LLM Red-Teaming Tools Compared: Garak, PyRIT, Robust Intelligence, HiddenLayer, Mindgard, and Protect AI Recon — Real Attack Libraries, Real Trade-offs (2026)

Manual red-teaming is a human security researcher sitting in front of a model, probing for weaknesses with creativity, context, and adversarial intent. Automated red-teaming is a tool that throws a library of pre-built attack prompts (or generates new ones via search algorithms like GCG) at the model and scores the responses. You need both, and the marketing copy from every vendor on this list conflates them on purpose. The OSS scanners (**Garak**, **PyRIT**) are automation tooling — they accelerate human red-teamers, they do not replace them. The commercial platforms layer dashboards, triage, and in some cases human services on top of similar automation engines. If a vendor tells you their product replaces manual red-teaming entirely, ask which 0-day jailbreak class their automation found last quarter that was not already in a public benchmark.

**Garak** (https://github.com/leondz/garak) is the closest analog to Nessus or OpenVAS for LLMs. You point it at an endpoint — an OpenAI key, a Hugging Face model, a local llama.cpp server — and it runs 120+ probes across categories like prompt injection, jailbreaks, training-data leakage, malware generation, toxicity, and PII extraction. Each probe is a Python module with seed prompts, mutation strategies, and an output detector. The NVIDIA acquisition in 2024 brought engineering resources and tighter NeMo Guardrails integration, but the project remains Apache 2.0 and community-driven. It is the right starting point for any team that wants automated coverage today without a procurement cycle.

**PyRIT** (https://github.com/Azure/PyRIT) is a framework, not a scanner. Microsoft's AI Red Team built it to automate the workflow they were running by hand against Copilot, Bing Chat, and Azure OpenAI deployments. The mental model is converters (transform a seed prompt), orchestrators (chain prompts together for multi-turn attacks), and scorers (judge whether the attack succeeded). PyRIT ships with HarmBench and ManyShotJailbreak datasets, supports red-team-as-a-judge patterns, and integrates with Azure OpenAI plus any provider through a thin adapter. It has a steeper learning curve than Garak but a higher ceiling — if your red-team can write Python, PyRIT lets them codify their playbook.

**Robust Intelligence** (https://www.robustintelligence.com/) was the leading commercial AI security platform before Cisco's 2024 acquisition. It now sits inside Cisco AI Defense as both a pre-deployment validation engine and a runtime guardrail. The product runs a proprietary attack library against your model, generates an executive-ready risk report, and exposes APIs to wire validation into CI/CD. The Cisco distribution channel makes this a default short-list entry for any organization already buying Cisco security. The integration with Talos threat intelligence is the differentiator — you get attack patterns informed by Cisco's broader telemetry.

**HiddenLayer** (https://hiddenlayer.com/) takes a runtime-first approach. The flagship product is AI Detection & Response, which behaves like an EDR for AI — it watches inference traffic for adversarial inputs, model extraction attempts, and data exfiltration. The Model Scanner is the pre-deploy companion, scanning model files (PyTorch, TensorFlow, ONNX, Pickle) for serialization attacks and known malicious payloads. HiddenLayer's Synaptic Adversarial Intelligence (SAI) team publishes ongoing threat research and offers managed red-teaming as a service. The platform is the right answer if your concern is supply-chain risk on the model artifacts themselves, not just prompt-level attacks.

**Mindgard** (https://mindgard.ai/) is a UK-based platform that frames itself as continuous offensive testing — closer to an external pentest-as-a-service offering than a tool you install. The differentiator is the depth of human-driven research backing the automated attacks; Mindgard's team has published several notable jailbreak techniques. **Protect AI Recon** (https://protectai.com/recon) sits inside the broader Protect AI Platform (which also includes Guardian for model scanning and Layer for runtime). Recon is the LLM penetration testing layer, mapping findings to OWASP LLM Top 10 and feeding them into the same dashboards as Guardian's static analysis. If you have already bought Protect AI for ML supply chain, Recon is the natural extension.

The capability of any automated red-teaming tool is largely the capability of its attack library. Three public benchmarks define the state of the art in 2026. **HarmBench** (https://www.harmbench.org/) is the Center for AI Safety's standardized evaluation framework, covering 510 harmful behaviors across categories like cybercrime, bio, chemical, and misinformation, with automated graders. **JailbreakBench** (https://jailbreakbench.github.io/) is the Princeton-led open benchmark for jailbreak attacks and defenses, providing standardized attack artifacts and an evaluation leaderboard. **AdvBench** (introduced in the GCG paper) is the older harmful-strings benchmark that powers many automated optimization attacks. If a vendor cannot tell you which of these their library covers, that is information.

