Automated red teaming suites for prompt injection testing

For a prompt-injection penetration test, there are several established providers of automated AI red-teaming suites. They range from open-source tools you can run in CI/CD to commercial platforms that continuously test LLM applications, RAG pipelines, and AI agents for prompt injection, jailbreaks, data leakage, and unsafe tool use.owasp.github.io+2

Leading automated red-teaming suites

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1. Promptfoo

(Best starting point)

Open source + enterprise

Automates adversarial testing for prompt injection, jailbreaks, RAG poisoning, and data exfiltration. Supports API and browser-based targets and CI/CD integration. A strong choice for repeatable penetration tests and regression testing.www.promptfoo.dev+1

Website and documentation

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2. Mindgard

Commercial platform + expert services

Emulates attacker behavior across complete AI systems, including multi-step prompt injection, agent hijacking, tool abuse, and data extraction. Particularly relevant when you need evidence of exploitability, remediation guidance, and continuous testing.mindgard.ai+1

Automated AI red teaming

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3. HiddenLayer

Commercial enterprise platform

Offers automated adversarial testing alongside broader AI security capabilities. Worth evaluating for organizations that want to test prompt-injection resistance and jailbreak defenses across multiple AI systems.www.hiddenlayer.com+1

AI red teaming

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4. garak

Free, open-source scanner

Runs probe libraries against LLMs to identify weaknesses such as prompt injection, jailbreaks, and information leakage. Best for technical teams wanting a scriptable scanner and control over their test environment.owasp.github.io+2

GitHub project

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5. Microsoft PyRIT

Free, open-source framework

Provides components for orchestrating automated, multi-turn adversarial tests against generative AI systems. Suited to security engineers building customized penetration-test workflows; it requires more implementation work than a turnkey commercial suite.owasp.github.io+2

GitHub project

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6. Giskard

Open source + hosted options

Generates and executes security tests for LLM applications, agents, and retrieval-augmented generation (RAG) systems. A useful option when prompt-injection testing needs to sit alongside broader model and application evaluations.owasp.github.io+1

Website

Which should you evaluate first?

These are different types of products, so compare actual attack coverage rather than treating them as interchangeable. In particular, verify that a suite tests indirect prompt injection through retrieved documents, webpages, emails, and tool outputs—not just malicious text typed directly into a chatbot.

For a penetration test, also check whether it can safely test an authorized staging target, capture reproducible attack traces, validate unauthorized tool calls or data access, and export findings for retesting.

One additional option: OpenAI Red Teaming offers a managed enterprise service, alongside guidance for using Promptfoo for open-source testing.developers.openai.com+1

My recommendation: start with Promptfoo for a fast, reproducible baseline, then evaluate Mindgard if you need deeper, adaptive attacks against agents and connected tools. Run all testing against systems you own or have explicit authorization to assess.

When looking for an automated red teaming platform focused on prompt injection and LLM vulnerability testing, options generally fall into open-source frameworks, developer-first testing tools , and enterprise cloud platforms.

The top platforms and tools available for automated prompt injection pen testing include:

1. Developer-First & CLI Platforms (Open Source / Commercial)

• Promptfoo : One of the most popular tools for continuous LLM red teaming. It automates testing across 50+ vulnerability types—including direct/indirect prompt injection, jailbreaking, PII leaks, and system prompt overrides. It generates application-specific attack matrices and maps findings to frameworks like OWASP Top 10 for LLMs and NIST.promptfoo.dev
• Garak (Generative AI Red-teaming & Assessment Kit) : Created by NVIDIA, Garak functions like an "Nmap for LLMs." It is a robust, open-source vulnerability scanner that probes endpoints for prompt injection, data leakage, jailbreaks, and toxic outputs using both static and dynamic/adaptive probes.github.com+1

2. Specialized & Penetration-Testing Utilities

• Augustus by Praetorian : An open-source, portable binary built specifically to fit into existing human-led penetration testing workflows. It eliminates heavy Python/npm dependencies and targets classic jailbreak and prompt injection techniques (such as DAN variants and Goodside-style injections).praetorian.com
• Giskard : An open-source testing framework with a strong focus on automated detection of LLM vulnerabilities, hallucination scoring, and security vulnerabilities like prompt injections.deeplearning.ai

3. Enterprise Cloud & Ecosystem Platforms

• Azure AI Foundry / AI Red Teaming Agent : Microsoft’s enterprise platform includes an automated AI Red Teaming Agent that uses synthetic adversarial datasets to probe target AI systems for direct and indirect jailbreak vulnerabilities and prompt injections prior to production deployment.
• Dedicated Enterprise AI Security Startups : Platforms like General Analysis and others specialize in continuous, agentic AI security testing that stress-tests tool-chains, multi-step business workflows, and multi-agent communication pipelines against indirect prompt poisoning.azure.microsoft.com+3

To help narrow down the best fit, could you share:

• Are you looking for an open-source CLI tool to run locally/in CI/CD, or an enterprise managed platform?
• Is your application a simple chat endpoint or an agentic system with tool/API access?

