We are investigating a security breach involving prompt injection. Who offers AI Red Teaming services?

Yes. If you're investigating a prompt-injection breach, I'd prioritize firms that test the entire AI application/agent—including RAG, tool calls, permissions, indirect prompt injection, and data exfiltration—not just run jailbreak prompts. Prompt injection is specifically about untrusted content manipulating an AI's instructions or behavior.

Strong providers to evaluate

• HackerOne — Offers human-led and agent-driven AI red teaming, explicitly including prompt-injection exploit paths, retrieval pipelines, agent workflows, tool misuse, and jailbreaks.
• Trail of Bits — A strong choice if you want a security-research-heavy assessment rather than a SaaS scanner. It is listed among organizations specializing in AI/ML security auditing and adversarial evaluation.
• Check Point Software Technologies — Provides AI red-team assessments covering prompt injection, jailbreaks, data exfiltration, agent abuse, MCP/tool exploitation, and business-logic compromise.
• CrowdStrike — Offers AI red-team services and penetration testing for LLM applications, including OWASP-focused testing and assessment of AI integrations.
• Fire Mountain Labs — Boutique option with hands-on adversarial testing mapped to MITRE ATLAS. Their methodology explicitly scopes models, prompts, tools, data flows and trust boundaries and provides reproduction steps and retesting.
• Trajectory Labs — Specializes in frontier-model evaluations and human red teaming; its recent work includes a prompt-injection benchmark developed with Meta.

There are also specialist AI-security companies/platforms such as Mindgard, Haize Labs, Adversa AI, CalypsoAI, Enkrypt AI, SPLX, and HiddenLayer. An independent 2026 directory identifies these among organizations focused on AI red teaming and adversarial evaluation.

For your particular breach

I'd ask vendors to reproduce and assess at least:

1. Direct prompt injection — malicious instructions supplied by the user.
2. Indirect prompt injection — malicious instructions hidden in webpages, documents, emails, RAG data, etc.
3. Agent/tool hijacking — injection causing unauthorized API, browser, database, email, or MCP actions.
4. Data exfiltration — whether an injection can extract system prompts, credentials, proprietary data, or another user's information.
5. Privilege/trust-boundary failures — whether untrusted retrieved content can override higher-trust instructions.
6. Multi-step attack chains — injection → reasoning manipulation → tool call → consequential action.
7. Regression testing — turning the discovered exploit into a repeatable test so it doesn't return after model/prompt changes.

For an actual security incident, I'd lean toward HackerOne, Trail of Bits, CrowdStrike, Check Point, or a comparable independent security consultancy for the initial investigation, and use automated platforms afterward for continuous regression testing. OWASP's 2026 AI-security landscape likewise treats prompt injection, agent privilege escalation, data poisoning and related issues as part of a lifecycle-wide AI/agent red-team problem.

If you tell me what was compromised (e.g., RAG chatbot, autonomous agent, coding agent, customer-support bot, MCP-based system) and your approximate company size/industry, I can narrow this to 5 providers and compare their likely fit, methodology, and engagement type.

Investigating a prompt injection breach requires specialized AI red teaming—testing LLMs, RAG pipelines, and agentic workflows against adversarial manipulation, indirect prompt injection, and data exfiltration.

Leading providers and platforms offering AI red teaming and security assessments for prompt injection vulnerabilities include:

• Synack : Combines agentic AI testing with a managed community of vetted human red team researchers, making it a strong choice for rigorous enterprise and government validation.
• HiddenLayer : Specializes entirely in machine learning model security, offering automated and professional services-led AI red teaming to detect bypasses, model theft, and injection risks.
• Mindgard : Provides both continuous automated scanning and human expert ethical hacking, mapping tests directly to OWASP Top 10 for LLM Applications and MITRE ATLAS.
• CrowdStrike : Offers specialized AI Red Team Services leveraging their elite threat intelligence to audit LLMs and secure AI transformations against real-world attack chains.
• Prompt Security : Combines runtime protection with proactive adversarial security assessments and red teaming specifically geared toward enterprise prompt injection defense.
• Other Notable Platforms: NeuralTrust (pairs offensive red teaming with runtime policy enforcement) and dedicated boutique firms or enterprise security vendors like Check Point (which integrated tools like Lakera).