**Garak** ships HarmBench-aligned probes, the original GCG / AdvBench harmful strings, the DAN family of jailbreak templates, Real Toxicity Prompts, and a steady stream of community-contributed probe modules. The probe catalog is public at https://github.com/leondz/garak/tree/main/garak/probes — you can read exactly what each probe does. The honest limitation is that the detector quality varies: some probes use simple string matching to determine success, which understates true risk for any model that paraphrases. Pair Garak with a stronger LLM-as-a-judge layer if you want defensible numbers.

**PyRIT** is dataset-agnostic by design. It ships connectors for HarmBench, ManyShotJailbreak (Anthropic's many-shot jailbreaking paper), AdvBench, and a handful of seed sets, but the expectation is that you bring or build your own. The framework's value is the orchestrator pattern — you can compose a multi-turn attack where one model generates jailbreak candidates, another model scores them, and a third executes them against the target. This crowd-of-attackers pattern is what Microsoft's AI Red Team uses internally against Copilot, and PyRIT exposes it as configurable code.

**Robust Intelligence** and **Cisco AI Defense** publish less about their underlying library composition, but Cisco's product documentation confirms HarmBench coverage and proprietary additions sourced from Talos threat research. The pitch is that the library is curated and continuously updated by a paid team, which is a real advantage if you do not have an internal red-team to maintain probe quality. The trade-off is opacity — you cannot audit the library the way you can with Garak.

**HiddenLayer** publishes the Synaptic Adversarial Intelligence threat reports (https://hiddenlayer.com/research/) which give the clearest public view of their offensive research. The Model Scanner side covers a different threat surface entirely — Pickle deserialization attacks, malicious ONNX operators, and supply-chain backdoors in model weights. This is not redundant with prompt-level scanners; it is complementary. Garak will never find a malicious Pickle. HiddenLayer's scanner will.

**Mindgard** maps its proprietary attack library to MITRE ATLAS (https://atlas.mitre.org/), the adversarial threat matrix for AI systems, which is a useful framing for security teams already living in MITRE ATT&CK for the rest of their stack. **Protect AI Recon** maps to OWASP LLM Top 10 and feeds findings into Protect AI's broader threat intelligence (https://protectai.com/threat-research/). The crowdsourced **HackAPrompt** dataset (collected by Lakera and now used widely as training data for jailbreak classifiers) sits underneath several commercial libraries — it is the largest public corpus of adversarial prompts, and most credible vendors have incorporated it in some form.

Red-teaming that only happens at procurement is theater. Real coverage means probes running on every model update, every prompt template change, and every fine-tune. **Garak**'s CLI returns standard exit codes and JSON reports, which makes it trivial to wire into a GitHub Actions workflow — call garak in a job, fail the build on critical findings, and upload the HTML report as an artifact. The repo at https://github.com/leondz/garak includes example Dockerfiles and the command-line interface is stable enough to script against. Most engineering teams can get Garak running in a pull-request check in under a day.

**PyRIT** integrates the same way but at a different level — it is a Python library you import in a test script. The pattern most teams use is a pytest suite that instantiates a PyRIT orchestrator, runs it against a staging deployment of the model, and asserts on the success rate of a known attack panel. This works well for teams that already have a Python test infrastructure; for teams that do not, PyRIT will feel heavier than Garak. The Microsoft team publishes example notebooks at https://github.com/Azure/PyRIT/tree/main/doc that are worth reading before you commit to a workflow.

**Robust Intelligence / Cisco AI Defense** ships official CI/CD integrations with documented APIs for triggering validation runs from Jenkins, GitLab, GitHub Actions, and Azure DevOps. The dashboard correlates findings across runs so you can see trend lines, which the OSS tools cannot do natively. This is the right architecture for an enterprise SOC that wants centralized visibility across dozens of model deployments. Implementation typically takes 4 to 6 weeks for the first integration and shrinks for subsequent ones.

**HiddenLayer** publishes a GitHub Action and CLI for the Model Scanner that fits naturally into model promotion pipelines — scan the artifact before it lands in your model registry. The AI Detection & Response side integrates at runtime via a sidecar or proxy, which is a separate deployment decision from the CI pipeline. Most HiddenLayer customers deploy the scanner first (low risk, high signal on supply-chain attacks) and add ADR after they have an inference observability story.

