Top 10 AI Red Teaming Solutions
A shortlist of the best AI Red Teaming platforms, allowing you to stress-test LLMs, GenAI apps, and AI agents with adversarial tactics. These platforms also cover continuous testing and compliance mapping with frameworks like EU AI Act and NIST AI RMF.
Written by
Alex Zawalnyski
Technical Review by
Craig MacAlpine
AI Red Teaming solutions will probe and stress test your AI tools, allowing you to fix security gaps before attackers can exploit them. They are designed to look for opportunities for prompt injection, jailbreaking and data extraction. In order to achieve this, the solutions will carry out red teaming exercises, continuous testing, and compliance mapping (covering key frameworks like EU AI Act and NIST AI RMF).
We have evaluated multiple AI red teaming solutions across a range of categories, allowing us to identify their strengths and the environments that they’d be best suited to. We assessed their attack technique coverage, any CI/CD integration, and how they handle compliance reporting.
What Is AI Red Teaming?
AI Red Teaming simulates attacks and adversary scoping exercises on LLMs, generative AI applications, and autonomous agents to identify vulnerabilities before any attackers do.
AI Red Teaming targets LLMs, generative AI applications, and autonomous agents to probe defenses, identifying vulnerabilities. They test for prompt injection, jailbreaks, data extraction, data poisoning, and tool misuse. This information is then passed on, allowing developers and organizations to address the findings, before an attacker is able to exploit it.
AI Red Teaming Solutions Compared
Here's how the top AI red teaming platforms compare on model, agent, and MCP testing coverage, plus CI/CD and compliance framework mapping.
| Solution | Model-Level Testing | Agent / MCP Testing | Multimodal Testing | CI/CD Integration | Compliance Mapping |
|---|---|---|---|---|---|
|
Adversa AI
|
Yes
|
Yes
|
Yes
|
No
|
No
|
|
Confident AI
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
|
Enkrypt AI
|
Yes
|
Yes
|
Yes
|
No
|
No
|
|
General Analysis
|
Yes
|
Yes
|
No
|
Yes
|
No
|
|
Giskard
|
Yes
|
No
|
No
|
No
|
Yes
|
|
HiddenLayer
|
Yes
|
Yes
|
No
|
No
|
Yes
|
|
Lakera Red
|
Yes
|
No
|
No
|
No
|
Yes
|
|
Mindgard
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
|
Pillar Security
|
Yes
|
Yes
|
No
|
No
|
No
|
|
SPLX
|
Yes
|
Yes
|
No
|
Yes
|
Yes
|
How We Tested
Expert Insights evaluated multiple AI Red Teaming solutions, assessing their range of attack techniques, alongside their effectiveness, and integration with compliance frameworks. This guide was written by Alex Zawalnyski and technically reviewed by Craig MacAlpine. You can read our full methodology. Read our full methodology
Adversa AI
Adversa AI
Best for carrying out human threat modeling with automated attacks
Adversa AI is an Israeli AI security vendor that pairs automated attack simulation and human-led threat modeling. The platform is built around three distinct components: threat modeling, continuous vulnerability audits, and AI-enhanced red teaming. The human research layer is a key differentiator here.
- Adversa AI assesses more than 300 attack techniques over a range of models, applications, agents, and MCP servers.
- Prompt injection, system prompt extraction, data leakage, and jailbreaking are all monitored
- Quick scan runs between 30-60 mins covering 100 attacks, with advanced tier increasing to between 10,000 and 100,000 attacks.
Adversa AI is worth considering if you are in need of an automated testing platform, backed by a proactive team of researchers. This proactive approach ensures that your systems are being tested with up-to-date and relevant threats.
Strengths
Cautions
Confident AI
Confident AI
Best for organizations that want observability to sit at the heart of their solution
Based in San Francisco, Confident AI is a relatively new company that combines automated red teaming with LLM evaluation and product observability. The platform is built on top of DeepTeam, an open-source engine also developed by the company. The cohesion between these two technologies results in an effective and streamlined platform.