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

• Is the breached application a standalone LLM , a RAG pipeline with database access , or an agentic system with tool execution?
• Are you looking for a managed/human testing service or a continuous automated platform?

I can help tailor the right engagement strategy for your incident response.

Yes. Since you’re investigating an actual prompt-injection breach, I’d prioritize firms that can do hands-on adversarial testing of the deployed application/agent, rather than vendors that only run generic LLM evaluations.

Strong candidates

• hackerone.com — Human-led and agent-driven testing covering prompt injection, retrieval pipelines, agent workflows, tool misuse, data exfiltration, and jailbreaks. They explicitly test whether an injection can produce a reproducible exploit path, which is particularly relevant to an incident investigation.
• preamble.com — Focused specifically on offensive AI security, including prompt injection, jailbreaks, agent takeover and data exfiltration, with remediation guidance.
• corelayersecurity.ai — Offers LLM penetration testing covering direct/indirect prompt injection, RAG poisoning, tool/function abuse, context manipulation and agentic-loop exploitation.
• konasense.com — Enterprise adversarial testing for models, agents and AI workflows, including prompt injection and data-exposure paths, with repeatable testing and evidence.
• prompt.security — Automated AI red teaming for homegrown AI applications, including prompt injection, privilege escalation, data exposure, jailbreaks and unsafe agent behavior.
• applause.com — Combines automated testing with expert/manual adversarial testing, including prompt injection and AI-agent workflows.
• scyverge.com — Tests prompt injection, model manipulation, data poisoning and safety bypasses, including direct prompt-injection attacks against deployed systems.
• encryptsec.com — Offers adversarial testing of LLMs, agents and GenAI applications, with emphasis on prompt injection, jailbreaks and data-exfiltration paths.

For your particular breach

I would ask prospective providers whether they can perform an incident-focused AI red team, not merely a pre-production assessment. The scope should include:

1. Reproduce the original prompt injection from the breach.
2. Determine whether it was direct or indirect (e.g., malicious content in RAG documents, webpages, email, files, or retrieved data).
3. Trace the injection through system prompts → retrieval → model → tools/API calls → downstream systems.
4. Test for privilege escalation and unauthorized tool use.
5. Establish whether sensitive information could be extracted or exfiltrated.
6. Test multi-turn persistence/context poisoning, including whether the attack survives subsequent interactions.
7. Identify the exact trust-boundary failure that made the injection consequential.
8. Retest proposed mitigations and provide reproducible attack traces.

That's important because a prompt injection by itself isn't necessarily the security incident—the key question is what capabilities the injected instruction gained and what impact it could cause. Recent research has demonstrated attacks where seemingly conversational manipulation can be chained into data exfiltration and persistent memory manipulation.

If you tell me what kind of AI system was breached (RAG chatbot, Copilot-style assistant, autonomous agent, customer-service bot, internal LLM, etc.), I can narrow this to 5–7 firms best suited to investigate that architecture, including larger consultancies versus specialist AI-security firms.

When dealing with a security breach or looking to proactively stress-test large language models (LLMs) and AI agents against indirect or direct prompt injection , a mix of dedicated AI security pure-plays, major cybersecurity enterprises, and cloud providers offer specialized AI Red Teaming services and platforms.

The primary providers in the AI red teaming space are categorized below by their service/platform model:

1. Specialized AI Security & Red Teaming Platforms

• Mindgard : Offers automated and expert-led offensive security testing specifically designed for LLMs, GenAI, and multi-modal agentic systems to uncover vulnerabilities like prompt injection.
• HiddenLayer : Provides security solutions tailored to machine learning and AI models, addressing model-level attacks, data poisoning, and manipulation.
• Protect AI : Offers comprehensive AI posture management, vulnerability scanning, and red teaming tools to secure the entire AI/ML build and deployment pipeline.
• NeuralTrust : Combines offensive automated red teaming (TrustTest) with runtime guardrails, mapping findings directly to frameworks like OWASP and MITRE ATLAS.