**Mindgard** offers GitHub Actions, Jenkins, and GitLab integrations plus a REST API for triggering offensive runs. Because Mindgard frames itself as continuous testing rather than scan-on-commit, the dominant pattern is a scheduled nightly run against staging plus an on-demand run from the dashboard before major releases. The Slack and Jira integrations route findings directly to engineering teams, which closes the loop better than the OSS tools do by default.

**Protect AI Recon** integrates through the broader Protect AI Platform CLI and CI plugins (https://protectai.com/platform/). Findings flow into the same dashboard as Guardian's model scanning and Layer's runtime monitoring, which is the architectural argument for buying Protect AI as a suite. **AISI Inspect** (https://inspect.aisi.org.uk/), the UK AI Safety Institute's open-source evaluation framework, is increasingly the layer underneath custom evaluation pipelines — not a red-teaming tool itself, but the harness many internal red-teams use to structure their evaluations across all the above libraries. Worth knowing about even if you never deploy it directly.

The OSS scanners are free to license, not free to operate. **Garak** runs as many probes as you let it, and each probe sends prompts to your target model — which costs money if the target is an OpenAI, Anthropic, or other paid API. A full Garak run against a GPT-4o endpoint with all probes enabled can consume 100,000 to 500,000 tokens of input and a similar volume of output, depending on probe verbosity. At current OpenAI pricing (https://openai.com/api/pricing/), that is roughly $5 to $20 per full scan run. Run it on every PR and the bill is meaningful. Most teams scope Garak to a critical subset of probes for PR checks and run the full sweep nightly.

**PyRIT** has the same compute economics with an added twist: many PyRIT orchestrators use an attacker LLM and a judge LLM in addition to the target. A red-team-as-a-judge run against a single hardened model can easily run $50 to $200 in API costs. The compute cost is still trivial compared to a human red-teamer at $250 an hour, but it is not zero. Budget for it explicitly in your tooling spend rather than letting it surface as a surprise on the OpenAI bill. Use the OpenAI API cost calculator to model your expected spend before you wire PyRIT into a nightly schedule.

**Robust Intelligence / Cisco AI Defense** pricing is custom and not publicly published, but multiple market signals as of June 2026 put a typical enterprise deployment in the $80,000 to $300,000 annual range, depending on number of models covered, on-prem vs SaaS, and whether Cisco Talos human services are bundled. The price reflects the dashboards, the curated library, the integration support, and the SOC 2 / FedRAMP posture that Garak and PyRIT cannot match. Negotiate firmly — Cisco's AI security pricing has compressed materially as the category has commoditized.

**HiddenLayer** pricing is similarly custom. Public signals put the Model Scanner alone in the $30,000 to $80,000 range and the full ADR platform in the $50,000 to $200,000 range, depending on inference volume. The runtime side is priced on inference scale, which is the only line item that grows with your usage rather than your headcount — model your projected inference volume carefully before signing a multi-year deal. Confirm current pricing at https://hiddenlayer.com/pricing/ or via direct sales conversation.

**Mindgard** pricing skews European mid-market — typical engagements as of June 2026 land in the $40,000 to $150,000 range, with managed red-teaming services priced separately. The platform-plus-services bundle is genuinely useful for organizations that do not have internal red-team capacity, but verify whether the services hours are included or sold separately in writing. **Protect AI Recon** is rarely sold standalone; the typical buyer takes the full Protect AI Platform (Guardian + Recon + Layer) at $60,000 to $200,000 annually. If you only need pre-deployment LLM testing, Recon alone may not be the most efficient buy — Garak plus PyRIT plus a small services budget can deliver comparable coverage for a fraction of the cost.

The honest bottom line on cost: the OSS tools are free in license terms and roughly $5,000 to $20,000 per year in compute plus 0.25 to 0.5 FTE in engineering time. The commercial tools are roughly $50,000 to $300,000 per year and replace 0.25 to 1.0 FTE in dashboard-building, triage, and reporting. The break-even depends on whether you can hire and retain that 0.5 to 1.0 FTE — which in 2026 is harder than it sounds, because AI security engineers with red-team experience are scarce and expensive.

If you are a small engineering team shipping an LLM-powered feature into production and the security ask is 'show me you tested for jailbreaks,' deploy **Garak** in your CI today. The probe library covers OWASP LLM Top 10 well enough to demonstrate due diligence to an enterprise customer's security review, and the cost is engineering time plus a small API bill. Wire it into a GitHub Action, fail builds on critical findings, and archive the HTML report. You can layer commercial tooling later when budget or scale demands it. Source the install at https://github.com/leondz/garak.