- Over 50 vulnerability types and 20 attack vectors covered
- Can run single-turn and multi-turn campaigns, with CVSS monitoring
- Reports are mapped to OWASP Top 10, NIST AI RMF, and EU AI Act
- CI/CD integration addresses issues upstream
Confident AI is a strong option for organizations looking for a comprehensive and integrated red teaming, evaluation, and observability platform, rather than having to work across multiple environments. This means that once detected, a flaw can be addressed directly, rather than being passed on to a new system.
Strengths
Cautions
Enkrypt AI
Enkrypt AI
Best for multimodal and agent attack surface testing
Enkrypt AI is an AI security vendor that has focused on multimodal AI, covering text, image, and audio inputs across the entire agent stack. This includes AI reasoning, tool calls, and real-world actions. The platform’s multimodal and MCP-focused testing is another strength. The platform was acquired by Anaconda in August 2026, meaning that there is some degree of uncertainty over their product roadmap.
- Image and audio prompt testing in addition to standard text-based prompt injection
- AI Red teaming covers tool misuse, privilege escalation, data exfiltration, goal hijacking, and insecure communication
- The platform has scanned 268,000 tools across 25,000 MCP servers, identifying vulnerabilities within 73% of these servers
- Additional Agent Guardrail product adds runtime detection with DLP and SIEM integration
Enkrypt AI’s multimodal and MCP-specific testing makes it a clear standout within the category, where many platforms focus on text-based threats. With attackers looking for any and all opportunity to attack and breach an organization, the fact that Enkrypt AI addresses a wider range of attack methods is a valuable feature.
Strengths
Cautions
General Analysis
General Analysis
Best for multi-step attacks against agents and MCP
General Analysis is a startup, based in San Francisco. The company is focused on developing adaptive red teaming for agentic AI, focusing on multi-step attack risks, rather than static threats. The platform tests agents, RAG pipelines, MCP servers, and coding agents. General Analysis uses adversarial attack algorithms, rather than a static library of pre-written prompts. This is a clear strength as it ensures that you can test systems that are more complex than chatbots.
- Broad range of attack algorithms, including Tree-of-Attacks, Crescendo, PAIR, AutoDAN, and GCG
- Testing covers tool and permission abuse, direct and indirect prompt injection, data exfiltration, and cross-tenant leakage
- Contextual evidence is also supplied, specifying exact prompts, tool calls, and model responses
- Platform integrates with CI/CD pipelines, addressing risks upstream
- Risk scoring helps to triage and prioritize threats
General Analysis is worth considering if your organization has a mature approach to AI agents, requiring testing that goes beyond static jailbreak prompts. The platform tests for adaptive, multi-stage attacks, making the findings more representative of the real world. As the company is a start-up, there is not a wealth of customer reviews and experience stories to understand the specifics of the platform.
Strengths
Cautions
Giskard
Giskard
Best for organizations managing the EU AI Act
Giskard is a French AI company that focuses on AI red teaming and agent evaluation. Giskard Hub, the company’s commercial layer, adds continuous testing, team collaboration, and compliance features to the existing capabilities. The fact that the company is based in Europe, with experience working with the EU AI Act, make the platform a strong solution for organizations looking to operate within these regulations.
- The LLM runs more than 40 adversarial probes, including multi-turn attacks
- RAGET toolkit automatically generates test questions to measure retrieval accuracy and hallucinations within RAG pipelines
- Vulnerability scanning splits findings between security risks (prompt injections and PII disclosure) and quality risks (hallucination and contradictions)
- Giskard also supports testing traditional models for bias and drift
Giskard is a great solution to consider for organizations based in Europe or need to prove compliance with EU AI Act. We would recommend confirming exactly what compliance documentation the Hub tier generates, before assuming that it covers your specific obligations.
