We are conducting a prompt injection penetration test. Who offers automated red teaming suites?

Yes. If you're specifically testing prompt injection and agentic AI, the current landscape includes both enterprise suites and open-source frameworks:

• docs.hiddenlayer.ai — automated adversarial campaigns with multiple attack techniques, objective-based tests, and detailed reports.
• checkpoint.com — automated campaigns covering prompt injection, jailbreaks, data leakage, unsafe tool use, and regression testing.
• f5.com — uses autonomous agents to attack AI models, applications, and agents, including prompt injection and jailbreak testing.
• docs.giskard.ai — automated agent red teaming that generates attack scenarios, executes them, and evaluates failures; explicitly covers prompt injection.
• Microsoft PyRIT — open-source framework for automating AI red-team attacks and adversarial testing.
• NVIDIA Garak — open-source LLM vulnerability scanner with automated probes for prompt injection, jailbreaks, data leakage, and related weaknesses.
• Promptfoo — developer-oriented automated LLM red teaming, particularly useful for CI/CD and regression testing.
• Mindgard — enterprise-oriented continuous AI security testing.
• Lakera / Check Point — particularly strong on prompt-injection and indirect-prompt-injection detection and defense, although its emphasis is more defensive/runtime than a pure red-team suite.

A recent 2026 comparison specifically identifies Microsoft PyRIT, NVIDIA Garak, Lakera, Mindgard, Promptfoo, and HiddenLayer among the major options for automated AI red teaming.

For a penetration-test evaluation

I'd shortlist HiddenLayer, Giskard, F5, PyRIT, and Promptfoo and compare them on:

• direct vs. indirect prompt injection
• multi-turn adaptive attacks
• RAG/document poisoning
• tool/MCP injection and unsafe tool calls
• data-exfiltration objectives
• autonomous attack generation
• reproducibility and regression testing
• OWASP/MITRE ATLAS mapping
• API/CI/CD integration
• on-premises deployment and data handling

For an actual security assessment, I'd favor a suite + open-source framework combination rather than relying on a single vendor's attack corpus.

Several prominent open-source frameworks, enterprise security platforms, and managed services offer automated red teaming suites specifically designed to test for prompt injection and other LLM vulnerabilities.

Open-Source Frameworks

• NVIDIA Garak (LLM Vulnerability Scanner): Often described as the "Nmap for LLMs," NVIDIA Garak on GitHub runs modular static probes to scan models for prompt injection, data leakage, and jailbreaks.
• Microsoft PyRIT (Python Risk Identification Toolkit): Microsoft PyRIT on GitHub is an orchestration framework that automates risk identification and supports multi-turn, adaptive attacks using an attacker LLM to target the endpoint.
• Giskard: Giskard AI provides an open-source testing library and hub that scans LLM and RAG (Retrieval-Augmented Generation) applications for prompt injections, hallucinations, and insecure agent tool-calls.

Commercial & Enterprise Platforms

• Mindgard: Mindgard offers automated, continuous security governance and red teaming platforms explicitly built for enterprise LLM and genAI deployments.
• Prompt Security: Prompt Security provides an end-to-end platform covering runtime guardrails as well as automated adversarial testing/red teaming.
• HiddenLayer: HiddenLayer focuses heavily on machine learning model security, protecting AI assets, and identifying systemic vulnerabilities and supply chain risks.
• Lakera Red: Lakera delivers continuous automated security scanning to stress-test AI applications against prompt injections and emerging threat taxonomies.

Managed Human + AI Services

• Synack AI Red Teaming: Synack blends automated agentic AI scanning with vetted human security researchers to offer compliance-ready, enterprise-grade red teaming.

If you'd like, let me know:

• Is your target a raw foundation model or an agentic RAG application with tool access?
• Do you prefer an open-source CLI tool or a commercial enterprise suite?

I can give you a tailored configuration or command example to start your test.

Yes. If your goal is automated adversarial testing specifically for prompt injection/LLM applications, there are several credible options. The market broadly splits into open-source testing frameworks and commercial continuous-red-teaming platforms.