If you are a security-engineering-heavy organization with a dedicated AI red-team, deploy **PyRIT** as the base layer of your offensive tooling. It is the only platform on this list that gives a skilled team the building blocks to codify novel attack chains rather than running pre-built probes. The community around PyRIT (https://github.com/Azure/PyRIT) is the most active in offensive LLM security in 2026, and the Microsoft AI Red Team's published research is downstream of techniques you can replicate with the tool. Pair PyRIT with Garak for breadth and you have the strongest OSS coverage available.

If you are a regulated enterprise — financial services, healthcare, government — and your CISO needs an auditable AI security posture for a regulator, buy **Robust Intelligence / Cisco AI Defense** or **HiddenLayer**. The dashboards, executive reporting, and human services are the value, not the underlying probes. Cisco AI Defense (https://www.robustintelligence.com/) wins if you already have Cisco anywhere in your security stack. HiddenLayer wins if model supply-chain attacks (Pickle, ONNX) are a top-three concern, because the Model Scanner is the strongest in the category.

If you are a mid-market European enterprise that does not want to staff an internal AI red-team, **Mindgard** (https://mindgard.ai/) is the strongest combination of platform plus services on this list. The UK government's adoption is meaningful signal, and the managed offering means you get attack output without owning the operational burden. The trade-off is that Mindgard's North American presence is smaller than Cisco's or HiddenLayer's, which matters if your procurement team requires US-headquartered vendors.

If you are already buying the Protect AI Platform for ML supply-chain scanning, add **Recon** rather than evaluating standalone alternatives. The integrated dashboard plus shared threat research is a real workflow advantage, and the marginal cost of adding Recon to a Guardian deployment is much lower than buying a separate red-teaming tool. If you are not already a Protect AI customer, Recon alone is not the obvious pick.

If you are evaluating frontier-model risk at the policy or governance layer — pre-deployment evaluations for a major model release, third-party audits, or regulator-facing risk reports — the right harness is increasingly **AISI Inspect** (https://inspect.aisi.org.uk/), the UK AI Safety Institute's open-source evaluation framework. Inspect is not a red-teaming tool per se; it is the framework that lets you compose evaluation suites in a defensible, reproducible way. Major AI labs and several governments use it. If you need 'we ran the same evaluations the UK government runs,' Inspect is the answer.

Public benchmarks consistently show that no production model is jailbreak-free in 2026 — including frontier models from OpenAI, Anthropic, and Google. The JailbreakBench leaderboard (https://jailbreakbench.github.io/) tracks attack success rates across published methods, and even hardened models show meaningful attack success on the strongest optimization-based attacks. The honest reading of this data is not that any one model is broken; it is that defense-in-depth — input filtering, output filtering, monitoring, and abuse response — is the only viable strategy. Red-teaming tools surface where your specific deployment sits on that curve.

HarmBench (https://www.harmbench.org/) results show that automated graders agree with human red-teamers roughly 80 to 90 percent of the time on whether an attack succeeded, depending on the category. Cybercrime and code-generation categories are easier to grade; nuanced misinformation and bio-related categories are harder. This means automated red-teaming dashboards inflate or deflate true risk by 10 to 20 percent on average, and the direction varies by category. If you are presenting numbers to executives, present them with that confidence interval, not as a single percentage point.

The GCG family of attacks (Greedy Coordinate Gradient) introduced in the AdvBench paper showed that adversarial suffixes can be optimized against open-weights models and then transferred to closed-weights models with non-trivial success. This finding underpins much of the automation in Garak and PyRIT's stronger orchestrators. The practical takeaway is that if your model is publicly accessible, you should assume adversarial-suffix attacks are being run against it continuously, and your detection and rate-limiting layers matter as much as your prompt design.

The Anthropic many-shot jailbreaking paper (https://www.anthropic.com/research/many-shot-jailbreaking) showed that long-context models can be jailbroken by stuffing the context with many examples of the model complying with the targeted behavior. PyRIT's ManyShotJailbreak orchestrator implements this attack directly. The implication for your red-teaming program is that as your model's context window grows, the attack surface grows with it — a tool that only tests at 4K context is missing the 200K-context attack class entirely.