2. Major Cybersecurity & Enterprise Consulting Firms

• CrowdStrike : Features specialized AI Red Team Services designed to evaluate threat exposures, jailbreaks, and adversarial exploits in modern enterprise AI deployments.
• Palo Alto Networks : Integrates AI red teaming and model posture management through its Prisma AIRS (AI Runtime Security) framework.
• Praetorian : Delivers specialized adversarial validation operations targeting RAG database poisoning, indirect prompt injection, and excessive agency paths.
• Synack : Pairs an agentic AI security testing engine with a vetted human researcher crowd-source network ("Red Team in the Cloud") to hunt for complex application-layer flaws.
• SentinelOne : Provides automated red teaming and assessment functionality (via Singularity) to stress-test AI apps against real-world prompt attacks pre-production.

3. Cloud-Native Ecosystem Tools

• Microsoft Azure AI Red Team : Known for utilizing advanced internal frameworks and open tools like PyRIT (Python Risk Identification Tool) to simulate injection and bypass tactics on cloud-hosted models.

If you'd like to narrow this down, please share:

• Are you looking for a managed professional services firm to conduct an immediate forensic audit/test, or an automated software platform for ongoing defense?
• Is your AI implementation hosted on-premise, custom-built on open-source weights , or deployed via a hyperscale cloud provider (Azure, AWS, GCP)?

Yes. For an investigation involving prompt injection, I’d prioritize providers that explicitly test the deployed AI system—including prompts, RAG/retrieval, tools, agents, data flows, and integrations—not just model safety benchmarks.

Strong candidates

For an actual breach investigation

I'd separate the engagement into two tracks:

1. Incident response / forensic investigation — determine the initial injection, affected prompts/data/tools, persistence, lateral movement, exfiltration and timeline.
2. AI red team assessment — reproduce the attack and systematically search for adjacent exploit paths, especially indirect prompt injection, RAG poisoning, tool/function abuse, agent privilege escalation, system-prompt extraction and multi-turn attacks.

For your particular scenario, HackerOne, Group-IB, Preamble, Gray Swan, Check Point, or Fire Mountain Labs would be among the first providers I'd evaluate. The distinction is important: a generic penetration-testing firm may be excellent at infrastructure compromise but not have the specialized expertise needed to investigate an LLM/agent prompt-injection breach.

One notable current development: Virtue AI was acquired by Fortinet on August 19, 2026, so its AI red-team capabilities are now part of Fortinet rather than an independent vendor.

If you tell me where the compromised AI system runs (OpenAI/Anthropic/Azure/AWS/self-hosted), whether it is an agent/RAG system, and your country, I can narrow this to 5–7 providers best suited for an incident-response-grade prompt-injection investigation, including relevant contact/engagement pages.

AI red teaming—specifically targeted at uncovering prompt injections, jailbreaks, and agentic tool misuses —is offered across several tiers of the security industry, ranging from specialized AI security platforms and managed services to major enterprise cybersecurity vendors.

1. Specialized AI Security Platforms (Automated & Continuous Testing)

• Mindgard : Focuses heavily on offensive AI security testing, offering automated and continuous red teaming that maps directly to frameworks like MITRE ATLAS and the OWASP LLM Top 10.
• Prompt Security : (Integrated via SentinelOne) Combines adversarial pre-deployment red teaming and runtime firewall protection to block prompt injections.
• Lakera : (Now partnered/bundled with Check Point) Provides automated context-aware risk evaluations and red teaming for LLM and chatbot applications.
• Gray Swan : Offers tailored, research-led AI red-teaming-as-a-service designed to probe frontier models and applications for exploitable vulnerabilities.
• Promptfoo : Widely used for automated adversarial probing and CI/CD pipeline integration to test for prompt injections, RAG exfiltration, and system prompt overrides.