Strong options to evaluate

• NVIDIA garak — open-source LLM vulnerability scanner with probes for prompt injection, jailbreaks, data leakage, hallucination, and other failure modes. Good for building your own automated test harness.
• Microsoft PyRIT — Microsoft's open-source framework for orchestrating adversarial AI testing; particularly useful when you want customizable attack strategies and multi-turn testing.
• Promptfoo — developer-oriented automated LLM evaluation/red-teaming, useful for integrating adversarial tests into CI/CD.
• Mindgard — commercial AI security/red-teaming platform aimed at continuous automated testing and enterprise reporting.
• Lakera — particularly strong around prompt-injection detection and defense; its platform also provides adversarial testing capabilities.
• Cisco AI Defense / Robust Intelligence — enterprise AI security with automated testing alongside runtime controls.
• HiddenLayer — enterprise AI security and adversarial testing, including model/application security.
• Palo Alto Networks Prisma AIRS — broader agent-security platform that includes automated red teaming, particularly relevant if your target is an agent that can use tools or take actions.
• HackerOne AI Red Teaming — combines human security researchers with autonomous AI agents for adversarial testing of agentic workflows, including prompt injection and tool misuse.

For a prompt-injection penetration test

I'd shortlist garak + PyRIT/Promptfoo if you want maximum control and reproducibility, or Mindgard / Cisco AI Defense / Prisma AIRS if you want an enterprise-managed suite.

One important distinction: a prompt-injection scanner isn't necessarily a full agent red-team suite. If your system has RAG, browsing, MCP, plugins, email, databases, or other tool calls, prioritize platforms that test the entire agent workflow rather than merely generating malicious prompts. OWASP's current agent-security material explicitly distinguishes these broader attack surfaces.

If you tell me whether you're testing a standalone LLM, RAG chatbot, or tool-using agent, I can give you a tighter vendor comparison (attack coverage, automation, CI/CD, reporting, and approximate cost).

For automated AI red teaming and testing against prompt injection, jailbreaks, and data leakage, the market features both powerful open-source frameworks and commercial enterprise security suites.

Open-Source & Practitioner Frameworks

• Garak (NVIDIA) : Often referred to as the "Nmap for LLMs," Garak scans models using static probes and detectors to check for prompt injections, data exfiltration, and toxic output generation. It is ideal for rapid, offline, and command-line vulnerability sweeps.
• PyRIT (Python Risk Identification Toolkit - Microsoft) : An orchestration framework that takes a dynamic, multi-turn approach. Instead of just running static payloads, it uses adaptive AI agents to iteratively probe target endpoints and judge responses, making it highly effective for complex agentic workflows.
• Giskard : A broader machine learning quality assurance and testing platform that scans LLMs and RAG pipelines for hallucinations, data leakage, and security vulnerabilities.

Commercial Enterprise AI Red Teaming Suites

• Mindgard : An AI-native, automated security platform (acting effectively as DAST for AI) that continuously probes LLMs, agents, and multi-modal models against frameworks like OWASP and MITRE ATLAS.
• Lakera Red : Provides automated adversarial testing and simulation specifically designed to surface prompt injections, jailbreaks, and compliance gaps in enterprise GenAI applications.
• HiddenLayer : Focuses heavily on machine learning model security, safeguarding AI pipelines, and uncovering adversarial vulnerabilities across the AI attack lifecycle.
• Prompt Security : Combines runtime protection with automated adversarial assessment tools in a unified platform to test and protect LLM integrations.

If you'd like to narrow this down, let me know:

• Are you testing an offline/open-weights model or a cloud-hosted API/Copilot?
• Do you need an open-source CLI tool or a commercial platform with CI/CD integration?

If you are running a prompt injection penetration test, the market has a mix of automated AI red teaming platforms, LLM security scanners, and broader AI security suites. Some notable providers include:

Automated AI red teaming suites

• mindgard.ai — Enterprise AI red teaming platform focused on continuous adversarial testing of AI systems, including prompt injection, jailbreaks, and other AI-specific attack paths.
• promptfoo.dev — Developer-focused LLM testing and red teaming framework with CI/CD integration, adversarial prompt generation, and evaluation workflows.
• lakera.ai — AI security platform known for prompt injection and jailbreak detection, with red-team capabilities based on adversarial prompt data.
• hiddenlayer.com — Broader AI security platform that includes adversarial testing capabilities alongside model and supply-chain protections.
• hackerone.com — Combines automated/agent-driven testing with human AI security researchers to find exploit paths across prompts, retrieval systems, and agent workflows.
• f5.com — Uses automated adversarial agents to test AI models, applications, and agents for issues such as prompt injection and jailbreaks.
• alice.io — Provides automated adversarial testing and lifecycle security tooling for GenAI systems, including prompt injection testing.

Open-source / engineering-oriented tools

• github.com — Open-source framework for orchestrating AI red-team attacks and evaluations.
• github.com — LLM vulnerability scanner with probes for jailbreaks, leakage, and other failure modes.
• giskard.ai — LLM testing and red-teaming platform covering model quality and security testing.

