9 Best AI Red Teaming Tools for LLMs in 2026

1. TestMu AI (Formerly LambdaTest)

Almost every other tool in this list attacks a model endpoint. TestMu AI attacks the agent your customer actually reaches, including the voice and phone surfaces where an attack arrives as speech rather than text. If your production risk is a caller talking a support agent into a refund, that difference is the whole ballgame.

Its Agent Testing platform exposes a dedicated red team command through the testmu-a2a CLI, covering nine attack categories: prompt injection, jailbreak, data exfiltration, PII leakage, harmful content, overreliance, hijacking, policy violation, and technical injection. Under the hood a Security Researcher agent probes for data exfiltration and prompt injection while a Data Privacy Guardian checks for PII exposure, as part of a wider set of 15+ specialised testing agents.

testmu-a2a redteam \
  --agent https://my-bot.com/api/chat \
  --categories prompt-injection,jailbreak,pii-leakage

Key features

• Nine attack categories run against chat, voice, and phone agents through the live interface.
• Adversarial scenarios generated automatically from an uploaded PRD, policy document, or knowledge base.
• Security findings scored alongside hallucination, bias, completeness, and context awareness.
• Green, Yellow, or Red production-readiness verdict instead of a raw findings dump.
• CLI returns exit code 0 on pass and 1 on failure, so a pipeline can block the merge.

Pros:

• Only tool here that red teams voice and phone agents through the live interface, including spoken attacks
• Nine attack categories run alongside the nine quality metrics in a single pass
• Green, Yellow, or Red verdict a release manager can act on without reading transcripts
• Scenarios generated automatically from a PRD, policy document, or knowledge base
• CLI exit codes let a pipeline block the merge

Cons:

• Open-source scanners carry a larger and more exotic probe library
• Commercial platform rather than a self-inspectable framework
• Most of the platform goes unused if you ship a raw model API rather than an agent

Pricing. Usage-based with a free tier starting at $0.01 per credit, scaling to enterprise contracts.

Verdict. The pick when the thing you are defending is a deployed conversational agent rather than a model endpoint.

2. Promptfoo

Promptfoo started as a prompt evaluation harness and grew a red team module on top of it, which is why it feels like a test runner rather than a security scanner. You describe your application and attack objectives in a YAML file, and it generates adversarial cases against those objectives instead of replaying a fixed payload list.

At 23,809 GitHub stars under an MIT licence, with commits landing daily, it is the most actively maintained project in this list. The trade-off is that its security depth is younger than its evaluation depth, so dedicated scanners still carry more exotic probes.

Key features

• Generates adversarial test cases from declared attack objectives rather than a static payload file.
• YAML configuration and a CLI that drops into GitHub Actions or GitLab CI without a wrapper script.
• Covers prompt injection, jailbreaks, and harmful content generation in the same run as quality evals.
• Local web viewer for reviewing failures side by side across model versions.

Pros:

• Easiest path from zero to a red team run inside CI
• One YAML config carries both quality evals and security tests
• Most actively maintained project in this list, with commits landing daily
• Local web viewer for comparing failures side by side across model versions

Cons:

• Security probe library is less specialised than a dedicated scanner
• Large adversarial suites get expensive because every case is a model call
• Security depth is younger than its evaluation depth

Pricing. Open source under MIT with an optional commercial enterprise offering. Check the vendor's site for current figures.

Verdict. The default pick when you want red teaming to run on every pull request rather than once a quarter.

3. Garak

Garak is NVIDIA's LLM vulnerability scanner, and it behaves the way a network scanner does: point it at a model, pick your probes, and get a report of what got through. Its own description is simply "the LLM vulnerability scanner", which is an accurate summary of the design philosophy.

It carries 8,643 GitHub stars under Apache 2.0 with active commits, and the probe library is the broadest of the open-source options. Because it is scanner-shaped, it is strongest against a model endpoint and weaker at reproducing a real multi-turn user session.

