AI Red-Teaming Platforms Compared: Mindgard vs. Cisco AI Defense vs. Adversa AI

AI red-teaming, also called LLM adversarial testing, is the practice of deliberately attacking a language model, GenAI application, or AI agent before it reaches production, using the same techniques a real attacker would use, so vulnerabilities become logged findings instead of live incidents. The core attack categories these platforms probe for include prompt injection (getting a model to ignore its system instructions, either directly through user input or indirectly through content the model reads from a document, website, or tool output), jailbreaks (bypassing safety guardrails to produce disallowed output), model and data extraction (pulling training data, system prompts, or proprietary weights back out through crafted queries), and, increasingly, agent-specific failures like tool execution abuse, memory poisoning, and goal hijacking that only exist once a system moves from a chatbot to an autonomous agent with tools and memory of its own.

This is a different job from a conventional web application penetration test. A model can pass every standard security check, patched dependencies, correct authentication, no SQL injection, and still be trivially jailbroken, because the vulnerability lives in the model's own weights and behavior rather than in the surrounding application code. Mindgard, Cisco AI Defense, and Adversa AI are each built to close that gap, though from different starting points, as described below. If you are evaluating HiddenLayer, Lakera, or Prisma AIRS instead, see our comparison of those platforms; this guide covers a different set of vendors and does not re-profile those three in depth. For how agentic-specific risk fits into a broader access and threat model, see the AI agent enterprise security threat model.

Only one of the three, Cisco AI Defense, sits inside a much larger incumbent security vendor. Mindgard and Adversa AI both remain independent companies as of this writing, but they differ sharply in scope: Mindgard tests the broadest range of model types (LLMs, computer vision, audio, multimodal), while Adversa AI narrows its focus specifically to agentic AI systems.

The biggest practical difference between these three in 2026 is not detection technique alone, since all three test overlapping attack categories such as prompt injection and jailbreaks. The bigger difference is company origin and how tightly (or loosely) red-teaming is bound to a larger platform.

Mindgard was founded in 2022 as a spinout from AI security research at Lancaster University, by Dr. Peter Garraghan, Dr. Neeraj Suri, and Steve Street, drawing on more than a decade of academic work on adversarial machine learning. It remains an independent, privately held company whose entire roadmap is AI red-teaming, and it is SOC 2 Type II certified and GDPR compliant. Its architecture is deliberately black-box: the platform needs only an inference or API endpoint to test a target, not access to model weights or training data, which lets it test third-party and closed-source models a team does not itself operate, not only models it built.

Cisco AI Defense is not a from-scratch Cisco product. Cisco announced its intent to acquire Robust Intelligence, an independent AI security startup that had pioneered the term "algorithmic red teaming," in August 2024, and completed the deal in October 2024. Cisco introduced the resulting AI Defense offering in January 2025, with general availability following in March 2025. Robust Intelligence's testing technology became AI Defense's AI Validation capability, now positioned as one module inside a much larger Cisco security and networking portfolio, continuously updated by Cisco's Talos threat intelligence team rather than by Robust Intelligence's own research group operating independently.

Adversa AI takes a third path: an independent company that has built its reputation on published adversarial AI research (jailbreak techniques, prompt-injection case studies, and AI threat modeling), now productized as a continuous red-teaming platform aimed specifically at agentic AI. Rather than broadening across model types the way Mindgard does, Adversa AI narrows its scope to the failure modes unique to autonomous agents: tool execution abuse, memory poisoning, goal hijacking, and inter-agent trust failures, on top of the more familiar prompt injection and jailbreak categories.

Mindgard. Acts as an autonomous red-teamer using attacker-style reconnaissance. A newer reconnaissance module discovers AI guardrails, system prompts, tools, integrations, and external services to map the production attack surface of a model, agent, or application before attacking it. Mindgard tests complete AI systems rather than isolated models, chaining domain-specific attack techniques across one-shot and multi-step interactions to see how a model reasons, responds to feedback, and behaves under sustained pressure, and reports thousands of attack scenarios aligned to MITRE ATLAS and the OWASP LLM Top 10. Coverage spans LLMs, AI agents, computer vision, audio, and multimodal models, which is the widest model-type coverage of the three vendors compared here.

Cisco AI Defense. The broadest end-to-end lifecycle architecture of the three, spanning development through production. AI Validation performs an automated, algorithmic assessment of a model's safety and security vulnerabilities, continuously updated through Cisco's AI Threat Research team. Model Scanning separately inspects model repositories and machine-learning pipelines for malicious code, poisoned data, unsafe tools, compromised components, and, notably, high-risk Model Context Protocol (MCP) assets, informed by Cisco Talos threat intelligence. Runtime Guardrails then protect live prompts and responses using NVIDIA NeMo Guardrails as a structural foundation, with Cisco layering its own continuously updated protection against prompt injection, denial-of-service attempts, and sensitive data leaks on top. That MCP-scanning capability is worth a closer look if your team is also evaluating dedicated MCP server security scanners, since it overlaps with a narrower, MCP-specific buying decision.