Strengths
Cautions
Lakera Red
Lakera
Best for uniting red teaming with runtime guardrails
Lakera Red is an AI adversary testing platform developed by Swiss security vendor Lakera. It’s designed to pair with Lakera’s runtime platform: Guard. This compatibility results in a comprehensive package, allowing you to address the entirety of the AI testing process. It’s worth noting that Check Point acquired Lakera in 2026, so the roadmap for this platform is yet to be publicly unveiled.
- The platform tests for context extraction, instruction override, content injection, service disruption, and indirect poisoning
- The workflow covers application enumeration, targeted attack development, impact amplification, and risk assessment
- Results are mapped to OWASP Top 10 for LLM Apps
- Real attack data is fed into detection models via Gandalf
Lakera Red is worth shortlisting if you’re looking for red teaming that flows directly into a runtime guardrail product from the same provider. There is some degree of uncertainty regarding the platform’s future, due to the recent Check Point acquisition.
Strengths
Cautions
Mindgard
Mindgard
Best for continuous red teaming mapped to compliance frameworks
Mindgard is a UK-based AI security platform that was designed to run automated, continuous red teaming against LLMs, GenAI applications, and AI agents. Its focus on mapping attacks to the MITRE ATLAS and OWASP LLM frameworks make it one of the strongest options out there for teams that want to carry out red teaming, alongside gathering evidence for compliance reporting.
- Attack coverage spans prompt injection, system prompt extraction, jailbreaks, and adversarial ML attacks
- Results are mapped against all 14 MITRE ATLAS tactics
- Mindgard runs through a CLI that integrates directly with GitHub Actions, ensuring that builds automatically fail if a risk is encountered
Mindgard’s platform is built around four lifecycle stages: discovery, mapping, attacks, and defense. This clear and comprehensive strategy, and alignment with EU AI Act and NIST AI RMF, ensures that the platform addresses the risks facing your organization. Mindgard should be on your list of options if your priority is continuous, CI/CD integration and red teaming with comprehensive technique level reporting.
Strengths
Cautions
Pillar Security
Pillar Security
Best for mapping agentic risk
Pillar Security is an Israeli security company focused on securing AI agents via red teaming, runtime guardrails, and governance. The platform’s RedGraph engine is used to map agents, tools, and permissions across an interconnected graph. The platform takes an agent-native approach to red teaming, making it a standout within the category.
- RedGraph maps agents, tools, permissions, and data flows
- Testing covers prompt injection, system prompt extraction, jailbreaking, tool orchestration abuse, and permission exfiltration
- Partnership with Wiz enhances product offering
Pillar Security is a great option if your biggest risk is through AI Agent exposure, rather than standalone chatbots or LLM APIs. The platform has a proven track record identifying indirect prompt-injection flaws in prominent platforms.
Strengths
Cautions
SPLX
SPLX
Best for continuous CI/CD testing
SPLX is a Zscaler company (since an acquisition in November 2025), designed to test, protect, and govern AI models, agents, and MCP servers. The platform addresses risks across the whole lifecycle, from development through to runtime. The CI/CD integration and compliance reporting features are both strong. It is worth understanding the platform’s roadmap, since the recent Zscaler acquisition may drive some changes.
- Runs more than 25 predefined and continually updated attack probes
- Tests against prompt injection, jailbreaks, hallucinations, and social engineering
- Integrates directly with CI/CD pipeline, ensuring pre-deployment testing, with Jira and ServiceNow remediation tracking
- AI Asset Management automatically discovers models, workflows, and MCP servers to build BOM
SPLX’s compliance mapping covers key frameworks including EU AI Act, NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, and MITRE ATLAS. This is a great offering, one of the most comprehensive that we’ve seen in the category. The recent acquisition by Zscaler leaves some degree of uncertainty surrounding the platform’s roadmap.