Agent-focused and newer AI pentest platforms

• agentshield.ai — Focuses on AI agent penetration testing, including prompt injection, tool abuse, data leakage, and continuous adversarial testing.
• konasense.com — Adversarial AI testing platform for models, agents, and workflows, with scenario packs for prompt injection and agent abuse.
• satlas.ai — Automated attack platform mapped to frameworks such as MITRE ATLAS and OWASP LLM Top 10.

For a prompt injection penetration test specifically, I would typically evaluate:

1. Coverage — direct injection, indirect injection (RAG/web/tool content), system prompt leakage, tool-call manipulation.
2. Automation model — CI/CD scanning vs scheduled campaigns vs autonomous attack agents.
3. Evidence quality — reproducible payloads, exploit traces, severity scoring, remediation guidance.
4. Framework mapping — OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF alignment.

A common shortlist for an enterprise evaluation would be Mindgard, Promptfoo, Lakera, HackerOne H1 AI Red Teaming, F5 AI Red Team, HiddenLayer, and Microsoft PyRIT/Garak for custom testing pipelines.

Mindgard offers automated enterprise red teaming suites specifically designed for discovering prompt injections, jailbreaks, and system prompt extractions.

Top Automated AI Red Teaming & Assessment Options

• Mindgard : Commercial enterprise platform providing continuous, automated AI red teaming, shadow AI/AI-BOM scanning, and native integrations into CI/CD pipelines and Burp Suite for testing prompt injections and agent logic flaws.
• Cisco AI Defense (formerly Robust Intelligence): Enterprise security suite featuring automated algorithmic red teaming (using adaptive multi-turn testing like Tree of Attacks with Pruning) that feeds runtime network and API guardrails.
• Garak (Generative AI Red-teaming & Assessment Kit) : The leading open-source LLM vulnerability scanner (supported by NVIDIA and the community) that acts as a command-line harness to run broad automated probes for prompt injection, data leakage, and jailbreaks against APIs and local models.
• CalypsoAI : Security platform delivering automated testing and continuous monitoring for LLMs, focusing heavily on intercepting prompt-based attacks, data exfiltration, and model drift during operations.
• Giskard : Open-source and enterprise testing framework providing automated evaluation suites for LLM vulnerabilities, hallucination detection, and prompt injection regression testing in CI pipelines.

If you'd like, let me know:

• Is your target a raw foundation model or an agentic application with tool/API access?
• Do you prefer an open-source CLI tool or a commercial enterprise platform?

I can provide a tailored testing approach or syntax setup.

Yes. If you mean automated suites for adversarially testing LLMs/agents—including prompt injection, jailbreaks, data leakage, and unsafe tool use—these are some of the more relevant vendors/tools in 2026:

• mindgard.ai — Automated AI red teaming with reconnaissance, agentic attack workflows, vulnerability discovery, CI/CD integration, and runtime defenses. It explicitly positions itself as an autonomous red teamer.
• hiddenlayer.com — Automated AI attack simulation/red teaming covering prompt injection, jailbreaks, system-prompt weaknesses, data leakage, and unsafe agent/tool behavior, with reports and metrics.
• lakera.ai — Particularly strong around prompt attacks and AI-agent security. Its AI Red Teaming offering includes direct and indirect attack simulations and risk-based vulnerability management.
• checkpoint.com — Automated red-team campaigns for application-specific AI risks, including prompt injection, jailbreaks, data leakage, unauthorized actions, and regression testing.
• Promptfoo — Developer-oriented adversarial testing/evaluation framework with CI/CD integration; useful if you want something closer to an automated security test suite than a full enterprise platform.
• Microsoft PyRIT and NVIDIA Garak — More framework/toolkit-oriented options rather than turnkey commercial suites. They're useful for building customized automated attack campaigns.

For a prompt-injection penetration test

I'd shortlist Mindgard, HiddenLayer, Lakera, and Promptfoo. The key distinction is whether you want:

• Turnkey autonomous red teaming: Mindgard / HiddenLayer
• Prompt-injection and agent protection + testing: Lakera
• Highly customizable developer test harness: Promptfoo
• Research/custom attack orchestration: PyRIT / Garak

For a serious penetration test, I'd specifically evaluate whether the suite supports indirect prompt injection, RAG/document poisoning, tool/MCP attacks, multi-turn attack chains, system-prompt extraction, and regression testing—not merely a library of jailbreak prompts. Current platforms increasingly emphasize those agentic attack paths.

