Prompt

Can you recommend an adversarial testing tool for finding prompt injections in a multi-turn support agent?

Artificial Intelligence / AI Safety & Alignment1 observationLast seen Jul 20, 2026

Latest observation

Jul 20, 2026OpenAI APIWeb search: off

Yes — a strong option is Giskard.

Why Giskard fits your use case

  • It can test LLM applications for prompt injection and jailbreak-like behaviors
  • It supports multi-turn conversational flows, which is important for support agents
  • It lets you define custom adversarial test suites and run them repeatedly in CI
  • It’s useful for evaluating whether the agent:
    • follows malicious user instructions,
    • leaks system prompts or hidden policies,
    • ignores tool/role boundaries,
    • or gets manipulated across turns

Other good options

  • promptfoo
    Great for lightweight, scriptable adversarial tests and regression checks. Good if you want to build your own injection cases and run them in CI.

  • OpenAI Evals
    Best if you want a flexible evaluation harness and are comfortable authoring your own test cases. More DIY, but powerful.

  • Red teaming frameworks / libraries such as:

    • PyRIT
      Designed for adversarial testing and red teaming of AI systems, including prompt injection-style attacks.

My practical recommendation

For a multi-turn support agent, I’d start with:

  1. PyRIT if you want more explicit red-teaming workflows,
  2. Giskard if you want a more evaluation-oriented product,
  3. promptfoo if you want a simple CI-friendly regression harness.

If you want, I can also suggest:

  • a tool comparison table, or
  • a sample test plan for prompt injection in a multi-turn support bot.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

Want this interpreted for your brand?

Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.