Adversa AI. Built around autonomous adversarial campaigns rather than one-off scans. The platform runs continuous red-team campaigns that automatically re-test whenever a model, prompt, or configuration changes, rather than waiting for a scheduled or manually triggered run. It attacks AI systems the way an adversary would, covering prompt injection, jailbreaks, tool execution abuse, goal hijacking, memory poisoning, and inter-agent trust failures, then reports validated vulnerabilities with reproduction evidence, attacker context, and remediation guidance. That continuous, change-triggered model is the closest of the three to treating red-teaming as an always-on control rather than a periodic assessment.

Mindgard deploys as a SaaS platform, reachable through a CLI, a GitHub Action that pulls the latest attack techniques on each run, a Burp Suite plugin for teams already using that tool for conventional application testing, or a single-click setup. Public materials do not describe an on-premises or air-gapped deployment option, so confirm that directly with the vendor if data residency requirements demand it.

Cisco AI Defense is the only one of the three with a genuine on-premises deployment path. Cisco offers two options for the data plane: cloud-based, or on-premises using Cisco AI PODs. That matters most for organizations in regulated industries or with strict data-residency requirements that already run Cisco infrastructure elsewhere and would rather keep AI model traffic inside their own environment than send it to a vendor's cloud.

Adversa AI operates as a cloud platform built around continuous testing, re-running campaigns at CI/CD release gates as models, prompts, and configurations change. Public materials reviewed for this article do not detail a dedicated on-premises deployment option, so, as with Mindgard, confirm data residency and hosting directly during a proof of concept rather than assuming a fit.

Mindgard's GitHub Action and CLI are built for CI/CD from the outset, and the Burp Suite plugin gives application security teams already running Burp a way to add AI-specific testing to a workflow they already know, without adopting an entirely separate tool. Public materials do not list specific SIEM connectors, so SIEM and ticketing forwarding should be confirmed directly in a proof of concept.

Cisco AI Defense has the deepest built-in integration story by virtue of sitting inside Cisco's existing security and networking estate. Its confirmed NVIDIA NeMo Guardrails integration matters specifically for teams building on that framework, and Model Scanning's stated ability to generate custom model guardrails that feed directly into CI/CD pipelines for ongoing compliance is a concrete automation hook, not just an API claim. Organizations already forwarding Cisco Security Cloud telemetry into a SIEM are more likely to see AI Defense findings land in that same pipeline with less custom integration work than a net-new vendor would require.

Adversa AI states integration with SIEM, MLOps, CI/CD, and Jira, with vulnerabilities importing into ticketing systems alongside assignee, team, and status synchronization. That Jira-native workflow is a meaningful operational detail if remediation ownership needs to be tracked the same way conventional application vulnerabilities already are, rather than living only inside a separate AI security dashboard.

In all three cases, ask for a live demonstration of a specific CI/CD trigger, for example a pull-request check that fails a build above a chosen severity threshold, and the exact SIEM or ticketing forwarding format, rather than accepting "integrates with" as sufficient evidence of a working integration.

Running any of these tools well requires more than turning it on. Expect roughly this operational shape from each:

Mindgard's black-box, endpoint-only access model reduces the setup burden considerably, since there is no need to expose model weights or internal pipelines, but a security or ML engineering owner still needs to triage findings across a wide range of model types (LLM, vision, audio, multimodal) and decide what severity threshold blocks a release.

Cisco AI Defense carries the heaviest operational surface area of the three, because it is the most componentized: a team adopting AI Validation, Model Scanning, and Runtime Guardrails together is effectively standing up three related but distinct capabilities across the AI lifecycle. That is a reasonable tradeoff for a team that already operates Cisco security infrastructure and has staff comfortable in that ecosystem, and a heavier lift for a team that does not, particularly since MCP asset scanning and NeMo Guardrails integration both assume some familiarity with those adjacent technologies.

Adversa AI's continuous, change-triggered re-testing reduces the manual labor of remembering to re-run assessments after every model or prompt update, but that same continuity means findings arrive more frequently, and someone has to own triage on an ongoing basis rather than during periodic review windows. Its narrower agentic-AI focus also means a team testing a simple, non-agentic chatbot may find some of its attack categories, like inter-agent trust failures, are simply not applicable yet.

Across all three, budget for a tuning period. No vendor's default severity thresholds or attack-prompt library will match a specific application's actual risk tolerance out of the box.

None of the three vendors publishes public per-seat, per-model, or tiered pricing for their red-teaming products. Mindgard requires a direct sales conversation, though its CLI and GitHub Action make a lightweight technical evaluation easy to start before that conversation goes far. Cisco AI Defense pricing is typically folded into a broader Cisco security or Security Cloud licensing conversation, meaning a standalone "AI Defense-only" quote in isolation from an organization's existing or prospective Cisco relationship is unlikely. Adversa AI similarly requires a custom quote, with published third-party estimates for large enterprise deployments varying widely enough that none should be treated as reliable without direct vendor confirmation. Get a written, scoped quote from each vendor based on the number of models or agents tested, testing frequency, and, for Cisco AI Defense, how it fits into an existing or new Cisco agreement, rather than relying on any published estimate.

