Mindgard and the Rise of AI-Native Offensive Security Platforms

In 2026 a new product category matured: AI-native offensive security platforms that test models, agents, and multimodal apps continuously — inside the developer workflow, not once a year.

Open-source scanners like Garak and PyRIT are excellent, but enterprises increasingly want continuous, managed coverage that plugs into CI and Burp. Mindgard is a leading example, alongside HiddenLayer, Protect AI, WitnessAI, and F5’s AI offerings. They are built to test AI systems specifically — LLMs, agents, and multimodal pipelines — and to keep AI security visible in the SDLC.

Using the Mindgard CLI

Mindgard ships a Python CLI so you can run tests from a terminal or a pipeline:

pip install mindgard
mindgard login
mindgard test --config-file mymodel.toml --parallelism 5

A minimal target config (mymodel.toml) describes the endpoint and how to talk to it:

target = "my-support-bot"
url = "https://your-app.example/api/chat"
request_template = '{"message": "{prompt}"}'
selector = "$.response"
system_prompt = "You are a helpful support assistant."

Into CI/CD and Burp

• GitHub Actions: Mindgard publishes an action that pulls the latest CLI and runs your test suite on every model or prompt change — so AI security regressions fail the build.
• Burp Suite: a Mindgard Burp Intruder extension lets you drive AI tests from the same tool your web pentesters already live in, including WebSocket chatbots.

The 2026 platform landscape

• Mindgard — offensive testing of LLMs/agents/multimodal, native Burp + GitHub Actions.
• HiddenLayer — model scanning, detection & response for ML assets.
• Protect AI — ML supply-chain and model-scanning focus.
• WitnessAI — runtime AI governance and guardrails.

Platform vs. people

These platforms are excellent at coverage, regression detection, and continuity. What they do not replace is the human red-teamer who finds the novel logic flaw, chains it across systems, and explains the real-world business impact. The 2026 best practice is a hybrid: a platform for breadth and continuous monitoring, experts for depth and judgement.

The RingSafe model

We combine AI-native tooling with experienced offensive engineers, so Indian teams get continuous coverage and the deep, contextual testing that finds the bugs that matter. Explore RingSafe VAPT or book a consultation.