Prompt injection — distinct from jailbreaking — is the highest-impact unsolved problem in the category. Indirect prompt injection (where attack content arrives via a retrieved document, tool output, or rendered HTML) routinely bypasses the same models that resist direct jailbreaks. The OWASP LLM Top 10 (https://owasp.org/www-project-top-10-for-large-language-model-applications/) ranks prompt injection as LLM01 for a reason, and every credible commercial red-teaming tool now ships an indirect prompt injection probe set. Verify this is in scope before you commit to any tool. See our prompt injection defense guide for the runtime side.

HackAPrompt — the 2023 crowdsourced jailbreak competition organized by Learn Prompting and acquired by Lakera as training data — produced the largest public corpus of human-generated adversarial prompts in existence (https://www.lakera.ai/blog/hackaprompt). Several commercial vendors on this list have incorporated subsets of this dataset into their attack libraries, either directly or as training data for jailbreak-classification models. If a vendor cannot tell you whether HackAPrompt is in their corpus, ask why.

Some security leaders ask whether they can skip the commercial tools entirely and build offensive coverage on **Garak** plus **PyRIT** plus **AISI Inspect**. For organizations with a competent AI security engineer and a willingness to maintain Python tooling, yes — this stack covers 70 to 85 percent of what the commercial platforms deliver, at a tenth of the cost. The shortfall is dashboards, executive-ready reports, and on-call human red-team services. If your CISO does not need to hand a PDF to a board committee, the OSS stack is genuinely defensible.

Where the OSS stack falls short: cross-deployment trend lines (commercial tools correlate findings across models and time), executive reporting (commercial dashboards are designed for the audit and procurement workflow), threat intelligence updates (Cisco Talos, HiddenLayer SAI, Protect AI Threat Research all publish updates faster than community OSS probes), and incident response support. If any one of these is a real requirement, the commercial tools earn their price.

The hybrid pattern that works in 2026: run **Garak** plus **PyRIT** in CI for breadth and developer velocity, layer **AISI Inspect** for structured pre-release evaluations, and buy a commercial platform — **Cisco AI Defense**, **HiddenLayer**, or **Mindgard** — for the dashboards and regulator-facing reports. This combination costs more than OSS-only but materially more useful than commercial-only, because the OSS layer gives your engineering team the inner-loop velocity they need to actually fix findings rather than just observing them in a dashboard.

The build-only path — writing your own probes from scratch — is almost never the right answer in 2026. The public attack libraries (HarmBench, JailbreakBench, AdvBench, HackAPrompt, ManyShotJailbreak) are mature and continuously updated by academic and industry teams who specialize in this. You will not out-research them by writing probes yourself. Where custom probes do make sense: domain-specific risks your product faces that public benchmarks do not cover — for example, a healthcare product testing for PHI leakage in a clinical-context format that HarmBench does not include.

If you go the build route, the operational discipline that matters more than tool choice is the triage workflow. A red-teaming tool that generates 500 findings per scan with no triage is worse than no tool at all — your engineers will learn to ignore the reports within a quarter. Whether you buy or build, invest in the workflow: severity grading, deduplication, assignment, and SLAs for remediation. Most failed AI security programs in 2026 fail at triage, not at detection.

The cost calculator at the RAG cost per query calculator is also relevant if your LLM deployment is RAG-based — the most under-tested attack class in 2026 is indirect prompt injection via retrieved documents, and your red-teaming budget should explicitly cover RAG-specific probes. Most OSS scanners can be configured to test this; most commercial platforms ship pre-built RAG probes. Verify in your tool selection.

**Garak** implementation is the fastest on this list. Day 1: pip install garak and run it against a staging endpoint to get a baseline. Week 1: triage the initial findings, suppress known false positives, and pick the critical probe subset for CI. Week 2: wire Garak into GitHub Actions or equivalent with appropriate timeouts and budget caps. Week 3: stand up the nightly full-sweep job and a dashboard (Grafana on top of the JSON output works fine). Week 4: document the triage workflow and onboard the engineering team. Most teams reach steady state by week 6.

**PyRIT** implementation takes longer because the framework is more flexible. Week 1-2: a security engineer reads the documentation, runs the example notebooks, and stands up an initial orchestrator against staging. Week 3-4: build the first red-team-as-a-judge pipeline, calibrate the judge model, and integrate with whichever test framework you use. Week 5-8: build out custom orchestrators for your specific attack surface (RAG retrieval injection, tool-use prompt injection, multi-turn jailbreaks). Plan on a senior engineer at roughly 50 percent allocation for the first 8 weeks.