Mindgard strengths: an independent, AI-native company built entirely around red-teaming, with black-box testing that needs only an inference endpoint, the widest model-type coverage of the three (LLM, agent, computer vision, audio, multimodal), a dedicated reconnaissance module for attack-surface mapping, and developer-friendly delivery through a GitHub Action, CLI, and Burp Suite plugin.

Mindgard limitations: as a smaller, earlier-stage company relative to Cisco, it carries less certainty around long-term roadmap scale and does not offer a bundled runtime-detection or model-scanning product of its own; public documentation is also light on specific on-premises deployment and SIEM connector details, so both need direct verification.

Cisco AI Defense strengths: the broadest lifecycle architecture of the three, spanning pre-deployment model scanning, algorithmic red teaming, and runtime guardrails; the only vendor here offering a genuine on-premises deployment option (Cisco AI PODs); continuously updated threat intelligence from Cisco Talos; and a confirmed integration with NVIDIA NeMo Guardrails plus MCP asset risk scanning that overlaps usefully with adjacent agentic-infrastructure security needs.

Cisco AI Defense limitations: it is the newest of the three to carry its current branding, since the underlying Robust Intelligence platform was only rebranded and integrated into Cisco's portfolio starting in early 2025, so the red-teaming capability specifically has a shorter track record under the Cisco name than Mindgard's or Adversa AI's do under their own; standalone pricing separate from a broader Cisco agreement is unlikely to be available; and organizations with no existing Cisco footprint take on a much larger platform than they may need if red-teaming is the only capability they actually want.

Adversa AI strengths: a narrow, deliberate focus on agentic AI failure modes (tool execution abuse, memory poisoning, goal hijacking, inter-agent trust failures) that general-purpose LLM testing tools may cover less thoroughly; continuous, change-triggered re-testing rather than periodic scans; a Jira-native remediation workflow; and reported findings mapped to a wide set of frameworks, including OWASP LLM Top 10, MITRE ATLAS, the EU AI Act, and NIST AI RMF.

Adversa AI limitations: its agentic-specific focus is a poor match for a team whose only production AI system is a simple, non-agentic chatbot with no tool use or memory; it does not offer a bundled runtime-detection or model-scanning product; and, like its competitors, its own performance and detection claims have not been independently, third-party benchmarked in public reporting reviewed for this article.

Mindgard tends to fit teams that want a focused, independent AI red-teaming vendor covering the widest range of model types, including computer vision, audio, and multimodal systems alongside LLMs, delivered through a lightweight, developer-first workflow (CLI, GitHub Action, Burp Suite plugin) without committing to a much larger security platform purchase.

Cisco AI Defense tends to fit organizations already standardized on Cisco for networking or security, especially those with data-residency or regulatory requirements that make an on-premises deployment option (Cisco AI PODs) valuable, and that want red-teaming bundled with model supply-chain scanning and runtime guardrails under one existing vendor relationship, even if the red-teaming module specifically is newer under the Cisco name than the alternatives.

Adversa AI tends to fit teams whose primary production risk is agentic AI, autonomous agents with tool access and memory, rather than a single-turn chatbot, and that want continuous, change-triggered testing mapped to a broad set of compliance frameworks, with findings synchronized directly into an existing Jira-based remediation workflow.

All three fits should be confirmed against an organization's own environment. Marketing pages describe intended use cases; only a proof of concept against a real model, agent, or application confirms which one actually performs better for a specific team.

A dedicated AI red-teaming platform is not always the right next purchase. Consider skipping all three vendors, at least for now, if:

• There is no production LLM application or agent yet. If no live GenAI app, agent, or model is serving real traffic, only internal experimentation, a dedicated red-teaming platform is premature spend. A manual review against the OWASP Top 10 for Large Language Model Applications and a plan to revisit tooling once something reaches production is a reasonable interim step.
• The team is too small to act on findings. Automated red-teaming, especially Adversa AI's continuous, change-triggered model, produces a steady stream of findings that someone has to triage, prioritize, and fix. A one- or two-person security function already stretched across other duties may get more value from a smaller, focused manual assessment than from an always-on automated platform generating a backlog no one has time to close.
• The actual need is runtime protection, not pre-deployment testing. Red-teaming finds vulnerabilities before deployment; it does not by itself stop an attack against a live system. Of the three vendors compared here, only Cisco AI Defense sells a dedicated runtime guardrails product alongside its testing capability. If the real gap is production protection right now and none of these three fits, that is a separate buying decision from red-teaming itself.
• Your AI system is not agentic and budget is tight. If the production system in question is a straightforward, single-turn chatbot with no tool use, memory, or multi-agent coordination, Adversa AI's agent-specific attack categories add little value, and a broader-coverage tool like Mindgard, or a manual assessment, is likely the better starting point.
• Budget and procurement already favor a bundle in place. If an organization already pays for a broader Cisco security agreement and has not activated the AI Defense modules, confirm what is already included or discounted under the existing contract before treating a standalone Mindgard or Adversa AI purchase as the default option.

Run any AI red-teaming proof of concept against a real model, agent, or application, not a vendor-provided demo environment. Before committing budget, confirm the following with each vendor side by side.