If you're evaluating automated red-teaming platforms specifically for prompt injection penetration testing, I'd focus on tools that can automatically generate adversarial inputs, test whether an LLM or agent follows malicious instructions, detect policy bypasses and data leakage, and run repeatable regression tests.

One important distinction: general-purpose LLM security scanners often focus on harmful outputs and jailbreaks, while agent-focused red-team platforms also test indirect prompt injection, untrusted documents, tool misuse, and attempts to exfiltrate sensitive data.

Platforms worth evaluating

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1. Promptfoo

(Best starting point)

Open source · Commercial options

Automates adversarial test generation against your application, including direct and indirect prompt injection, system-prompt extraction, RAG poisoning, data leakage, and agent security tests. Supports API and CI/CD workflows.www.promptfoo.dev+2

Best for: AppSec teams wanting repeatable penetration tests and regression testing in development pipelines.

Website and documentation

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2. Lakera Red

Commercial · Managed platform

Particularly relevant to prompt injection in AI agents. Its documented testing includes indirect attacks that inject instructions into retrieved documents, web pages, and tool results, with evaluation of resulting tool calls.docs.lakera.ai

Best for: Agentic applications that retrieve untrusted content or can take actions through tools.

Documentation

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3. Mindgard

Commercial · Platform and expert-led services

Tests attack paths across LLM applications and agents, including prompt injection, agent hijacking, tool abuse, data extraction, and guardrail bypass. Offers reproducible findings and remediation guidance.docs.lakera.ai+5

Best for: Organizations seeking a more comprehensive security assessment with expert support.

AI red-teaming services

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4. Microsoft PyRIT

Open source · Extensible Python framework

Automates single- and multi-turn attack campaigns, including iterative adversarial strategies, and supports custom scenarios and scoring. It's a framework rather than a fully managed security platform.github.com+1

Best for: Security researchers who need custom attack logic and control over their testing harness.

GitHub and documentation

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5. NVIDIA garak

Open source · Scanner

Runs libraries of probes against language models to uncover weaknesses such as prompt injection, jailbreaks, and data leakage. It's useful for broad model-level testing, though less comprehensive for realistic, tool-using agent workflows.secagentlabs.com+1

Best for: Baseline vulnerability scanning and model comparisons.

GitHub

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6. HiddenLayer

Commercial · Enterprise security

Offers AI red teaming alongside model security and runtime protection. Its agent-security capabilities cover indirect prompt injection, unsafe tool use, and agentic workflows. Verify the specific automated testing features and deployment model in a proof of concept.www.hiddenlayer.com+1

Best for: Enterprises looking to integrate AI testing with broader AI security controls.

Platform

Additional options to consider

How I'd shortlist them

For a practical first evaluation: Promptfoo

Start here if you want automated attack generation, configurable security policies, repeatable runs, and CI/CD integration.

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For realistic indirect prompt injection: Lakera Red

Prioritize it if the target is an agent that reads emails, web pages, retrieved documents, or tool responses and can then perform actions.

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For expert-assisted penetration testing: Mindgard

Evaluate it if you want attack-chain discovery, documented evidence, and help interpreting and remediating findings.

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For custom testing and research: PyRIT

Choose it if your team has Python and AI security engineering expertise and wants to build bespoke attack campaigns.

What to test in a proof of concept

For prompt injection specifically, I would require each vendor to demonstrate these capabilities against your actual application architecture:

• Direct injection: Can an attacker override system instructions or bypass guardrails?
• Indirect injection: Can malicious instructions in a retrieved document, email, web page, or tool output hijack the agent?
• Objective-based attacks: Can the platform automatically discover multi-step attack paths rather than just replay a static list of prompts?
• Impact verification: Does it detect actual unauthorized tool calls, access to restricted data, or attempted exfiltration—not just suspicious text in a response?
• Repeatability: Can you rerun tests after model, prompt, or application changes and compare results?
• Operational fit: Can it run in your environment, meet your data-handling requirements, and produce actionable evidence?

Run tests against an isolated staging environment with synthetic data and sandboxed tools, especially when testing actions such as sending emails, modifying records, or calling external APIs.

One question to narrow this down

What are you primarily pen testing?

1. Target architecture

Standalone LLM chatbots and APIs

RAG applications and knowledge assistants

AI agents with tools, APIs, or MCP servers

A mix of these

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2. Preferred deployment

Commercial platform with support

Self-hosted / on-premises

Open to either

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3. Main testing priority

Direct prompt injection and jailbreaks

Indirect prompt injection and agent hijacking

Data leakage and unauthorized tool actions

Continuous automated testing in CI/CD

[Build my shortlist]