Strengths
Cautions
AI Red Teaming Pricing Comparison
Most AI red teaming vendors price on a custom, contact-for-quote basis rather than publishing rate cards.
| Product | Starting Price | Billing | Link |
|---|---|---|---|
|
Adversa AI
|
Custom pricing; not publicly available
|
Contact for quote
|
|
|
Confident AI
|
Red teaming available at Enterprise tier; contact for pricing
|
Contact for quote
|
|
|
Enkrypt AI
|
Custom pricing; not publicly available
|
Contact for quote
|
|
|
General Analysis
|
Custom pricing; not publicly available
|
Contact for quote
|
|
|
Giskard
|
Custom pricing; contact for Giskard Hub tier details
|
Contact for quote
|
|
|
HiddenLayer
|
Custom pricing; not publicly available
|
Contact for quote
|
|
|
Lakera Red
|
Free community tier (10,000 API requests/month); paid tiers by quote
|
Contact for quote
|
|
|
Mindgard
|
Custom pricing; not publicly available
|
Contact for quote
|
|
|
Pillar Security
|
Custom pricing; not publicly available
|
Contact for quote
|
|
|
SPLX
|
Custom pricing; not publicly available
|
Contact for quote
|
|
What To Look For In AI Red Teaming
AI Red Teaming platforms vary in their capabilities and offerings. Their attack depth, coverage and compatibility with existing technologies can greatly affect their utility within your environment. To help you decide which features are the most important ones, we've assessed the factors that may influence your decision making.
Look for a platform that goes beyond testing basic prompt injection. The assessment should cover jailbreaks, system prompt extraction, data exfiltration, RAG poisoning, and adversarial examples. This testing should be run as adaptive, multi-stage campaigns, rather than static prompts. These testing strategies should be updated to reflect current attack trends.
AI red teaming that focuses exclusively on the model level will miss any risk that targets the application layer. This includes tool use, retrieval pipelines, and agent permissions. It is essential that you understand what your platform tests, and what it does not. So long as you have all the risks covered, it is okay to have different platforms targeting different areas.
With the EU AI Act and NIST AI RMF both being enforced now, it's increasingly important that the platform's findings can be mapped cleanly to specific frameworks, rather than a generic vulnerability list.
Continuous testing only works if it fits into how your team already ships software. Look for native CI/CD hooks, API access, and the ability to gate a release on red team results.
Red teaming is great for identifying vulnerabilities, but it is not designed to fix them. You need to pair your red teaming platform with a runtime guardrail product. If you don't get this integration right, you won't be able to act on the information that your platform has found.
This is a market that is consolidating quickly, thanks to the rapid growth in AI capabilities. There are countless acquisitions and mergers between the companies listed even in this article. An acquisition should not be taken as a negative, nor as an indicator of certainty, but should be taken into consideration when selecting your platform.
Look for platforms that generate clear remediation guidance alongside their findings. While it's important to know where the issue is, it's only useful if you can do something to address it. This is an area where smaller, newer platforms sometimes lag behind more established players.
How We Compared The Best AI Red Teaming Solutions
We assessed multiple AI red teaming platforms, judging them based on the breadth of attack technique that each one runs (prompt injection, jailbreaks, data extraction, RAG poisoning, and agent tool misuse). We assessed whether this coverage extends beyond single models to full GenAI applications, autonomous agents, and MCP-connected workflows. We also considered how each platform fits with existing workflows. This included assessing integration with CI/CD, as well as gauging how they map findings with frameworks like EU AI Act, NIST AI RMF, and OWASP LLM Top 10.
We read the product documentation, completed technical walkthroughs, and sat in on vendor demos to understand how each platform actually works. We wanted to be sure that the advertised features are effective in the real world.
We also considered verified customer reviews in our assessment of each platform. This helps us to understand common issues or weaknesses within a platform, as well as the features that users praise. It might be that users find there is a steep learning curve, difficulty with integration, or a gap between what a platform detects and the level of assistance it can give when resolving the issue. If these factors are on the minds of verified users, then they ought to be on yours.