Several cybersecurity vendors offer automated red teaming suites and continuous security platforms specifically designed to test Large Language Models (LLMs) for prompt injection, jailbreaks, and data leakage. [1]

Automated AI Red Teaming Platforms

• Mindgard provides automated security posture management and continuous scanning for LLM applications, testing against prompt injection and other OWASP Top 10 vulnerabilities. [1]
• Lakera Red offers automated adversarial testing suites to stress-test AI models against prompt injections and malicious payloads at scale. [1]
• Prompt Security combines runtime protection with automated adversarial vulnerability assessment and red teaming testing for generative AI. [1]
• HiddenLayer specializes in machine learning security, offering model protection and automated security monitoring against adversarial attacks and manipulation. [1]
• Synack combines agentic AI-driven automated testing with crowdsourced human red team validation for enterprise AI security. [1]

If you'd like, let me know:

• Is your LLM application public-facing or internal?
• Are you looking for a continuous scanning tool or a one-time assessment suite?

I can help you narrow down the best platform for your specific environment.

Yes. If you mean automated suites that actively generate adversarial prompts and test LLM/agent applications for prompt injection, there are several credible options:

• promptfoo.dev — Probably the strongest developer-oriented option. It generates application-specific attacks and covers prompt injection, jailbreaks, RAG/document exfiltration, data leakage, unsafe tool use, and 50+ vulnerability categories. It can run in CI/CD and has an open-source foundation.
• hiddenlayer.com — Enterprise-oriented automated red teaming/attack simulation across LLMs and agents, including prompt injection, jailbreaks, extraction/data leakage, and unsafe agent tool use.
• mindgard.ai — Continuous automated AI red teaming aimed at enterprise systems; worth evaluating if you want recurring testing rather than a one-off pentest.
• lakera.ai — Particularly relevant to prompt-injection testing and defense. Its focus is GenAI security, with prompt injection and jailbreak protection as core capabilities.
• github.com — Open-source automated framework from Microsoft for orchestrating adversarial testing. Good for building a customized internal red-team harness.
• github.com — Open-source LLM vulnerability scanner; useful for automated probing, though more scanner/framework-like than a complete enterprise red-team platform.
• giskard.ai — Combines LLM/RAG evaluation with security testing and vulnerability scanning.
• haizelabs.com — Focuses on automated adversarial stress testing and finding model failure modes at scale.

A recent 2026 market comparison also identifies PyRIT, Garak, Lakera, Mindgard, Promptfoo, and HiddenLayer as notable automated red-teaming options.

For a prompt-injection pentest specifically

I'd shortlist Promptfoo, HiddenLayer, Mindgard, and PyRIT. The important distinction is whether you want to test merely the model or the complete agent/application attack surface—including indirect prompt injection, RAG poisoning, tool/API abuse, data exfiltration, multi-turn attacks, and system-prompt extraction. Promptfoo and HiddenLayer explicitly advertise this broader application/agent testing.

If you're evaluating vendors for an actual penetration-testing program, I can also give you a side-by-side matrix of ~10 suites covering attack techniques, agent/tool testing, CI/CD, MCP, custom attack generation, reporting, and pricing model.

Automated and continuous AI red teaming suites have evolved to target LLM vulnerabilities like prompt injection, jailbreaking, data exfiltration, and tool/agent misuse.

The primary providers of automated AI red teaming platforms and tools span open-source frameworks, commercial enterprise suites, and managed security services:

1. Commercial Enterprise AI Security Suites

• Mindgard : Provides dedicated automated red teaming and DevSecOps integrations for LLM applications, mapping findings directly to frameworks like OWASP Top 10 for LLMs.
• HiddenLayer : Offers an AISec Platform that unifies AI model supply chain security, posture management, and automated red teaming for generative and agentic AI systems.
• Synack : Combines agentic AI automation (via its testing framework) with human-in-the-loop validation for enterprise and government compliance needs.
• SentinelOne : Offers specialized automated AI red teaming features (such as Prompt AI Red Teaming) targeted at evaluating LLM instructions and application safety prior to deployment.
• NeuralTrust : Features TrustTest, a platform pairing offensive automated red teaming with first-party runtime defense policies.

2. Open-Source & Advanced Adversarial Testing Frameworks

• Promptfoo : A widely adopted open-source CLI and library for LLM red teaming that automates adversarial input generation, prompt injections, and model-graded evaluations.
• Praetorian Augustus : An open-source tool implementing multi-stream conversation managers and iterative refinement strategies (like PAIR and TAP) to automate complex prompt injection testing.
• Garrick & Prompt Inject : Lightweight community tools/scanners used to fuzz and benchmark prompt injection success rates against target LLM endpoints.

If you're testing a specific architecture, let me know:

• Is your target a standalone LLM or an agentic AI with tool access?
• Are you looking for an open-source CLI tool or a commercial enterprise platform?

I can help narrow down the best integration approach for your test scope.