**Robust Intelligence / Cisco AI Defense** enterprise rollouts run 6 to 12 weeks. Week 1-2: contract execution and tenant provisioning. Week 3-4: identity and SSO integration, role-based access setup, model registration. Week 5-8: CI/CD pipeline integration, initial baseline scans, dashboard configuration. Week 9-12: executive reporting setup, SOC and IR runbook integration, optional Cisco Talos services kickoff. The vendor's CSM team is thorough but not fast — budget for it in your Q1 capacity planning.

**HiddenLayer** Model Scanner deploys in 2 to 4 weeks because the scope is narrower — install the agent in your model registry or CI, configure the policies, and start scanning. The full AI Detection & Response platform takes 6 to 10 weeks because the runtime sidecar requires infrastructure work and the inference observability story has to be designed with your platform team. Most HiddenLayer customers do scanner first, ADR second.

**Mindgard** implementation lands at 4 to 8 weeks depending on whether you take the managed services bundle. Platform-only deployments are faster (configure target models, run baseline scan, integrate Slack/Jira). Managed-services engagements add a kickoff workshop, scope definition, and a researcher-led initial red-team that typically takes the longer end of that range. Budget for the change-management piece of getting engineering teams to act on findings — most platforms generate output faster than engineers can remediate.

**Protect AI Recon** implementation runs 4 to 8 weeks within a broader Protect AI Platform rollout. Standalone Recon (rare) takes 3 to 5 weeks because the integration scope is narrower. The dominant pattern is Guardian first (model scanning, lower disruption), Recon second (LLM testing, requires endpoint access), and Layer third (runtime monitoring, requires infrastructure work). Plan accordingly if you are buying the full suite.

If I were standing up an LLM security program tomorrow at a 200-engineer SaaS company shipping an AI feature into production, I would deploy **Garak** in CI on day one, **PyRIT** for our internal red-team within the first quarter, and budget for a commercial platform at the 12-month mark once we knew which dashboards we actually needed. The combined upfront cost is a few thousand dollars in compute and roughly 0.5 FTE of engineering time. The coverage demonstrates due diligence to enterprise customers and gives our internal team the velocity they need to actually fix findings, not just file them.

If I were a CISO at a regulated enterprise — say, a top-50 US bank deploying internal LLM tooling for analysts — I would buy **Cisco AI Defense / Robust Intelligence** for the executive reporting and audit posture, plus deploy **Garak** in CI underneath it for engineering velocity. The combined annual cost is real (well into six figures), but the OSS layer gives engineering teams the same inner-loop tooling they would build anyway, and the commercial layer gives the SOC and audit teams the dashboards and reports they need. Do not skip the OSS layer just because you have the commercial budget — the workflow benefits are independent.

If I were a UK or EU mid-market enterprise with no dedicated AI red-team, I would buy **Mindgard** specifically for the managed services bundle and use **Garak** for self-serve CI testing in between formal engagements. This combination gives you continuous coverage plus periodic deep-dive human red-team output without staffing the function internally. Verify the managed services scope in the master services agreement — make sure it includes incident-driven probes, not just scheduled testing.

If I were an MLOps-heavy organization already buying **Protect AI** for ML supply-chain scanning, I would add **Recon** rather than evaluating alternatives — the integrated dashboard plus shared threat research is worth more than a marginal capability advantage at another vendor. If I were not already in the Protect AI ecosystem, I would not buy Recon standalone — Garak plus PyRIT plus a smaller commercial dashboard tool delivers comparable coverage at lower cost.

If I were responsible for pre-deployment evaluation of a frontier model — at an AI lab, a government agency, or a third-party auditor — the right harness is **AISI Inspect** with Garak and PyRIT as input layers and HarmBench / JailbreakBench / HackAPrompt as the evaluation corpora. This is the closest 2026 has to a defensible, reproducible pre-release evaluation pipeline, and it is what the UK AI Safety Institute itself uses for the labs it has evaluated under the voluntary commitments framework. Worth knowing about even if you never run Inspect yourself, because the regulatory direction of travel points here.

The one thing I would not do in 2026 is buy two commercial red-teaming platforms. Cisco AI Defense, HiddenLayer, Mindgard, and Protect AI Recon overlap enough that buying two is paying twice for similar dashboards. Pick a lane based on the criteria above — already a Cisco shop, regulated and supply-chain-anxious, European mid-market, or already a Protect AI customer — justify it to procurement, and reinvest the saved budget into the OSS layer plus a human red-teamer (internal hire or managed services). The marginal value of a second commercial dashboard is near zero. The marginal value of a skilled human red-teamer is substantial.