Adversarial AI testing services

Absolutely. For your use case, I’d look for AI security firms that perform adversarial assessments of both the models and the surrounding ML infrastructure, rather than relying on standard penetration testing alone.

A strong assessment should attempt to make your systems fail under realistic attacker behavior, demonstrate reproducible vulnerabilities, measure their impact, and provide actionable remediation guidance.

1. Services worth evaluating

I've found several providers that explicitly advertise LLM red teaming, AI penetration testing, or adversarial ML security assessments. These are good candidates for an initial shortlist; the descriptions below reflect their published service offerings, not an independent validation of their effectiveness.

Select a provider's name to visit its website.

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Evaluris Solutions

(Broad ML + LLM coverage)

Advertises adversarial assessments across LLM applications, AI agents, model security and ML pipelines. Its stated coverage includes training-data poisoning, model extraction, RAG manipulation, supply-chain risks and pipeline integrity.

Best fit: a combined assessment of model, data and deployment risks.

Visit Evaluris AI security ·evaluris.com

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Complex Technologies

New Jersey-based cybersecurity consultancy advertising AI/LLM security testing, including prompt injection, jailbreaks, training-data poisoning and model inference attacks.

Best fit: a regional provider to discuss hands-on testing of your LLM applications.

Visit ComplexTech ·www.google.com

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Astra Security

Advertises human-led AI/LLM penetration testing and ongoing testing, including prompt injection, RAG poisoning, agent abuse, model extraction and AI application vulnerabilities.

Best fit: application-level AI testing combined with ongoing security validation.

Visit Astra Security ·www.getastra.com

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Bishop Fox

A candidate for organizations that want AI testing as part of a broader adversary simulation, alongside application, cloud and infrastructure security testing.

Best fit: enterprise red teaming and testing AI within a larger technology estate.

Visit Bishop Fox ·www.getastra.com

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RedSecLabs

Advertises human-led AI and LLM penetration testing, covering direct and indirect prompt injection, sensitive-data exposure, RAG vulnerabilities, agent tool integrations and supporting application infrastructure.

Best fit: a focused, human-led security assessment with remediation and retesting.

Visit RedSecLabs ·www.redseclabs.com+1

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NuSummit Cybersecurity

Jersey City-based cybersecurity provider advertising AI/LLM penetration testing, threat modeling and red-team services.

Best fit: a regional provider to evaluate for broader security assurance alongside AI testing.

Phone:

www.google.com

2. Which provider best matches your pipeline?

My initial recommendation is to shortlist Evaluris and Bishop Fox for broad, end-to-end testing, and ComplexTech if you prefer to explore a New Jersey-based consultancy.

For ML pipelines, be particularly careful to distinguish adversarial model robustness testing from pipeline security testing. They overlap, but are not interchangeable.

3. What should you ask vendors to test?

LLMs and AI agents

Direct and indirect prompt injection, jailbreaks, system-prompt leakage, sensitive-data exfiltration, tool misuse, and authorization boundary failures.

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Data and retrieval pipelines

Training-data poisoning, malicious dataset ingestion, RAG index poisoning, cross-tenant retrieval leaks, and data provenance issues.

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ML infrastructure and supply chain

Model registry permissions, artifact tampering, insecure serialization, CI/CD and MLOps access controls, dependency risks, and deployment credential exposure.

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Adversarial ML robustness

Evasion attacks, membership inference, model extraction, model inversion, and poisoning attacks, as relevant to your model type and threat model.

Ask vendors to map the assessment to MITRE ATLAS, the OWASP Top 10 for LLM Applications, and the OWASP Machine Learning Security Top 10. The relevant framework depends on your architecture and threat model.

Also require reproducible proof of concept, severity and business-impact ratings, remediation recommendations, and a retest. OWASP's 2026 vendor evaluation criteria provide a useful starting point for comparing proposals.genai.owasp.org

4. Let’s narrow the search to the right providers

The best shortlist depends on whether you need a one-time independent assessment, ongoing adversarial testing, or a specialist engagement focused on ML internals.

What do you want assessed?

LLMs and AI agents

ML models and training pipelines

Both LLMs and ML pipelines

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Preferred assessment model

Independent, human-led assessment

Automated, continuous adversarial testing

Both consulting and continuous testing

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Approximate budget

Under $10,000

$10,000–$50,000

$50,000+

Not sure yet

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Where should the provider be based?

New Jersey or nearby Northeast US

US-based or remote

Anywhere globally

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Where do your pipelines run?

Cloud-hosted (AWS, Azure, GCP or managed AI services)

Self-hosted or on-premises

Hybrid or multiple environments

[Find matching providers]

Once those requirements are clear, I can narrow this down to a more targeted vendor comparison with relevant service links, published pricing where available, and questions to use when requesting proposals.