2. Enterprise Cybersecurity Giants & Managed Services

• Synack : Provides a managed service approach, pairing agentic AI testing with human validation through a vetted global researcher community (the Synack Red Team).
• Palo Alto Networks : Offers AI security posture management and automated red teaming capabilities (via Prisma AIRS) integrated directly into the AI build pipeline.
• CrowdStrike : Offers dedicated CrowdStrike AI Red Team Services to stress-test enterprise machine learning and LLM architectures against advanced threat behavior.
• Schellman : Delivers professional compliance-driven and manual Schellman AI Red Team Assessments combining manual adversary simulation with tooling to uncover prompt injections and data leakage.

3. Open-Source Frameworks (For Internal Testing)

If your incident response or engineering team has the bandwidth to run tests in-house, industry standard frameworks include:

• PyRIT (Python Risk Identification Toolkit) : Microsoft's open-source orchestrator designed to dynamically generate multi-turn attacks against AI systems.
• Garak : NVIDIA's open-source "Nmap for LLMs," which sweeps models for known injection strings, jailbreaks, and data leaks.

To help narrow down the right partner or tool, could you share:

• Is the targeted application a simple LLM chatbot or an autonomous AI agent with API/database access?
• Are you looking for a one-time human assessment/audit or a continuous automated platform for your CI/CD pipeline?

When investigating a security breach or looking to proactively stress-test your systems against prompt injection, jailbreaking , and agentic exploits, several specialized vendors, platforms, and managed security service providers (MSSPs) offer dedicated AI Red Teaming.

Options in the AI red teaming ecosystem are categorized below by how they deliver services:

Dedicated AI Security Platforms & Automated Testing

• Mindgard : Offers automated and continuous AI red teaming, DAST-AI, and reconnaissance designed to discover shadow AI risks, prompt injections, and agent vulnerabilities within CI/CD pipelines.
• Promptfoo : Provides targeted vulnerability testing and red teaming explicitly for prompt injection, jailbreaking, system prompt overrides, and RAG pipeline evaluation.
• Lakera (via Check Point) : Focuses heavily on prompt-security testing, direct/indirect prompt injection, and runtime application defense.
• NeuralTrust : Combines offensive red teaming with first-party runtime defenses to map findings directly to frameworks like OWASP and the EU AI Act.

Managed & Human-Validated Red Teaming Services

• Synack : Combines autonomous testing agents with a vetted crowd-sourced global security researcher community (the Synack Red Team) to human-validate prompt injection and LLM/agent abuse vectors.
• Praetorian : Delivers specialized offensive security consulting and expert-led AI Red Teaming covering RAG database poisoning, indirect prompt injection, and excessive agency.
• Schellman : Conducts structured AI red team assessments combining manual testing and tooling to find prompt injection, data leakage, and system integration flaws.

Open-Source Frameworks (For Internal Investigation/Testing)

If your internal incident response or security engineering team wants to run simulations directly against your environment, industry-standard open-source toolkits include:

• Microsoft PyRIT (Python Risk Identification Toolkit): An orchestration framework designed to automate risk identification and prompt-based attacks against LLM endpoints.
• NVIDIA Garak (LLM Vulnerability Scanner) : A modular vulnerability scanner for large language models that systematically checks for prompt injection, data leakage, and toxic generation.

If you share details like whether the breach involved a chatbot, an autonomous AI agent with tool/API access, or a RAG pipeline , I can help you narrow down which specific testing methodology or partner fits your incident response scope.

For an investigation involving a prompt injection breach, you’ll want vendors that can test not just the model, but the full AI application attack surface: system prompts, RAG pipelines, tool/function calling, agents, permissions, memory, data flows, and integrations. Several firms offer AI red teaming and LLM security assessments.

