on how AI is changing both offense and defense, see our broader guide on AI in security testing. On the building side, this category map reflects how we think about evidence backed testing at UnboundCompute, where the emphasis is on findings a tool can prove rather than findings it can only flag; you can read more on our about page. Whichever half of this landscape you are shopping in, the discipline is the same. Decide which problem you are solving, name the tools honestly, hold them to a framework, and believe the ones that show their work.
Frequently asked questions
What are llm security testing tools?
The phrase covers two distinct categories. The first is tools that use large language models to perform security testing of ordinary software, which includes AI augmented scanners, LLM assisted manual testing copilots, and autonomous pentest agents. The second is tools that test the security of LLM applications themselves, meaning red teaming and guardrail tools that probe a model for prompt injection, jailbreaks, and data leakage. A buyer should decide which problem they are solving first, because the products are different. The risk classes on the application side are catalogued in the OWASP Top 10 for Large Language Model Applications.
What tools red team LLM applications?
Several open source projects are the verifiable anchors in this category. NVIDIA garak is an LLM vulnerability scanner that runs a library of probes against a model and judges what gets through. Microsoft PyRIT is a framework for composing red teaming campaigns that can adapt across a multi turn exchange. Promptfoo started as an evaluation harness and added red teaming and vulnerability scanning. Giskard is a testing library that extends into LLM and agent security. Read each project’s current documentation for exact coverage, since they iterate quickly. The garak repository is at github.com/NVIDIA/garak.
Are autonomous AI pentest tools real?
Yes, though capabilities are easy to overstate. XBOW describes itself as an autonomous offensive security platform that performs web application penetration tests and surfaces a finding only after confirming exploitability through a controlled, non destructive challenge. On the research side, PentestGPT is an open source project and academic study that structures a model’s reasoning into a tester like workflow; its own authors found language models handle discrete operations well but struggle to hold a coherent multi step strategy over a long engagement. The PentestGPT research was presented at USENIX Security 2024 and is documented at USENIX.
How do you evaluate an llm security testing tool?
Ask whether it proves findings with evidence or merely flags them, because false positive triage is where teams lose the most time. Check its coverage by asking which framework entries it actually exercises rather than accepting a broad claim. Decide whether you want an autonomous tool or a human in the loop copilot, and confirm there are hard scope and safety controls plus reproducible results. Finally, ask whether the tool itself can be turned against you through prompt injection of the untrusted content it reads. For the adversary behavior these tools should map to, see MITRE ATLAS.
Looking for a tool that proves what it finds
The hardest part of this whole category is the one this guide keeps returning to: separating a real, proven finding from a confident guess. UnboundCompute is an autonomous security researcher built around that exact constraint, reporting only the vulnerabilities it can confirm with evidence and holding back the ones it cannot. If that is what you want from your testing, you can request access.