Yes. For an ongoing independent red-teaming + AI risk-assessment partner, I’d prioritize firms that combine adversarial testing with repeatable evaluation and monitoring. NIST’s current ARIA guidance is also a useful benchmark: it recommends combining model testing, red teaming, and user testing rather than treating red teaming as the entire assurance program.www.nist.gov

Strongest candidates

1. NeuralTrust — my first choice for continuous testing.
Its TrustTest offering explicitly covers hallucination, bias/fairness, jailbreaks, prompt injection, misuse and agent/tool abuse. More importantly for your requirement, it can run tests on every change, through CI/CD, or on a scheduled basis, with run-over-run comparison and traceability.neuraltrust.ai+1

2. Bulsoft — strongest fit if you want an independent assurance program.
They describe a recurring evaluation model covering accuracy/robustness, adversarial red teaming, hallucination and bias, RAG evaluation, and model monitoring. Their deliverables include a reproducible evaluation harness, written evidence, CI regression suite and prioritized remediation backlog.www.bulsoft.com+1

3. Natton.ai — attractive for large-scale, multilingual programs.
They advertise independent model evaluation, adversarial red teaming, bias detection, hallucination/factuality scoring and drift monitoring, including monthly probe-set refreshes. They also claim coverage across 60+ languages and multiple AI-governance frameworks.natton.ai+1

4. Omnus AI Lab — good if you want a research-oriented independent assessment.
Their methodology includes threat modeling, held-out evaluation sets, manual and automated adversarial testing, bias dimensions, reliability metrics and weekly/monthly drift testing. They also provide reproducible test cases and a rerun protocol.omnusailab.com+1

5. Katalyst AI Lab — particularly aligned with your exact failure modes.
Its published service scope explicitly includes bias/fairness audits, hallucination testing, accuracy, instruction-following and regression testing, plus human red teaming. It also says it can monitor these risks as models change over time.katalysttech.com+1

What I'd put in the RFP

I wouldn't simply ask vendors for "AI red teaming." I'd require a continuous AI assurance program with:

• Bias/fairness: demographic and intersectional slices, disparate-error analysis, false-positive/false-negative disparities.
• Hallucination: factuality, groundedness, citation accuracy, domain-specific knowledge, RAG failures.
• Reliability: repeatability, robustness to paraphrasing, instruction following, refusal calibration, regression and drift.
• Adversarial testing: jailbreaks, prompt injection, indirect injection, data leakage, unsafe tool use and multi-turn attacks.
• Human evaluation: expert reviewers plus calibrated scoring rubrics—not automated LLM judges alone.
• Continuous cadence: pre-release assessment plus weekly/monthly automated probes and independent quarterly deep dives.
• Reproducibility: every finding should include the test case, model/version, prompt, output, severity and remediation recommendation.
• Release gates: define thresholds that can block a model release when risk increases.
• Independent reporting: executive risk summary plus detailed technical findings your engineering team can reproduce.

That distinction matters: OpenAI's own red-teaming guidance describes red teaming as complementary to ordinary evals, with evals measuring intended behavior and red teaming probing adversarial/unexpected behavior.neuraltrust.ai+3

My shortlist for an RFP would therefore be NeuralTrust, Bulsoft, Natton.ai, Omnus AI Lab, and Katalyst AI Lab, with the first three getting the most attention for a genuinely ongoing program.

If you tell me your industry, approximate model volume, whether these are LLMs/agents/RAG systems, and geography, I can turn this into a 10–15 vendor shortlist with pricing/engagement model, customer evidence, certifications, strengths/weaknesses, and a weighted RFP scorecard.

You're looking for what's often called continuous AI assurance or AI red teaming and model risk assessment: an independent company that repeatedly tests your AI system for bias, hallucinations, unsafe behavior, and reliability problems—not just a one-time audit.

There are several providers worth evaluating. The key distinction is that some specialize in adversarial security testing, while others focus on factual accuracy, fairness, ongoing monitoring, or broader AI governance. For your needs, I'd prioritize companies that can combine these capabilities into a recurring assessment program.expertinsights.com+2

Companies to put on your shortlist

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1. Mindgard

(Best fit for continuous red teaming)

Mindgard offers automated and expert-led adversarial testing of AI applications, agents, models, and connected tools. It looks for prompt injection, guardrail bypasses, data leakage, and vulnerabilities introduced by system changes. Its services include actionable findings and remediation guidance.mindgard.ai+1

Best for: Organizations that want recurring, attacker-style testing as their AI evolves.

Website:mindgard.ai

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2. Appen

Appen provides human-led model evaluation, factuality and hallucination benchmarking, and regulatory and ethics assessments. Its use of human evaluators can help identify subtle errors that automated tests miss. Its evaluation services cover assessment before and after release.expertinsights.com+2

Best for: Human-reviewed accuracy, bias, and domain-specific evaluations at scale.

Website:Appen Model Integrity & AI Evaluation

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3. Humane Intelligence

This nonprofit conducts paid AI red-teaming and customized contextual evaluations, with a focus on accountability, fairness, and responsible AI. Its approach can involve diverse participants and subject-matter experts to surface failures that standard benchmarks overlook.humane-intelligence.org+1

Best for: Independent evaluations emphasizing bias, fairness, and real-world societal impacts.