Key features

• Large library of probe modules spanning jailbreaks, prompt injection, data leakage, and toxicity.
• Command-line first, so a full sweep is one invocation with no code to write.
• Pluggable generators for Hugging Face models, hosted APIs, and local endpoints.
• Detector modules score each probe independently, so results explain which attack class failed.

Pros:

• Widest out-of-the-box attack coverage with no configuration required
• Backed by NVIDIA with active ongoing development
• A full sweep is a single command with no code to write
• Detector modules explain which attack class actually failed

Cons:

• Runs are slow because probe counts are high
• Output is verbose enough to need a triage pass
• Targets model endpoints rather than deployed applications

Pricing. Free and open source under Apache 2.0.

Verdict. Use it for the broad first sweep that tells you which attack classes deserve deeper work.

4. PyRIT

PyRIT is Microsoft's Python Risk Identification Toolkit for generative AI, built by a team that red teams models at production scale. Its distinguishing idea is orchestration: rather than firing one prompt, it uses an attacker model to refine prompts against the target across many turns.

That makes it the right tool for the attacks that actually work on shipped chat agents, where a single hostile message fails but fifteen polite ones gradually move the model off its guardrails. It is MIT licensed, and it expects you to write Python rather than fill in a config file.

Key features

• Orchestrators that automate multi-turn attack chains against a target model.
• Attacker-model strategies that iteratively rewrite prompts based on the target's replies.
• Pluggable targets, converters, and scorers so each layer can be swapped independently.
• Memory of prior attempts, which is what allows an attack to build across a session.

Pros:

• Most capable open-source option for automated multi-turn attacks
• Attacker-model strategies rewrite prompts based on the target responses
• Pluggable targets, converters, and scorers can each be swapped independently
• Memory across attempts lets an attack build gradually over a session

Cons:

• Genuine learning curve before the first useful result
• Python fluency is a hard requirement, not a nice to have
• No turnkey report for a non-technical stakeholder

Pricing. Free and open source under MIT.

Verdict. Pick it when you have a security engineer who will write attack code, not a QA team who needs a scan button.

5. DeepTeam

DeepTeam is Confident AI's red teaming framework, and its main advantage is inheritance: it sits on the same metric engine as DeepEval, so teams already running DeepEval evaluations get adversarial testing without adopting a second mental model.

It holds 2,314 GitHub stars under Apache 2.0. The project is younger than Garak or Promptfoo, so the attack library is smaller, but the ergonomics are good if your evaluation stack is already Python and pytest-shaped.

Key features

• Vulnerability and attack abstractions that map onto the OWASP Top 10 risk categories for LLM applications.
• Shares metric definitions with DeepEval, so quality and safety scores stay comparable.
• Simulates single-turn and multi-turn attacks from Python test code.
• Targets agents as well as raw models, including tool-calling behaviour.

Pros:

• Almost zero adoption cost for teams already running DeepEval
• Vulnerability abstractions map cleanly onto recognised LLM risk categories
• Shares metric definitions with DeepEval so quality and safety scores stay comparable
• Targets agents as well as raw models, including tool-calling behaviour

Cons:

• Narrower probe library than Garak or Promptfoo
• Smaller ecosystem and community than the projects above it
• Harder to justify as a standalone choice if DeepEval is not already in use

Pricing. Free and open source under Apache 2.0, with an optional hosted platform. Check the vendor's site for current figures.

Verdict. The obvious choice if DeepEval is already in your test suite, and a harder sell if it is not.

6. Giskard

Giskard came from classical ML testing and carried that discipline into LLMs, which shows in how it treats a scan: it detects issues, then generates a reusable test suite from them so the same failure is caught again next release.

The open-source core sits at 5,726 GitHub stars under Apache 2.0 with recent activity. Its RAG focus is the differentiator here, since retrieval pipelines fail in ways a pure prompt attack never surfaces, such as poisoned context or retrieval of documents the user should not see.

Key features

• Automatic scan that surfaces hallucination, harmful content, and prompt injection issues.
• Converts detected issues into a regression suite rather than a one-off report.
• RAG-specific diagnostics covering retrieval and generation separately.
• Covers tabular and NLP models too, which suits mixed ML and LLM estates.