We cross-check vendor claims, including compliance mapping and attack coverage figures, with publicly available documentation and any relevant third-party research. In any cases where we could not independently verify a claim, we have flagged this, rather than repeating them as a fact.
Expert Insights’ editorial team operates independently of our commercial team. This means that no vendor can pay to influence the testing, review, or ranking of their product. Our recommendations are based on real testing, verified customer feedback, and independent research.
The Bottom Line
Selecting the right red teaming platform depends on identifying your biggest exposure, whether that is model-level jailbreaks, agent tool misuse, or compliance reporting against a specific framework. Once you have assessed your own organization and know what the risks are, test this scenario with your shortlisted vendors before committing.
As the market is consolidating quickly, speak to each vendor to understand the product roadmap, and whether there are any acquisitions coming down the line. While this may seem like unnecessary effort at this stage, it will save you having to restart this process several months down the line.
For more guidance on evaluating AI security risk more broadly, read our AI Security Risks: What CISOs Need To Know In 2026.
Frequently Asked Questions
AI red teaming is the practice of simulating adversarial attacks against LLMs, GenAI applications, and AI agents to find vulnerabilities before attackers do. It assesses techniques like prompt injection, jailbreaks, data extraction, and RAG poisoning. This can typically be carried out as one-off assessments prior to launch, or continuously against live systems.
Traditional pentesting targets infrastructure, networks, and application code for known vulnerability classes. AI red teaming targets the behavior of a model or agent itself, assessing if it can be manipulated through natural language, adversarial inputs, or tool access. Attackers may wish to do this to produce harmful outputs, leak sensitive data, or take unauthorized actions. There is an increasing overlap between pentesting and AI red teaming, with the latter focusing on threats targeting the unique way that LLMs and agents process instructions.
AI red teaming does help with compliance. The EU AI Act requires that high-risk systems are tested for risks, with these being properly documented. Any findings need to be mapped across the framework’s risk categories. This is also true for NIST AI RMF and OWASP LLM Top 10. It’s worth confirming directly with each vendor how detailed that mapping is, and if it’s compatible with the frameworks you need it to be compatible with.
AI red teaming is able to identify vulnerabilities before or during deployment. It does this by attacking a system as an adversary would. Guardrails are runtime controls that filter inputs and outputs to block attacks in production. The two techniques are complementary, rather than one being a replacement for the other.
Yes, and this is now a core requirement for most buyers, rather than a niche feature. The market now holds platforms that are built specifically to test agent tool use, permission boundaries, and MCP-connected tool chains, in addition to the conversational testing that covers chatbots and standalone LLM applications.
Point-in-time assessments before launch are not enough to be sure that a system is safe. Continuous testing is a much more effective way of ensuring that capabilities and features cannot be abused.
AI Security Resources
Further reading on ai security from Expert Insights — buyers' guides, comparison articles, and platform-specific shortlists.
Written By
Alex is an experienced journalist and content editor, working alongside software experts to research, write, meticulously factcheck, and edit articles relating to B2B cybersecurity and technology solutions, focusing on topics such as DevSecOps, network security and firewalls, and cloud infrastructure security.
Technical Review
Craig MacAlpine is CEO and Founder of Expert Insights. Before founding Expert Insights in August 2018, Craig spent 10 years as CEO of EPA Cloud, an email security provider that rebranded as VIPRE Email Security following its acquisition by Ziff Davis, formerly J2Global (NASDAQ: ZD) in 2013.
Craig is a passionate security innovator with over 20 years of experience helping organizations to stay secure with cutting-edge information security and cybersecurity solutions.
Using his extensive experience in the email security industry, he founded Expert Insights with the singular goal of helping IT professionals and CISOs to cut through the noise and find the right cybersecurity solutions they need to protect their organizations.