Website:humane-intelligence.org

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4. AppSierra

AppSierra advertises AI governance and model evaluation services that include bias and fairness testing, hallucination and drift detection, production monitoring, guardrails, and model risk management.expertinsights.com+2

Best for: A service provider to help assess AI and establish ongoing operational controls.

Website:AppSierra AI Governance & Evaluation

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5. Trail of Bits

Trail of Bits provides AI/ML security assessments, red teaming, model capability benchmarking, and security threat analysis. Its strengths are technical depth and specialist security research rather than broad, turnkey fairness monitoring.trailofbits.com

Best for: High-assurance AI systems, technical risk reviews, and adversarial security assessments.

Website:Trail of Bits AI/ML services

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6. Bulsoft

Bulsoft advertises AI evaluation and assurance services covering hallucinations, bias, robustness, red teaming, retrieval-augmented generation (RAG) accuracy, and model drift. It describes a repeatable testing approach with regression suites and written evidence.www.bulsoft.com+1

Best for: Teams seeking an engineering-oriented assurance partner that can build repeatable tests into their development process.

Website:Bulsoft AI evaluation and assurance

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7. Katalyst AI Lab

Katalyst AI Lab offers structured safety assessments, adversarial testing, and model evaluation. Its stated scope includes hallucinations, sensitive-data leakage, unpredictable behavior, and bias, with support for monitoring risks as systems change.katalysttech.com

Best for: A specialist AI safety engagement that may extend into ongoing monitoring.

Website:Katalyst AI Lab services

Which providers match each risk?

These are suggested starting points based on each provider's published capabilities, not an independent ranking of service quality. Confirm which offerings are available under a recurring contract and whether the provider will test your actual deployed system.

What to require from a provider

For your use case, I'd ask each company to propose a recurring program with these five components:

• Bias and fairness: Test outcomes across relevant demographic groups, languages, and user scenarios.
• Hallucination and factuality: Measure unsupported claims, incorrect answers, fabricated citations, and whether answers are grounded in trusted sources.
• Reliability and regression: Repeat benchmark tests whenever models, prompts, retrieval sources, or application code change.
• Adversarial red teaming: Test jailbreaks, prompt injection, sensitive-data exposure, unsafe responses, and unauthorized actions.
• Risk reporting and remediation: Deliver reproducible examples, severity ratings, trend reports, and retesting after fixes.

A good engagement should measure both how often failures occur and how severe their impact would be. A single overall AI safety score isn't enough.

Let's narrow this to the best providers for you

The right shortlist depends on whether you want an outside team to do the work for you or a platform that continuously tests your AI.

1. What kind of AI are you assessing?

Customer-facing chatbot or knowledge assistant

Internal enterprise AI or employee copilot

AI agents that take actions or use tools

Predictive models or AI used for decisions

Multiple types of AI systems

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2. What kind of service do you prefer?

Managed service: experts run assessments and report findings

Platform: automated tests run continuously

Hybrid: experts plus continuous automated testing

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3. Which risks matter most?

All four: bias, hallucinations, reliability, and security

Bias and fairness

Hallucinations and factual accuracy

Security and adversarial attacks

Governance, risk, and compliance

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4. What is your organization's size?

Startup or small business

Mid-size company

Large enterprise or regulated organization

[Find my best-fit providers]

If you're testing a model against adversarial attacks, the service category to look for is adversarial robustness testing or AI red-teaming. Providers deliberately perturb inputs, try evasion attacks, and measure how often a classifier makes incorrect predictions.

There are two main options: use a self-service testing framework if you want to run experiments internally, or hire an independent evaluation provider if you want an external assessment and a report.

Providers and tools to consider

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1. Adversarial Robustness Toolbox (ART)

(Best for traditional classifiers)

An open-source toolkit originally started by IBM, now under the LF AI & Data Foundation. Supports classifiers built with scikit-learn, PyTorch, TensorFlow, XGBoost, and more. Tests evasion, poisoning, model extraction, and privacy-inference attacks.research.ibm.com+1

Best if you want reproducible, technical adversarial tests against your own model.

Official website

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2. ITRex

Offers AI model validation services that include adversarial robustness testing, noisy or corrupted input testing, boundary-condition analysis, and bias evaluation.itrexgroup.com

AI model validation services

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3. TestFyra

Provides AI/ML testing services, including robustness checks with noisy, incomplete, and adversarial inputs. A candidate if you want an external QA/testing partner.testfyra.com

AI/ML testing services

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4. StressBench AI

Specializes in stress-testing document recognition models, including OCR, using controlled input variations and condition-level diagnostics. Most relevant if your classifier handles documents or extracted text.www.stressbenchai.com

Stress-testing service

Which should you choose?

My recommendation: Start with ART if your model is a conventional classifier. It has broad framework support and lets you measure adversarial robustness directly. If you need an independent assessment, consider a paid provider as well.

One important distinction: some companies advertising AI red-teaming focus primarily on LLM jailbreaks, not conventional classification models. Verify that a provider supports your model type and threat model before engaging them.

What kind of classifier are you testing—text moderation/spam, image classification, fraud detection/tabular data, or an LLM-based safety classifier? That will help narrow this down to the most relevant providers.