Pros:

• Strongest RAG coverage in the open-source group
• Converts detected issues into a reusable regression suite rather than a one-off report
• Diagnoses retrieval and generation separately so you fix the right layer
• Covers tabular and NLP models too, which suits mixed ML estates

Cons:

• Breadth across classical ML makes the LLM surface feel less specialised
• Richer collaboration and reporting features sit in the commercial tier

Pricing. Apache 2.0 open-source core with a paid hub for teams. Check the vendor's site for current figures.

Verdict. The one to reach for when your risk is mostly in the retrieval layer rather than the prompt.

7. Mindgard

Mindgard is a commercial AI security platform with roots in academic offensive-AI research, positioned around continuous automated red teaming rather than a scheduled engagement. It targets the reality that a hosted model can change under you between assessments.

The value here is operational rather than technical novelty: attack libraries are maintained for you, and results arrive as tracked findings with remediation guidance instead of raw scanner output that a security engineer has to interpret.

Key features

• Continuous automated red teaming against deployed models and AI applications.
• Vendor-maintained attack library, so coverage tracks new techniques without your effort.
• Findings mapped to recognised AI risk taxonomies for audit and reporting.
• Integrations aimed at running inside an existing security workflow.

Pros:

• Removes the maintenance burden of keeping attack coverage current
• Vendor-maintained attack library tracks new techniques without your effort
• Findings map to recognised AI risk taxonomies for audit and reporting
• Output arrives in a form a security team can action directly

Cons:

• Less transparency than an open framework you can read and modify
• Commercial licensing puts it out of reach for small teams still evaluating

Pricing. Commercial. Check the vendor's site for current figures.

Verdict. Sensible when you need continuous assurance and do not have an in-house offensive AI capability.

8. Lakera

Lakera approaches the problem from both ends, pairing adversarial testing with a runtime guardrail layer that inspects traffic in production. That combination matters because red teaming tells you a jailbreak works, but something still has to stop it at request time.

The company is also known for Gandalf, a public prompt-injection game that has collected an unusually large corpus of real attack attempts, which feeds the threat intelligence behind the product.

Key features

• Automated red teaming for GenAI applications ahead of release.
• Runtime detection of prompt injection and jailbreak attempts against live traffic.
• Threat intelligence drawn from a large corpus of crowd-sourced attack attempts.
• API-first integration designed to sit in front of an existing application.

Pros:

• Closes the loop from finding a jailbreak to actually blocking it at runtime
• Threat intelligence drawn from a large corpus of real attack attempts
• API-first integration that sits in front of an existing application

Cons:

• The runtime layer adds latency to every request
• The testing half is less deep than a dedicated offensive toolkit

Pricing. Commercial, with a free tier for the guardrail API. Check the vendor's site for current figures.

Verdict. Best fit when you want detection in production, not just a report before launch.

9. HiddenLayer

HiddenLayer works one layer below everything else in this list. Its focus is the model artefact and the pipeline around it, covering risks such as malicious model files, tampering, and theft rather than conversational jailbreaks alone.

That maps to the supply chain and model poisoning entries in the OWASP Top 10 for LLM applications, which most prompt-focused tools do not touch at all. If your organisation pulls third-party model weights from public hubs, this is the risk nobody else here is checking.

Key features

• Scanning of model artefacts for malicious code and tampering before deployment.
• Red teaming services covering model and pipeline attack surfaces.
• Detection and response tooling aimed at ML assets in production.
• Governance reporting for regulated AI estates.

Pros:

• Covers supply-chain risk that prompt-level tools structurally cannot see
• Scans model artefacts for malicious code and tampering before deployment
• Governance reporting suited to regulated AI estates

Cons:

• Enterprise-shaped and correspondingly heavier to adopt
• Largely irrelevant if you only consume a hosted model API

Pricing. Commercial and enterprise-oriented. Check the vendor's site for current figures.

Verdict. Necessary when you host your own models or ingest third-party weights, optional when you do not.