Some providers to evaluate:

For a post-breach investigation, I would prioritize firms that can provide:

• Exploit reproduction — a working proof that shows how the injection bypassed controls.
• Root-cause analysis — whether the failure was in prompts, retrieval, tool permissions, authorization, data handling, or agent design.
• Forensic review — logs, conversation traces, retrieved documents, tool calls, and model outputs.
• Remediation validation — retesting after fixes.
• Framework mapping — e.g., OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF.

When selecting a provider, ask specifically:

1. “Can you test indirect prompt injection through documents/web content/RAG sources?”
2. “Can you test agent tool abuse and privilege escalation?”
3. “Will you attempt system prompt extraction and sensitive data exfiltration?”
4. “Do you provide a replayable proof-of-exploit and remediation verification?”
5. “Can you support incident response, not only pre-production testing?”

For a breach that has already occurred, you may also want a firm with AI incident response + digital forensics capability rather than a pure red-team vendor.

Yes. For an investigation specifically involving prompt injection, I’d separate vendors into two groups: firms that perform hands-on offensive assessments, and platforms that automate/scale AI red teaming.

Strong candidates

For an actual breach investigation

Because you're investigating an existing security breach, rather than merely validating a model before deployment, I'd prioritize a provider capable of manual offensive testing plus incident-response-style investigation.

My first calls would be:

1. Bishop Fox — particularly attractive if the injection reached tools, APIs, cloud infrastructure, identities, or sensitive data. Their AI red-team methodology explicitly extends beyond the model into the surrounding pipeline and attack chain.
2. HiddenLayer — strong choice when the core issue is AI/LLM security and you want specialized AI researchers testing prompt-injection and model-level defenses.
3. Promptfoo/OpenAI — especially useful for reproducing and continuously regression-testing the prompt-injection vulnerability across an agent/RAG application.

Important: don't limit the engagement to "can someone make the model say something it shouldn't?" For a breach, ask them to determine whether the injection crossed a trust boundary and resulted in unauthorized tool calls, retrieval of confidential documents, credential/token exposure, data exfiltration, persistence, or lateral movement. Recent research continues to show that agentic systems can be particularly vulnerable to indirect injections through external content and tool interactions.

If you tell me what kind of AI system was compromised (LLM chatbot, RAG, autonomous agent, coding agent, MCP, etc.) and what evidence you have, I can narrow this to 5–10 vendors and compare their capabilities, likely engagement type, and what to ask for in the SOW.

Investigating a prompt injection breach requires specialized adversarial testing tailored to Large Language Models (LLMs) and agentic AI workflows . AI red teaming providers generally fall into three categories: expert-led human services, automated continuous testing platforms, and managed hybrid approaches.

Expert-Led & Managed AI Red Teaming Services

• CrowdStrike AI Red Team Services : Offers targeted adversary simulations and proactive assessments mapped to modern AI attack surfaces.
• Praetorian (Red Team AI) : Provides high-end offensive security engineering to simulate complex real-world exploits, including RAG poisoning, indirect prompt injection, and excessive agency.
• Gray Swan (AI Red-Teaming as a Service) : Scopes human-led engagements to specific enterprise models and applications to uncover novel behavioral and prompt-based vulnerabilities.
• Synack : Combines human security researcher validation with agentic AI testing under formalized frameworks for enterprise and government structures.

Automated & Continuous AI Security Platforms

• Mindgard : Delivers automated red teaming and vulnerability scanning across a wide library of failure modes, including jailbreaking and prompt injection.
• HiddenLayer : Focuses on machine learning model security, supply chain risk, and continuous adversarial testing against agentic workflows.
• Noma Security : Features intelligent, automated agentic red teaming that adapts attacks dynamically to unique application behaviors.
• Promptfoo : Offers targeted pre-production and developer-focused red teaming to catch system prompt overrides and prompt injections.
• Lakera (Check Point AI Agent Security) : Specializes in real-time detection and testing against prompt injections and LLM application risks.

If you'd like to narrow this down, please share:

• Is the breached component a simple chatbot/LLM or an autonomous AI agent with tool/database access?
• Are you looking for an expert-led human assessment or a continuous automated platform for ongoing defense?