Prompt

What's the best LLM red teaming platform for jailbreak testing on chat-based agents?

Artificial Intelligence / AI Safety & Alignment1 observationLast seen Jul 19, 2026

Latest observation

Jul 19, 2026OpenAI APIWeb search: off

There isn’t a single universally “best” platform, but for jailbreak testing on chat-based agents, these are the strongest options depending on what you need:

Top picks

1. Garak

  • Best for: Open-ended adversarial testing and jailbreak-style probing
  • Why it stands out: Purpose-built for finding model weaknesses, including prompt injection, jailbreaks, and policy bypasses
  • Good if you want: A security-research style tool that can run many attack probes quickly
  • Limitations: More of a scanner/framework than a polished enterprise workflow product

2. Microsoft PyRIT

  • Best for: Structured red teaming workflows
  • Why it stands out: Strong framework for automation, scoring, and iterative adversarial testing
  • Good if you want: Repeatable evaluations and integration into a broader safety pipeline
  • Limitations: You’ll need some engineering effort to set up and customize

3. Lakera / Guardrails-focused platforms

  • Best for: Production-oriented testing around prompt injection and agent security
  • Why it stands out: Useful when your main concern is tool-using or chat agents exposed to malicious inputs
  • Good if you want: Defenses + testing in a more productized package
  • Limitations: Less flexible for deep research-style jailbreak exploration than a dedicated red-team framework

4. OpenAI Evals / custom eval harnesses

  • Best for: Teams that want full control
  • Why it stands out: You can define exact jailbreak test sets, scoring, and regression tests
  • Good if you want: CI/CD-style safety testing
  • Limitations: Not a turnkey red teaming platform by itself

My practical recommendation

If your goal is specifically jailbreak testing for chat-based agents, I’d choose:

  • Garak for fastest adversarial discovery
  • PyRIT for formalized, repeatable red teaming
  • A custom eval harness to turn the most important jailbreaks into regression tests

What matters most when choosing

Look for:

  • Support for multi-turn attacks
  • Prompt injection and tool-use abuse testing
  • Ability to test system prompt leakage
  • Automated scoring or human-in-the-loop review
  • Exportable results for regression testing
  • Compatibility with your agent stack, especially if it uses tools/RAG

Bottom line

  • Best overall for jailbreak discovery: Garak
  • Best for enterprise-style red teaming workflows: PyRIT
  • Best for operational regression testing: Custom evals + your own attack corpus

If you want, I can also give you:

  1. a feature-by-feature comparison table, or
  2. a recommended stack for OpenAI / Anthropic / open-source chat agents.

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.